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Copernicus Hyperspectral Imaging Mission for the Environment - Mission Requirements Document

Prepared by Earth and Mission Science Division Reference ESA-EOPSM-CHIM-MRD-3216 Issue/Revision 2.1 Date of Issue 23/07/2019 Status Issued Document Type Mission Requirements Document (MRD) Distribution Public Maurice






Borgeaud





2019.07.24

17:46:36 +02'00' ESA UNCLASSIFIED - For Official Use

CHANGE LOG Reason for change Issue Nr. Revision Number Date Reference Nr. , update of Mission Requirements 1. 1 18/03/2018 General update, formatting, re-pagination 1. 2 05/07/2018 Update Traceability Matrix, References, consolidation 1. 3 31/01/2019 Mission Requirements, Issue for signature, minor updates 2. 0 31/03/2019 Issue for signature, minor updates 2. 1 23/07/2019

CHANGE RECORD Issue Number 1 Revision Number 1 Reason for change Date Pages Paragraph(s) Reference number 18/03/2018 1 MR-030, 040, 060, 085, 086, 090, 100, 110, 120, 125, 58-61 130, 135 Issue Number 1 Revision Number 2 Reason for change Date Pages Paragraph(s) General update (editing, formatting, references) 17/05/2018 all pages Re-pagination 05/07/2018 pages 34 to 43 Issue Number 1 Revision Number 3 Reason for change Date Pages Paragraph(s) Update Approval 31/01/2019 1 Update Chapter 4 53-59 Update Mission Requirements 30, 50, 80, 85, 86, 110, 62-67 120, 125, 140, 150, 160, 170, 180, 190, Update References 78-94 Issue Number 2 Revision Number 0 Reason for change Date Pages Paragraph(s) Issue for signature 27/03/2019 2 Insertion of Acronym title 27/03/2019 7 Punctuation clean-up 27/03/2019 74 Issue Number 2 Revision Number 1 Reason for change Date Pages Paragraph(s) Minor text edits 23/07/2019 11, 29, 38, 52, 69, 73 Update 6.4 1st para and MR-060 23/07/2019 63, 64

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Name/Organisational Unit CHIME study team, CHIME Phase A/B1 contractor responsibles, Copernicus Task Force members

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Table of contents:

1 INTRODUCTION ...... 7 2 BACKGROUND AND JUSTIFICATION ...... 8 3 USER NEEDS AND REQUIREMENTS ...... 12 3.1 Hyperspectral Imaging And User Needs For Natural Resources Management ...... 12 3.2 Application Pillars Of Hyperspectral Imaging For Natural Resource Management ..... 13 3.2.1 Sustainable Agriculture And Food Security ...... 13 3.2.1.1 Food Nutrition And Nutrition Quality ...... 14 3.2.1.2 Sustainable Use Of Nutrients And Water ...... 17 3.2.1.3 Soil Degradation And Soil Properties ...... 22 3.2.2 Raw Materials ...... 29 3.2.2.2 Responsible Raw Materials Exploration And Mining ...... 29 3.2.2.2 Mine Environment Management ...... 30 3.2.3 Additional Applications ...... 34 3.2.3.2 Biodiversity And Ecosystem Sustainability ...... 34 3.2.3.2 Forestry ...... 40 3.2.3.2 Coastal And Inland Waters ...... 43 3.2.3.2 Environmental Degradation And Hazards ...... 45 3.2.3.2 Hydrology / Cryosphere ...... 49 3.3 Summary Of User Requirements And High Level Observational Requirements ...... 51 4 TRACEABILITY OF HYPERSPECTRAL IMAGING MISSION POLICY NEEDS/APPLICATIONS, USERS & STAKEHOLDERS AND OBSERVATIONAL REQUIREMENTS ...... 54 5 MISSION OBJECTIVES ...... 61 6 MISSION REQUIREMENTS ...... 62 6.1 Spatial coverage and geometry ...... 62 6.2 Observation Time ...... 63 6.3 Timeliness ...... 63 6.4 Revisit Time ...... 63 6.5 Spatial Requirements ...... 64 6.6 Spectral Requirements ...... 65 6.7 Radiometric Requirements ...... 65 7 CALIBRATION REQUIREMENTS ...... 69 7.1 Radiometric calibration ...... 69 7.2 Spectral Calibration ...... 69 7.3 Dark Current Measurements ...... 70 7.4 Geometric Calibration ...... 70 7.5 Instrument Monitoring and Data Quality Control ...... 70 7.6 Vicarious Calibration ...... 70 8 CAMPAIGNS ...... 71 9 PRELIMINARY SYSTEM CONCEPT(S) ...... 72 10 DATA PRODUCTS, USAGE AND ACCESS ...... 73 10.1 Core Data Products ...... 73 10.2 Higher Level Data Products ...... 73 10.3 Contribution to EU Policies and Copernicus Services ...... 75 11 SYNERGIES AND INTERNATIONAL CONTEXT ...... 77

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12 REFERENCES ...... 79

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1 INTRODUCTION

Evolution in the Copernicus Space Component (CSC) is foreseen in the mid-2020s to meet priority user needs not addressed by the existing infrastructure, and/or to reinforce services by monitoring capability in the thematic domains of CO2, polar, and agriculture/forestry. This evolution will be synergetic with the enhanced continuity of services for the next generation of CSC.

Growing expectations about the use of Earth observation data to support policy making and monitoring puts increasing pressure on technology to deliver proven and reliable information. Hyperspectral imaging (also known as imaging spectroscopy) today enables the observation and monitoring of surface measurements (geo-biophysical and geo- biochemical variables) due to the diagnostic capability of spectroscopy provided through contiguous, gapless spectral sampling from the visible to the shortwave infrared portion of the electromagnetic spectrum.

Hyperspectral imaging is a powerful remote sensing technology based on high spectral resolution measurements of light interacting with matter, thus allowing the characterisation and quantification of Earth surface materials. Quantitative variables derived from the observed spectra, e.g. directly through distinct absorption features are diagnostic for a range of new and improved Copernicus services with a focus on the precise management of natural resources. These services support the monitoring, implementation and improvement of a range of related policies and decisions.

Thanks to well-established spectroscopic techniques, optical hyperspectral remote sensing has the potential to deliver significant enhancement in quantitative value-added products. This will support the generation of a wide variety of new products and services in the domain of agriculture, food security, raw materials, soils, biodiversity, environmental degradation and hazards, inland and coastal waters, and forestry. These are relevant to various EU policies, that are currently not being met or can be really improved, but also to the private downstream sector.

The Main Mission Objective of the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is: “To provide routine hyperspectral observations through the Copernicus Programme in support of EU- and related policies for the management of natural resources, assets and benefits. This unique visible-to-shortwave infra-red spectroscopy based observational capability will in particular support new and enhanced services for food security, agriculture and raw materials. This includes sustainable agricultural and biodiversity management, soil properties characterisation, sustainable mining practices and environment preservation.”

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2 BACKGROUND AND JUSTIFICATION

Hyperspectral Imaging has already supported, or been used for a large range of applications (Hochberg, Roberts et al. 2015). Corresponding variables have been derived from the observed spectra, e.g. directly through distinct absorption features or indirectly, through inversion of physically based models, assimilation, spectral un-mixing, and/or (de-) correlation techniques. Based on successful airborne deployments over the last three decades and the past satellite missions along with preparatory activities of some national demonstrative satellite missions, hyperspectral imaging from satellite is now ready for operational use. The development of a spaceborne hyperspectral sensor is a logical step to complement and expand the Copernicus Space Component to serve emerging applications and services and to improve on the existing ones.

In general, the application potential of a hyperspectral mission is a direct result of adding an increased number of narrow spectral (contiguous) bands with a high Signal-to-Noise- Ratio (SNR) to the conventional passive optical multi-spectral remote sensing missions, such as Sentinel-2 and Landsat, thereby allowing direct and indirect identification of target compositions and quantities (see Figure 2-1)

Figure 2-1 Reflectance spectra for different Earth surface materials at high spectral resolution and resampled to the spectral response of the multispectral instrument onboard Sentinel-2.

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Considering the Sentinel missions currently operational and those being developed, a hyperspectral mission will add to- and complement the conventional passive optical multi- spectral remote sensing missions, such as Sentinel-2. It will support EU- and related policies for the management of natural resources, assets and benefits and, more specifically it will provide unique and major contributions in closing existing gaps in fulfilling user requirements in the domains of raw materials- and sustainable agricultural management with a focus on soil properties, sustainable raw materials development and agricultural services, including food security and biodiversity.

Potential supported policies comprise the UN Sustainable Development Goals (SDG), in particular SDG 2: Zero Hunger, SDG 12: Responsible Consumption and Production, and SDG 15: Life on Land, the UN SEEA (System of Environmental-Economic Accounting) the EU Common Agriculture Policy CAP, the EU Raw Materials Initiative and the European Innovation Partnership on Raw Materials, Natura 2000, the UN Convention for Combating Desertification and Land Degradation, the Soil Thematic Strategy and the Soil Framework Directive, the EU Water Framework Directive, the UN Convention on Biodiversity (Aichi targets), the EU Biodiversity Strategy and EU Mine Waste Directive. The explicit need for hyperspectral observations to fulfill the information needs with respect to the above policies has been addressed in user requirements workshops organised by the EC from the agriculture and forestry as well as from the raw materials communities.

In the following, examples are provided for a first, preliminary and qualitative understanding of the information that can be generated and of the associated benefits.

In the domain of food security and agriculture, hyperspectral remote sensing has the potential to allow a more accurate determination of the main crop characteristics and their temporal change along with the derivation of soil fertility (Asner and Heidebrecht 2002). This is clearly a tool to improve farm management and field productivity (smart farming). Specific results show that with hyperspectral remote sensing it is possible to conduct a detailed phenology assessment in the developing state of crops used for a better determination of nutrient and pesticide applications, and therefore providing means to improving its application through enhanced efficiency. Further, chlorophyll content, a biochemical variable, which can be quantitatively derived from hyperspectral observations, is related to gross primary production in canopies. This main plant pigment of photosynthesis has in the past been investigated in several studies aiming to increase the understanding of assimilation rates and primary production. Hyperspectral sensors provide the unique possibility to assess chlorophyll content and photosynthesis rates at the leaf and canopy level without the restrictions of laboratory methods.

As a synthesis of these assessments, innovative farm management options (smart farming) based on hyperspectral remote sensing have been proposed and initially tested, including the prediction of temporal and spatial patterns of crop productivity and yield potential. Furthermore, the quantification of crop residues after the harvesting period in the soil is an effective measure for soil functionality that relies on organic matter and important attributes as input factors. Apart from that, the estimation of photosynthesis rate and metabolism gives detailed information about primary production.

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Overall, in agriculture the main advantages of a hyperspectral imager over a conventional multi-spectral imaging radiometer lie in the diagnostic capability to distinguish photosynthetically active from non-photosynthetically active vegetation, the improvement of crop type classification, the simultaneous provision of nitrogen uptake and other nutrient content, as well as information on plant disease, yield quality information and quantitative crop damage (Apan, Datt et al. 2005).

Agriculture sustainability requires an assessment of the long-term viability of farming systems, ensuring that the landscape is not degrading to a point that food production is reduced. There are many components of sustainability that the proposed hyperspectral satellite can monitor. For example, these include (as mentioned above) the nitrogen (and chlorophyll) balance to ensure that the soil nutrients are not being reduced by erosion, or that excessive fertilizer applications are not polluting drinking water. In addition, maintaining the biodiversity of the agricultural landscape is a recognised proxy that the food supply chain is being managed sustainably by the farmer as well as the industries that bring the food from the field to the table.

Soils are the foundation of agriculture where ninety-five per cent of food is produced from our soils. Accordingly, preserving the soils' ‘health’ is of critical importance. Soil plays a key role in the supply of clean water and resilience to floods and droughts. It is thus an important and finite resource, directly related to the ability to support plant life that has significant implications on food security, agricultural management and climate change. Therefore, monitoring of soil conditions in a quantitative way from local through to global scales is important to support farmers, land users, policy and decision makers.

An important geophysical property of soils that hyperspectral imaging can provide is soil mineralogical composition. Minerals are fundamental components of all soils and are an indicator of many important soil parameters, such as pH, Redox, water/metal activities and permeability important for understanding soil chemical and physical condition. The direct derivation of mineralogical composition and abundances using hyperspectral technology has long been proven.

Hyperspectral imaging has shown to be a powerful technique for the direct and indirect determination and modelling of a range of soil properties, including soil organic carbon (SOC), moisture content, textural and structural information, pH, as well as other properties assigned to a soil quality parameter (Paz-Kagan, Zaady et al. 2015), that are directly linked to crop production and fertility. In the context of food security, besides crop properties, many important soil attributes including cation exchange capacity, soil erosion, soil salinity, degradation processes, and especially organic matter which is strongly linked to the CO2 cycle, can be generated from airborne imaging spectroscopy and thus from any future space borne mission (Bartholomeus, Kooistra et al. 2011, Schmid, Rodríguez- Rastrero et al. 2016).

Raw materials and more specifically, minerals are valuable resources used throughout modern society and there is little doubt that the demand for these commodities will increase. However, the extraction and processing of minerals are associated with a number of sustainable development challenges, including various economic, environmental and

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social issues (Azapagic 2004). Therefore, evidence of responsible exploration and mining is crucial to gaining license to operate for the continuity of current mineral resources operations and expected expansion. Over the last three decades, the use of hyperspectral imaging in support of sustainable raw material resources development has been demonstrated as one of the most compelling cases for a hyperspectral imaging mission (Goetz 2009), since this technology is able to provide: 1) crucial mineralogical information unattainable from other exploration and mining tools contributing towards improved exploration targeting and resource characterisation, thereby reducing environmental footprints as well as contributing towards more efficient and safer mining practices; and, 2) quantitative, direct environmental information required for evidence-based decision making and substantiating compliance with regulatory requirements. In addition, the non- intrusive acquisition is highly valued as many mining operations are often located in environmentally and culturally sensitive rugged terrains. Figure 2-1 provides the spectral fingerprints of representative terrestrial materials demonstrating the detailed and diagnostic information obtainable from hyperspectral imaging relative to the discrete information from multispectral imaging satellite sensors in orbit.

A hyperspectral imaging mission has the potential to contribute across the complete value chain of raw materials use from the exploration stages for the provision of baseline environmental information as well as exploration targeting; throughout the mine life for environmental monitoring (of rehabilitated lands, mine waste facilities and other impacts such as dust) as well as improving resource characterisation; to the end of the mine life for the assessment of progress of rehabilitation towards closure plans. The evidence of the need for such technology was recognised at the Copernicus Users Workshop for Raw Materials, and by a submission to Australian Space Review by one of the world’s largest mining house, Rio Tinto, with the following statement “Access to higher spectral and spatial resolution hyperspectral data will improve our ability to explore and monitor the environment surrounding our mining operations and waste facilities”1.

While this mission will be designed to meet the main objectives identified above, availability of hyperspectral observations is expected to also support the additional application areas on i) Biodiversity on land; ii) Forestry; iii) Inland and coastal waters and iv) Environmental degradation and hazards; v) Hydrology/cryosphere.

1 https://consult.industry.gov.au/space-activities/review-of-australian-space-industry- capability/consultation/view_respondent?_b_index=120&uuId=671347567

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3 USER NEEDS AND REQUIREMENTS

3.1 Hyperspectral Imaging And User Needs For Natural Resources Management

The analysis of user requirements, as discussed in the next sections, underlies the mission requirements and associated observational requirements, as presented in Chapter 6.

In the overview below (Table 3-1) the prime focus of the mission in support of EU- and related policies for the management of natural resources which are Sustainable Agriculture and Food Security, and Raw Materials is shown. Organic and inorganic materials (vegetation and soils) are intimately linked in the areas of sustainable agriculture and raw materials management. Sample application pillars with high societal relevance cover food nutrition and nutrition quality, sustainability in agricultural and soils practice, more efficient use of nitrogen and water, improved governance for applications like mine waste management and responsible raw material use. These sample applications were selected because of their relevance and the unique capabilities of imaging spectrometry.

Natural Resources Management Sustainable Agriculture and Food Security Raw Materials Food Sustainable use Soil Responsible raw Mine Environment nutrition of nutrients and degradation materials Management and water and soil exploration and nutrition properties mining quality Table 3-1 Application pillars of the hyperspectral imaging mission for natural resources management and their unique information provision using hyperspectral sensors. Securing food supply is not limited to the amount of food available, but also depends on food nutrient content and quality, that might be reduced through climate change as recent studies revealed. Only hyperspectral data will give us a quantitative measure of protein content in space and time through analysing chlorophyll, nitrogen and finally protein contents.

Increase of food production further requires intensification since expansion is not an option with regards to environment and biodiversity. For a more efficient use of nitrogen and water again specific absorption features (chlorophyll, carotenoids, plant water) can be studied and used to optimise fertilization and irrigation. This is a prerequisite to limit the environmental impact of intensification.

Soil degradation is another important threat to food security (UNCCD 2017). Sustainability in agricultural and soil practice needs a monitoring system that objectively measures soil quality (like organic content) and monitors the management of crop residues. Only the analyses of the dedicated absorption features of organic matter, cellulose and lignin (Figure 2-1) possible with hyperspectral imagers allows this. The same is true for temporal changes in soil mineralogy due to water or wind erosion.

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Besides these samples, a thorough analysis of user needs and requirements follows in the next chapters covering a wider range of applications.

3.2 Application Pillars Of Hyperspectral Imaging For Natural Resource Management

3.2.1 Sustainable Agriculture And Food Security Because of global population increase as well as changing dietary habits (Tilman, Balzer et al. 2011), global demand for biomass-based products will have to sharply increase over the next decades to meet the demand of the world population (FAO 2017). Meeting this demand provides a significant challenge. While expansion of agricultural land into non- agricultural ecosystems is discussed critically, agricultural intensification by closing existing yield gaps is considered the most promising choice for meeting the increasing demand (Mueller, Gerber et al. 2012). At the same time, negative impacts of agricultural intensification and expansion (e.g., biodiversity loss by habitat fragmentation, depletion of fresh water resources by irrigation, disruption of nitrogen and phosphorus cycles impacting groundwater quality) have to be mitigated (Atzberger 2013). Accordingly, we will need tools to manage this transition, monitor the progress and eventually ensure sustainable agricultural production that provides food security and nutrition for everyone.

Agriculture is one of societies’ most important sectors as it secures the World’s food supply with a resulting strong environmental impact e.g. through a high energy consumption and CO2 and methane sources, being the largest consumer of fresh water and polluter of natural resources with its nitrogen and agro-chemical inputs. The way agriculture is conducted is changing steadily with the integration of modern technologies into the production process – from GNSS and auto-steering to site-specific farming and use of IoT. The production of food will have to meet the demand of a growing world population under challenging environmental and economic conditions. Therefore, new management measures that are introduced have a focus on getting more precise in their applications and producing sustainable high yields while lowering the risks of crop failure. Remotely-sensed data can provide a valuable contribution through the monitoring of current field status. There are several commercial service offers (e.g. www.talkingfields.de, FarmStar) already on the market that rely mostly on multi-spectral data. Hyperspectral remote sensing has the potential to allow the derivation of additional crop and soil variables as well as allowing a more precise derivation of the main crop characteristics, thus giving the potential to improve farm management and field productivity. Research and demonstrations have widely shown the essential role of hyperspectral observations to meet users’ requirements and to bring agricultural practice to a next level: it would greatly improve existing agriculture-related applications and allow development of new ones mainly addressing food security. The development of a mission such as CHIME, which will ensure the availability of a continuous flow of quality data, would allow to quickly move to operational applications. Figure 3-1 highlights the additional spectral features available in hyperspectral data for crop monitoring comparing “CHIME-like” spectra to multispectral observations.

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Figure 3-1 Imaging spectrometer data set from the San Joaquin Valley, California highlighting the addition spectral content available for a larger set of agriculture and food security products.

In Europe, for the Common Agricultural Policy (CAP), traditional multispectral satellite data have been used successfully since 1992, ensuring crop type mapping (e.g. classification of winter cereals, corn, soybean, sugar beet, stable fodder, etc.). However, with the new “Greening CAP” and the return to ''coupled subsidy” (payment related to real crop cultivation on parcel) the need to identify individual crops is growing. An example is the "crop diversification" application, imposed by the new “Greening Policy”, where it is necessary to separate barley and wheat (impossible in high quality through simple multispectral data) on the same farm declaration. Hyperspectral data with contiguous "narrow bands" up to short infrared (2.5 µm) can certainly support these operational requirements and be included in the operational chains of the Agricultural Agencies in Europe.

3.2.1.1 Food Nutrition And Nutrition Quality

In order to better manage food security, one has to monitor both the quantity of the crop production together with the nutrition content and quality of its harvested parts. An example for this is that the protein content of wheat determines its use for human

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consumption or animal feeding. In this context, high spatial resolution yield maps for wide areas will allow simultaneous use for governmental and private purposes.

On the one hand, large scale maps of yield formation could be regularly distributed to authorities and organisations for governmental purposes e.g. to provide early warning for food scarcities and allow better food distribution. In the European context, yield monitoring could provide the geographical information for offset payments for farmers in disadvantaged regions. On the other hand individual agricultural users could use site- specific maps of yield to improve their farm management. Yield information could also be used for crop insurances in order to hedge weather dependent risks of farmers. For yield monitoring, current multispectral sensors have the spatial, spectral and temporal resolution to deliver yield quantity, e.g. (Machwitz, Giustarini et al. 2014, Hank, Bach et al. 2015), though hyperspectral sensors are expected to improve accuracy. Biomass and yield predictions can be enhanced by hyperspectral data (Bach, Begiebing et al. 2007); (Begiebing, Schneider et al. 2007); (Migdall, Bach et al. 2009). Successful demonstrations for this are given both for airborne and spaceborne instruments (Figure 3-2). These improvements also include the assessment of crop productivity (Mariotto, Thenkabail et al. 2013) and soil fertility.

Figure 3-2 Yield estimates for wheat grains based on hyperspectral data (airborne AVIS and spaceborne CHRIS) assimilated in a crop growth model (PROMET). Ground truth yield map is retrieved from interpolated combine measurements. (Begiebing, Bach et al. 2005)

Besides yield quantity, the capability of hyperspectral instruments to deduct yield quality (e.g. protein content), which is linked to nutritious healthy food is of importance. Current sensors lack the spectral resolution required for this. Hyperspectral data allow in principle these diagnostics, however recent studies are limited to ground based measurements and case studies. For example (Apan, Kelly et al. 2004) (Figure 3-3) succeeded to use field spectrometer measurements during flowering and PLS analyses for the early estimation of wheat protein content at harvest. With an RMSE of 0.66% for grain protein contents varying between 9.4% and 16% this allowed for differentiating high quality wheat for flour/bread production or animal feeding (protein content above or below 13%). Grain protein thus not only determines the use of the produced goods, but also has strong impact

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on prices and farmers’ income. Additionally, (Beeri, Phillips et al. 2007) estimated forage quality from hyperspectral data.

Figure 3-3 Wheat canopy reflectance (mean) of samples with high (>13.5%; bold line) and low (thin line) grain protein (Apan, Kelly et al. 2004).

Studies of (Fava, Parolo et al. 2010) indicate that plant species composition, biodiversity and forage value can be obtained by spectrometric data and hence incorporated in food security, nutrition and livestock production analysis.

Within the research of nutrition quality, the assessment of nutrients (nitrogen, phosphorus, sulphur and potassium) are widely studied topics (Ramoelo, Skidmore et al. 2011) (Psomas, Kneubühler et al. 2011, Clevers and Kooistra 2012, Miphokasap, Honda et al. 2012, Pullanagari, Yule et al. 2012, Mahajan, Sahoo et al. 2014, Marshall and Thenkabail 2015, Pellissier, Ollinger et al. 2015). However, these are often conducted on leaf level or based on ground spectrometer measurements. An impressive example for this is given in Figure 3-4. The distribution maps of macronutrients of single leaves are based on a VIS-NIR spectrometer using Partial Least Squares Regression (PLSR) technique and support vector machine.

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Figure 3-4 Macro-nutrients content (Nitrogen, Phosphorus, and Potassium) in two leaf samples of rapeseed (top: highly fertilized, bottom: not fertilized) (Zhang et al., 2013)

On canopy level, the combination of remotely sensed hyperspectral data and radiative transfer models allows the prediction of complex biophysical parameters. The most prominent are: • Leaf Area Index (LAI), • Fraction of Photosynthetically Absorbed Radiation (fAPAR), • Leaf pigment content of chlorophyll and carotenoids and other pigments, • Specific Leaf Area (SLA), • fraction of Non-Photosynthetic Vegetation (NPV), • Leaf and canopy water content.

Some of them can be retrieved from multispectral data (like LAI and fAPAR) but will be measured more reliably and with higher accuracy using hyperspectral data. Others like NPV and leaf water content require hyperspectral measurements for their retrieval.

3.2.1.2 Sustainable Use Of Nutrients And Water

Food security can further only be achieved and assured for the next decades if also the water consumption for crop production is managed in a more efficient way. Here, irrigation plays a central role. How hyperspectral data can be used for irrigation monitoring could be demonstrated e.g. in Tunisia (Bach, Begiebing et al. 2007). Apart from

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that, water requirement of crops in India (Bhojaraja, Hegde et al. 2015) and leaf water content in different maize genotypes were assessed successfully. Services for irrigation advice in terms of time, amount and distribution of water to the crops (e.g. variable rate irrigation) sometimes rely on the FAO56 method with however limited use of Earth Observation (EO) data since empirical crop coefficients are derived from NDVI with early saturation. They can also be based on the assimilation of EO variables in more complex hydrological models. Farmers benefit from this advice through reliable yields combined with savings in production costs. Mapping of wider areas allows to track regional water consumption for agriculture. This information could be aggregated on watershed scale to deliver valuable information about water consumption in individual catchments to track flux rates and to improve watershed management.

For sustainable use of nutrients, monitoring of the leaf and canopy chlorophyll content plays a central role, since it describes how much nitrogen (a central element of chlorophyll) has been taken up by the crops.

Chlorophyll Content in Canopies (CCC) is an essential ecophysiological variable for photosynthetic functioning (Migdall, Klug et al. 2012, Gitelson, Peng et al. 2014). CCC was investigated in various studies aiming to increase the understanding of assimilation rates and primary production. Hyperspectral sensors provide the unique possibility to assess chlorophyll content and photosynthesis rates at the leaf and canopy level without the restrictions of laboratory methods (Serbin, Singh et al. 2015). Recently, an index based on the ratio of the reflectances at 815 and 704 nm was developed using data from six different crops that allows the prediction of CCC from hyperspectral images (Inoue, Guérif et al. 2016).

Since chlorophyll and nitrogen content of leaves are often closely linked, this allows the monitoring of nitrogen uptake during crop development, as shown in Figure 3-5. This information can be used for a more efficient application of fertilizers by supplying the plants with enough nitrogen for high quality yields and at the same time minimise nitrate leaching to the groundwater. An operational hyperspectral satellite sensor would be the technical basis for a fast market acceptance of this smart farming service (not only) in Europe.

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Figure 3-5 Monitoring of canopy nitrogen content for a wheat field during the growing season derived from hyperspectral AVIS data, compared to measured yield (Oppelt 2002).

The assessment of crop phenology, which was before limited to the description of the start, maximum and end of growing season, will be derived from hyperspectral data in growth stages with high relevance for farming praxis (Gnyp, Yu et al. 2013). Phenological development is an indicator for the overall plant development and gives an indication of current risk for certain plant diseases. Phenology estimation can be used for better determination of nutrient and pesticide applications as the efficiency of these measures depends on the state of plant development. Flowering dates and maturity level can be named as prominent phenological indicators. The overall goal is the production of high resolution maps of crop type specific phenological development for wider areas. Information about phenology can then be distributed to agricultural users for management decisions as well as to meteorological services.

Phenological models for the stage of rice (Gnyp, Miao et al. 2014) and spring barley (Lausch, Salbach et al. 2015) were tested on the field scale. (Migdall, Ohl et al. 2010) used radiative transfer modelling to derive individual phenological stages (esp. blooming) for

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rapeseed. Sample mapping results are illustrated in Figure 3-6. However, the authors detected several gaps in the understanding of plant physiology and spectral properties and called for further research in this topic.

Blooming Intensity

0.00 0.35 %

Figure 3-6 Hyperspectral monitoring of phenological stages using CHRIS data. Left: Phenological macro-stages (BBCH) of barley varying between head emergence (BBCH 5) and grain development (BBCH 7); after (Lausch, Salbach et al. 2015). Right: blooming intensity for rape seed; (Migdall, Ohl et al. 2010)

Furthermore, the quantification of crop residues (Figure 3-7) after the harvesting period in the soil (Bannari, Staenz et al. 2015) is an effective measure for soil functionality that relies on several important soil attributes including organic matter, carbonates and clay content as input factors. Also, mapping of severe soil erosional stages from hyperspectral remote sensing data over a vegetation-free period (Schmid, Rodríguez-Rastrero et al. 2016) is important for agricultural management as it allows to detect areas with reduced crop production.

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Figure 3-7 Crop residue cover in percentages derived from Hyperion data and validation with ground reference data; (Bannari, Staenz et al. 2015)

As a synthesis of these assessments, innovative farm management options based on hyperspectral remote sensing have been proposed ((Migdall, Klug et al. 2012, Mariotto, Thenkabail et al. 2013, Bannari, Staenz et al. 2015, Bhojaraja, Hegde et al. 2015) including the prediction of temporal and spatial patterns of crop productivity and yield potential.

As precision farming methods are under growing attention, most of the applications presented are directly related to this topic. As mentioned before, some of the studies showed the potential for up-scaling on local and regional levels (Bhojaraja, Hegde et al. 2015) while other authors demonstrated the benefit of hyperspectral over multispectral data (Mariotto, Thenkabail et al. 2013). Most of the hyperspectral imaging systems are handheld or airborne, but also NASA EO-1 Hyperion and CHRIS data were used successfully (i.e. (Migdall, Ohl et al. 2010, Marshall and Thenkabail 2015). While in-field studies and airborne studies are costly, restricted to a small area and temporal coverage is sparse, satellite derived products and applications of hyperspectral sensors provide the possibility to investigate spatial and temporal patterns on a regional scale.

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The studies showed the potential hyperspectral remote sensing has to meet users’ requirements and the manner in which it could help to bring agricultural practice to a next level.

3.2.1.3 Soil Degradation And Soil Properties

Soil carries out a number of key environmental functions that are essential for human subsistence such as food, fibre and timber production, water storage and redistribution, pollutant filtering or carbon storage. Soils have been recognised as a non-renewable resource that can be affected by soil erosion and degradation processes. Following the FAO definition, “soil degradation is defined as a change in the soil health status resulting in a diminished capacity of the ecosystem to provide goods and services for its beneficiaries”, and relates to a loss of soil productivity either by a chemical or a physical process. Therefore, the process of soil degradation is directly linked to a change in soil and soil- related properties, and thus can be assessed and monitored indirectly.

In soil science, the usage of reflectance spectroscopy is well established, as many important properties of soils can be reliably derived when combining spectroscopy with chemometric techniques (Malley, Martin et al. 2004, Rossel, Walvoort et al. 2006). This includes the quantification of a wide range of physical soil properties (e.g., aggregation and particle size including clay and sand fraction), chemical properties (e.g., organic carbon, cation exchange capacity, heavy metal content, salinity status, hydrophobicity and hydrocarbon soil contamination content) and biological properties (e.g., microbial enzyme activities, cyanobacteria activity and microbial decomposition processes of litter), as shown within the meta-analysis of over 240 studies by (Soriano-Disla, Janik et al. 2014). Consequently spectroscopy is now considered as an alternative to wet chemistry method in some application fields (Nocita, Stevens et al. 2015).

Following the manifold applications of spectroscopy in soil sciences, also airborne imaging spectroscopy at the remote sensing scale has been successfully used even though only the top soil properties and not the full soil profile can be addressed. Nonetheless, a combination of bore hall point and airborne image spectroscopy can lead to a 3-D visualisation of the soil body (Ben-Dor, Heller et al. 2008, Ogen, Goldshleger et al. 2017). In the following, topsoil parameters are listed which can be retrieved directly (i.e., not by using proxy) using imaging spectroscopy, and are key relevant soil properties and soil degradation parameters:

• Topsoil mineralogy, which includes the qualitative identification and the quantitative estimation of: • Iron and iron oxides e.g. (Kemper and Sommer 2002, Ben-Dor, Levin et al. 2006) • Carbonate type and content e.g. (Ben-Dor and Kruse 1995), (Lagacherie, Baret et al. 2008) • Clay minerals e.g. (Chabrillat, Goetz et al. 2002, Gerighausen, Menz et al. 2012) • Soil salinity and salt minerals e.g. (Ben-Dor, Patkin et al. 2002, Dehaan and Taylor 2002, Dehaan and Taylor 2003, Milewski, Chabrillat et al. 2017)

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• Soil formation (Ben-Dor, Patkin et al. 2002)

• Topsoil moisture e.g. (Bach and Mauser 1994, Haubrock, Chabrillat et al. 2008, Fabre, Briottet et al. 2015), • Topsoil organic carbon e.g. (Ben-Dor, Patkin et al. 2002, Gomez, Rossel et al. 2008, Stevens, Udelhoven et al. 2010) • Topsoil temporal spectral changes (Adar, Shkolnisky et al. 2014) • Topsoil erosion and degradation parameters • Alteration by fire (Lugassi, Ben-Dor et al. 2014) • Biological soil crusts e.g. (Karnieli, Kidron et al. 1999) • Physical soil crust e.g. (Goldshleger, Ben-Dor et al. 2001) • Status of soil degradation and soil contamination e.g. (Demattê, Huete et al. 2000, Hill and Schütt 2000, Asner and Heidebrecht 2002, Kemper and Sommer 2002, Schmid, Rodríguez-Rastrero et al. 2016) • Soil runoff and water infiltration rate to the soil profile (Ben-Dor, Goldshleger et al. 2004, Goldshleger, Ben-Dor et al. 2010) • Soil quality (Paz-Kagan, Shachak et al. 2014)

A comprehensive review of the potential of hyperspectral technology for soil applications is provided by (Ben-Dor 2002) and (Ben-Dor, Chabrillat et al. 2009)

Several success stories have proven that upscaling the spectroscopy from lab-field domains to airborne domains is possible e.g. (Ben-Dor, Patkin et al. 2002, Ben-Dor, Levin et al. 2006, Paz-Kagan, Zaady et al. 2015), see Figure 3-8. Accordingly, several recent studies looked at the potential of upcoming hyperspectral satellite missions for soil mapping, based on satellite simulated data (Chabrillat, Milewski et al. 2014, Castaldi, Palombo et al. 2015, Gomez, Oltra-Carrió et al. 2015, Steinberg, Chabrillat et al. 2016). Overall, it was showed that simulated satellite imagery (EnMAP) at 30 m using semi-operational methods are able to predict soil properties such as soil organic carbon, clay, and iron oxide content, with slightly reduced accuracy compared to airborne hyperspectral imagery, but still with unprecedented accuracy in comparison with current satellite multispectral products.

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Figure 3-8 A thematic (spectral imaging based map) showing four soil attributes: Soil organic matter, Soil salinity (measured as Electrical Conductivity EC), specific surface area and soil hygroscopic moisture in agriculture field in Israel. The dark areas are vegetation masks after (Ben-Dor, Patkin et al. 2002))

The spatial distribution of the soil properties was in general coherent between the simulated spaceborne and the airborne mapping (see Figure 3-9). Also, (Castaldi, Palombo et al. 2016) clearly demonstrated the improvement of accuracy for the estimation of soil variables over bare soils using forthcoming hyperspectral imagers, as compared to current generation multispectral sensors such as ALI, Landsat8, Sentinel-2.

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Figure 3-9 Key soil products (clay and soil organic carbon maps) from airborne and simulated spaceborne hyperspectral imagery. Left: Airborne imagery (top) HyMAP (4.5 m); and (bottom) AHS (2.6 m). Right: Spaceborne simulated EnMAP soil products based on EnMAP end-to-end simulated images (30 m) adapted from (Steinberg, Chabrillat et al. 2016).

In addition to the derivation of direct soil parameters, hyperspectral sensing technologies can provide the necessary information for carrying out the assessment and monitoring of soil degradation processes such as soil salinity (see Figure 3-10) and soil erosion (Ben-Dor, Goldshleger et al. 2004, Shoshany, Goldshleger et al. 2013).

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Figure 3-10 The Electrical Conductivity (EC) of a saline field from Israel as generated from AISA Eagle Hawke. The EC measurement in 30cm depth was modelled to the reflectance data showing promising capability to assess the root zone.

Soil erosion may be described by a combination of several characteristics of soil surface conditions and properties. These include chemical (pH, SOC, free iron oxides, CaCO3) and physical (structure, texture, coarse fragments) soil properties, as well as ground cover (with fine textured minerals to coarse fragments, and with organic elements, such as plant debris and vegetation), most of which can be better estimated using spectral indicators (Chabrillat 2006) based on hyperspectral imagery as demonstrated in many studies. For example, (Schmid, Rodríguez-Rastrero et al. 2016) were able to use hyperspectral imagery to identify, define and map soil properties that could be used as indicators to assess soil erosion and accumulation stages in a European Mediterranean rainfed cultivated region (see Figure 3-11). These properties were characterised by different soil horizons that emerge at the surface as a consequence of the intensity of the erosion processes, or the result of accumulation conditions. In another study (Ben-Dor, Goldshleger et al. 2004) were able to model the exact rain infiltration rate on a pixel by pixel domain to the soil's profile, which mirrored the runoff rate and its erosion potential (see Figure 3-12).

Furthermore, an essential parameter for soil erosion estimation and erosion modelling is the amount of vegetation cover including dry non-photosynthetic vegetation cover, which is the main vegetation cover in shrublands and drylands all over the word.

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402000

Stages am1

4440000 am 2 es1 es2a es2b es2c es3a es3b es3c npv 4432000

N W E

0 S 2 km

Figure 3-11 Distribution of soil erosion stages and accumulation zones (am= accumulation, es= erosion stage, from low to high) based on hyperspectral imagery (Schmid, Rodríguez-Rastrero et al. 2016).

Hyperspectral imagery thanks to narrow bands spectroscopy in the SWIR spectral range is especially adequate to estimate dry vegetation cover (Chabrillat 2006) and in general for the improved estimation of bare soil percentage versus cover by green and dry vegetation including litter e.g., see (Asner and Lobell 2000, Asner and Heidebrecht 2002, Malec, Rogge et al. 2015). Hyperspectral data allowed to map dry biomass in several context studies using airborne and simulated spaceborne hyperspectral imagery over dry vegetation types.

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Hyperspectral imaging technology is currently available from both handheld and airborne instruments, and it is envisioned that global satellites hyperspectral missions at high spatial resolution (≤30 m) will be able to support large-scale soil mapping and soil monitoring combined with digital soil mapping approaches and soil datasets over the world. An impressive effort has been made by several researchers to generate and keep updated the Global Soil Spectral Library that will support the soil applications from CHIME (Rossel, Behrens et al. 2016).

Figure 3-12 The infiltration image (a negative value of the runoff) of a Loess soil as generated on the basis of soil reflectance information and rain simulator measurements using AISA-Eagle sensor (after Ben-Dor et al., 2004)

For the extraction of soil compositional mapping, there are already semi-operational models available for the delivery of soil organic carbon and soil mineralogical composition products based on hyperspectral imagery, such as the HYSOMA/EnSOMAP package from the EnMAP mission e.g. (Chabrillat, Naumann et al. 2016). A recent (big) data-mining engine PARACUDA –II (Carmon and Ben-Dor 2017) demonstrated a promising capability to derive quantitative models for several soil attributes (Kopačková and Ben-Dor 2016).

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3.2.2 Raw Materials

3.2.2.2 Responsible Raw Materials Exploration And Mining

Mineral deposits are often located in areas of high environmental and cultural values at remote locations that are difficult to access. Minimising the impacts on these areas during the exploration and mining processes are key to achieving SDGs such as UN SDG 15 and technologies that can explore the ground non-intrusively are critical for this.

Hyperspectral imaging between the visible to shortwave infrared region has long been demonstrated to be an effective tool for mapping minerals (Chen, Warner et al. 2007, Kodikara, Woldai et al. 2012, Van der Meer, Van der Werff et al. 2012, Swayze, Clark et al. 2014, Boesche, Mielke et al. 2016, Calvin and Pace 2016, Cudahy 2016, Mielke, Rogass et al. 2016). Published research (Clark 1999) has shown that these wavelength regions are diagnostic of key minerals and mineral groups important for mineral exploration specifically, the Visible and Near-InfraRed (VNIR, 400 to 1000 nm) are important for transition element-bearing oxides/hydroxides (e.g., hematite and goethite) and minerals with rare earth elements (e.g., apatite and perovskite); and, the ShortWave InfraRed (SWIR, 1000 to 2500 nm) are useful for hydroxyl-bearing dioctahedral silicates (e.g., kaolinite, montmorillonite, muscovite, pyrophyllite) and trioctahedral silicates (e.g., talc, chlorite, actinolite–tremolite, nontronite), carbonates (e.g., calcite, dolomite, magnesite) and sulfates (e.g., alunite, gypsum). The spectral footprint of these materials can be partially seen and well demonstrated in Figure 2-1.

With a long history of development, hyperspectral imaging for mineral resources applications has matured and would be one of the applications with the highest Scientific Readiness Level. It is now at a stage where regional scale maps such as Figure 3-13 are being produced for baseline geological mapping and mineral exploration. Such data help to better define prospective ore bodies and focus exploration thereby reducing the environmental footprints. Additionally, acquired with appropriate calibration data, the same data are also useful for baseline environmental monitoring.

Due to the historical establishment and development of hyperspectral technology for mineral mapping, semi-operational models (software packages) are already available for the identification and classification of surface minerals materials from the USGS (Clark, Swayze et al. 2003, Kokaly, King et al. 2011) and from preparatory activities for the EnMAP mission, which have proven their suitability in the use with spaceborne imaging spectroscopy data e.g. from Hyperion and simulated data in preparation of the EnMAP mission (Mielke, Rogass et al. 2016), see Figure 3-14. Furthermore, these tools were demonstrated in a country-wide reference for mineral deposit targeting at large scale available through the USGS Afghanistan project: https://afghanistan.cr.usgs.gov/hyperspectral-data (Kokaly, King et al. 2013) and Figure 3-13.

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Figure 3-13 Largescale mineralogical mapping : Surface Materials Map of Afghanistan including Carbonates, Phyllosilicates, Sulfates, Altered Minerals, and Other Materials from HyMAP airborne sensor(Kokaly, Couvillion et al. 2013).

Figure 3-14 Automated mineral classification (EnGeoMAP) of a high sulfidation, epithermal gold deposit, in Rodalquilar, Spain (Mielke, Rogass et al. 2016).

3.2.2.2 Mine Environment Management

One of the most comprehensive studies related to the use of hyperspectral imaging for monitoring the environmental impact of mineral resources operations was conducted at the Port Hedland handling facility in Western Australia (Ong, Cudahy et al. 2003, Ong, Lau et al. 2008). In this study, the quantification of the impacts of fugitive dust originating from the iron ore handling facility on the surrounding mangrove ecosystem was needed to

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support an environmental regulatory compliance requirement. Here, the unique diagnostic capability of hyperspectral imaging was exploited to identify the composition of the dust, quantify the levels of dust and additionally, identify the surfaces on which the dust settled. Specifically, the diagnostic spectral features of iron oxide together with diagnostic vegetation spectral signature of mangrove leaves were used as the key tracers and a method was developed to provide measurements of the levels of iron oxide dust on mangrove leaves in mg/cm2 (Figure 3-15). This method was applied to data acquired over a ten-year period demonstrating the capabilities of hyperspectral imaging for providing quantitative measurements for multi-temporal monitoring. Although this development paved the way for hyperspectral imaging to be used for routine monitoring, it is only with operational missions such as this proposed CHIME mission that such applications used on an operational basis can be realised, and, indeed hyperspectral missions has been called for by the industry (see footnote 1) specifically to support environmental regulatory compliance requirements such as this.

Figure 3-15 Maps of ferric iron oxide dust levels on mangroves and other vegetation surrounding Port Hedland harbour, Western Australia, generated from remotely-sensed imaging spectroscopy data (HyMAP sensor) acquired on 20th October 2002 (top) and 24th August 2006 (bottom). The colour coding is such that cool colours (starting with dark blue) denote low fugitive dust levels and hot colours (ending with red) are high fugitive dust levels. The dust measurements are in units of g/m2. From (Ong, Lau et al. 2008).

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The EU Mine Waste Directive is a key regulatory framework for the management of waste resulting from prospecting, extraction, treatment, and storage of minerals. Managing the impacts of Acid Mine Drainage (AMD) is an environmental issue which has been recognized internationally as one of the more significant challenges in the mining industry (Harries 1997, Akcil and Koldas 2006) and in the EU, AMD related to legacy mines is a major challenge.

Hyperspectral imaging is able to provide key information related to AMD, as many of the minerals formed from the oxidation of pyrite and other iron sulfide minerals in mine waste and in mineralised rocks are spectrally discernable. Specifically, many of the secondary minerals produced by the AMD process such as copiapite, jarosite, schwertmannite, ferrihydrite, goethite, and hematite are iron rich and many are hydroxyl-bearing and thus have diagnostic spectral reflectance signatures (Swayze, Smith et al. 2000, Crowley, Williams et al. 2003). In fact, the use of hyperspectral imaging to map and monitor environmental parameters related to AMD has been well demonstrated (Fenstermaker and Miller 1994, Swayze, Clark et al. 1996, Farrand and Harsanyi 1997, López-Pamo, Barettino et al. 1999, Ong, Cudahy et al. 2003, Kemper and Sommer 2004, Zabcic, Rivard et al. 2014) and many others.

In addition, previous studies found that the secondary minerals generated as a result of acid mine drainage varied with pH (Bigham, Schwertmann et al. 1996) allowing for higher value added products such as pH maps to be generated. Indeed in preparation for the Mine Waste Directive, research and development were conducted to demonstrate the ability of hyperspectral imaging to provide such a higher-level derived pH product (Figure 3-16). Operational missions such as CHIME will allow environmental practitioners to access such data to enhance their decision-making.

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Figure 3-16 Predicted pH maps derived from airborne hyperspectral imagery (EnPAM sensor) acquired in July 2005 (a) tailing pond A located North of the processing plant (Sulphuric Acid plant/flotation plant); (b) tailing pond B West of the processing plant; (c) tailings C around the processing plant; (d) Main waste-rock tailings D, a more distal mine waste- rock tailing site found along the Odiel River from (Zabcic, Rivard et al. 2014).

Other areas where hyperspectral imaging has been used to support mine environmental monitoring applications and safety of mine operations include asbestos contamination (Frassy, Candiani et al. 2014, Bonifazi, Capobianco et al. 2017, Massarelli, Matarrese et al. 2017). Another example of monitoring environmental impacts can be seen in (Adar, Shkolnisky et al. 2014) who used hyperspectral imaging to distinguish small environmental changes based on spectral change over one year mining activity in an open mining peat area in Sokolov, Czech Republic.

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3.2.3 Additional Applications

3.2.3.2 Biodiversity And Ecosystem Sustainability

Large scale, long-term and spatially complete information on biodiversity is required as global biodiversity loss is intensifying (Jetz, Cavender-Bares et al. 2016). Earth observation data has been proven to be crucial in this, thanks to its capacity to provide global coverage of different biodiversity indicators and ecosystem properties (Skidmore and Pettorelli 2015). The whole spectrum of available sensors ranging from low to very high resolution, multispectral to hyperspectral and passive to active, have been used to directly or indirectly retrieve different biodiversity indicators with the degree of success typically depending on the used sensor characteristics.

Hundreds of narrow spectral bands, typically present in hyperspectral sensors, greatly contribute in obtaining these biodiversity indicators by providing a highly differentiated spectrometric signal of optical characteristics of biophysical or biochemical traits, and their changes in plants, plant communities and biodiversity (Homolova, Malenovský et al. 2013). Consequently, many studies have used hyperspectral data as input to obtain different biodiversity indicators like (invasive) species occurrence, habitat extent and quality, land use/land cover and their changes, plant functional types, as well as different biochemical and biophysical traits and indices of taxonomic and functional diversity.

The biodiversity variables (Essential Biodiversity Variables or EBVs) that can be retrieved from hyperspectral remote sensing are listed in the following table, classified by EBV class and entitled remote sensing enabled EBVs. In addition, a large number of the Convention on Biodiversity (CBD) Aichi targets can be supported by a hyperspectral imager.

EBV Class Candidate RS-EBV Potential support for (+RS-EBV subclass) Aichi targets 2 Species populations Species distribution 4,5,7,9,10,11,12,14,15 Species populations Species abundance 5,7,9,12,14,15

2 Aichi Biodiversity Targets (https://www.cbd.int/sp/targets/) comprise 20 targets in 5 strategic categories ranging from addressing the causes of biodiversity loss to knowledge management and capacity buildung. These Targets form an integral part of the Convention of Biodiversity (CBD) strategic plan for biodiversity 2011-2020.

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Species traits Plant traits (e.g., specific 7,9,12,14 leaf area, leaf nitrogen content) Community composition Plant taxonomic diversity 8, 10, 12, 14 Community composition Plant functional diversity 5,7,10,12,14,15 Ecosystem function Productivity (e.g., NPP, 5,7,10,12,14,15 GPP, LAI, FAPAR, Biomass) Ecosystem function Disturbance regime (fire 7,9,10,12,14,15 and inundation) Ecosystem function Carbon stock (biomass, 7,9,10,12,14,15 phenology, NPV, Specific Leaf Area, LAI) Ecosystem function Phenology (e.g., leaf-on 5,9,11,12,14,15 and leaf-off dates; peak season) Ecosystem function Canopy biochemistry 5,7,10,12,14,15 (chlorophyll content and foliar nitrogen content) Ecosystem function Biotic water dynamics 7,9,10,12,14,15 Ecosystem structure Surface cover (e.g., crown 5,7,9,14,15 cover and density) Ecosystem structure Ecosystem extent and 5,11,12,14,15 fragmentation – land cover Ecosystem structure Ecosystem composition 5,7,10,12,14,15 by functional type Ecosystem structure Vertical distribution 5,7,9,14,15 (vegetation height, structural variance and vertical heterogeneity) Table 3-2 Essential Biodiversity Variables retrievable from hyperspectral remote sensing.

Hyperspectral sensors can provide additional, mostly proximal information about habitat and niche, for animal diversity mapping and monitoring. Direct monitoring of animals from space has been limited to very high-resolution imagery, such as identifying East African mammals in open savanna (Yang, Wang et al. 2014) though it has been suggested that with the addition of hyperspectral imagery the species of individuals could be identified. Another advantage of hyperspectral compared to multispectral sensors is in providing advanced trait based information (Schweiger, Risch et al. 2015), which permits

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accurate modelling of species distribution. A recent compelling example demonstrating the value of a hyperspectral imager, at a continental level, uses MODIS data, and models the distribution and endemism of the approximately 250 bat African species stacked together to map species diversity (Figure 3-17) (Herkt, Barnikel et al. 2016). A specialised hyperspectral imager such as CHIME would further improve the accuracy and spatial resolution of animal species mapping.

Figure 3-17 Species Distribution Models (SDMs) of all 250 African bat species stacked together at 1 km2 to explore emerging diversity patterns. Predicted species richness generally increases towards the equator conforming to expectations. Centres of endemism are found primarily at low latitudes near major elevation ranges. Overlap with hotspots of species richness is rather low. Generated with MODIS imagery and other ancillary geographical data layers.

There are numerous examples of airborne hyperspectral imaging being used for plant species and trait mapping – an early application using the CASI hyperspectral imager combined with airborne LIDAR data was adopted for monitoring coastal vegetation and demonstrates the utility of detecting plant biodiversity (Schmidt and Skidmore 2003) (Figure 3-18). The change in biodiversity, resulting from to sea level rise and land sinking from gas extraction in the Netherlands, was most accurately mapped (66%) compared with the gold standard technique of interpreting aerial photography (43%). In addition, the geese grazing intensity (species abundance) is influenced by the plant species – the grazed short plants of the early succession are most nutritious and palatable, while the high marshes (Elymus sp.) have low species abundance and remain ungrazed.

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Figure 3-18Species mapping of saltmarsh and coastal biodiversity (at species level) using airborne hyperspectral imagery at 3 m resolution (CASI).

Since then, the Carnegie Airborne Observatory (CAO) hyperspectral imagery has operationalised the mapping plant species in agricultural landscapes (Graves, Asner et al. 2016) (Figure 3-19) as well as tropical forests (also see next section - Forestry - for an example from Switzerland).

Figure 3-19 Species abundance of trees in a tropical agricultural landscape. CAO airborne hyperspectral imagery reformatted to 5 m resolution classified with a support vector machine (SVM). Important indicators of biodiversity include traits associated with variation in productivity, functional diversity and ecosystem structure – an important example is foliar nitrogen,

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which is now routinely retrieved from airborne hyperspectral imagery for biodiversity monitoring (as well as forest productivity monitoring – see 3.2.3.2 Forestry). In (semi)- natural grassland and woodland systems, the foliar nitrogen content is a key indicator of forage quality and hence herbivore diversity and abundance. Work in South Africa using the airborne HyMAP hyperspectral imager shows that high resolution maps of foliar nitrogen (i.e. protein for grazers) of grass and dominant mopane woody vegetation, as well as tannin (i.e. deterrent for herbivores), can be simultaneously modelled (see Figure 3-20). In this work, an artificial neural network was used in a feed-forward mode to train the model using a series of ground points, and the model was then inverted to retrieve foliar nitrogen over larger areas (Skidmore, Ferwerda et al. 2010).

The work on foliar nitrogen mapping of grasslands and savannah ecosystems was further extended to take advantage of the red edge of the RapidEye satellite (at 5 m resolution) – thereby proving the concept that leaf nitrogen content and canopy nitrogen content may be measured from space in order to monitor the biodiversity of the African savannah ecosystem using a non-linear spatial PLSR (Ramoelo, Skidmore et al. 2012) see Figure 3-21. The ESA/EU CHIME satellite would allow such monitoring at a global level through the provision of the red edge at adequate (20-30 m) spatial resolution.

Functional diversity, referred to as the rate that organisms undertake functions within an ecosystem, is proposed as a proxy for biodiversity (Jetz, Cavender-Bares et al. 2016). A recent ESA funded Innovators-III project to explore monitoring functional diversity from the Sentinel-2 satellites showed that functional dispersion, divergence, richness and evenness may be mapped from the red edge of the Sentinel-2 multispectral sensor when using time series data, but that the provision of true hyperspectral imagery would greatly enhance retrieval accuracy (O'Connor, Skidmore et al. 2016).

Figure 3-20a) Homogeneous areas with respect to parent material and fire (F = fire, NF = no fire), overlaid on a map of parent material.

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Figure 3-20b) Foliar nitrogen concentration in grass (percent). The white areas on this figure represent the ‘tree’ pixels (i.e. the binary sliced tree pixels which appear in Figure 3-20c and Figure 3-20d. Lines are geological class boundaries as defined in Figure 3-20a.

Figure 3-20c) Foliar nitrogen concentration for mopane trees and shrubs (percent). The white areas represent the ‘grass’ pixels (i.e. the binary sliced grass pixels which appear in Figure 3-20b). The lines are geological class boundaries as defined in Figure 3-20a.

Figure 3-20d) Total polyphenol concentration for mopane trees and shrubs (quebracho polyphenol equivalents in gg− 1). The white areas on Figure 3-20d represent the ‘grass’ pixels (i.e. the binary sliced grass pixels which appear in Figure 3-20b). Lines are geological class boundaries as defined in Figure 3-20a.

Figure 3-20 Forage quality of savanna- simultaneously mapping foliar protein and polyphenols using 3 flight lines of HyMap airborne hyperspectral imagery.

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Figure 3-21 Map showing the spatial distribution of the foliar nitrogen (top) and canopy nitrogen (N*PV) (bottom) in relation to geology classes such as basalt, gabbro, granite and shale (PV = photosynthetic vegetation cover). Foliar nitrogen retrieved from the red edge bands of RapidEye.

3.2.3.2 Forestry Within the forestry domain, the limited availability of operational hyperspectral satellite data can be seen as a main reason for the current lack of operational applications. Current technological developments of light-weight hyperspectral sensors and Unmanned Aerial Vehicles (UAVs) however, lead to a potential increase in possibilities for the investigation of applications that can be integrated in forest monitoring practices for future hyperspectral imaging satellite missions. Most research towards an operational application was made with data collected by airborne hyperspectral sensors. The next section will summarise the most current evolvements and demonstrated hyperspectral applications that are relevant to forestry.

Assessment / Monitoring Of Biotic Damages (e.g. Fungi, beetles) In Forest Stands Using Hyperspectral Imagery In different studies in the past, the potential of hyperspectral data for the detection and assessment of biotic damages in forest stands has been examined (Gao and Goetz 1995, Martin and Aber 1997). The studies included field spectrometers, airborne and spaceborne hyperspectral data. (Coops, Stone et al. 2003) used Hyperspectral airborne imagery

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(Compact Airborne Spectrographic Imager-2 or CASI-2) to detect dothistroma needle blight caused usually by the fungus Dothistroma septosporum on conifer species such as Pinus radiata. The study showed a statistically significant relationship between crown reflectance values and ground estimates using a set of different indices such as the lower slope of the red-edge or the red-edge vegetation stress index (RVSI) developed by (Merton and Harvey 1997). Further studies in the field of the assessment of abiotic forest damages used the EO-1 Hyperion instrument with 220 spectral bands to detect mountain pine beetle red attack damage (White, Coops et al. 2007) or tree defoliation caused by spruce budworm in Quebec, Canada (Huang and Zhang 2016). Based on a selection of six moisture indices from Hyperion data, which incorporated both NIR and SWIR regions of the electromagnetic spectrum, were correlated (especially the Moisture Stress Index (MSI) and the Normalized Difference Infrared Index (NDII)) to different levels of damage (White, Coops et al. 2007). (Huang and Zhang 2016) used bi-temporal EO-1 Hyperion data for the detection and change of spruce budworm defoliation and generated two different stress maps for each acquisition based on a simple ratio of NIR and RED, the Normalized Difference Water Index and the Red Green Ratio Index. The stress maps generally matched the results achieved from an aerial survey. The potential of hyperspectral imagery (airborne hyperspectral data, HyMap) to map bark beetle induced tree mortality was investigated by (Fassnacht, Latifi et al. 2014). The HyMap data was examined combining a genetic algorithm (GA) for feature selection with a supervised support vector machine (SVM) classification to differentiate between three different mortality classes. The GA analysis showed that bands from different parts of the electromagnetic signal (near green peak (560nm), around chlorophyll absorption (680nm), the red edge rise (690nm), NIR (1076nm) as well as SWIR (1532)) notably contributed to the high classification accuracies e.g. for differentiation between dead trees and healthy trees. Recent work in central European forests shows that the European spruce bark beetle can be monitored through hyperspectral remote sensing whilst still in the green attack phase (i.e., before visual damage is apparent) (Abdullah, Darvishzadeh et al. 2018). This finding demonstrates the potential of operational detection of European spruce budworm using a hyperspectral imager, as proposed with CHIME.

Estimation Of Forest Canopies Chemical Compounds The estimation of plant chemical components is especially of interest in the agricultural domain. Of special importance is either the nitrogen content or the retrieval of photosynthetic activity by estimating the chlorophyll content. The canopy chlorophyll content is an essential eco-physiological variable for photosynthetic production. Only a few studies have been focused on forest ecosystems, but the retrieval of forest canopies chemical components and the spatial explicit representation of these is an important input to various themes. Large-scale foliar nitrogen maps can be generated to improve the modelling of ecosystem processes as well as to model ecosystem-climate feedbacks. Forest canopy nitrogen concentration images can be used to predict patterns of forest productivity and quantifying changes in canopy nitrogen content provides direct information about ecosystem functioning. Nitrogen content was researched in various studies. (Martin, Plourde et al. 2008) predicted nitrogen concentration to be within 7–15% of the mean field measured values indicating a strong potential.

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Remotely Sensed Physiological And Morphological Traits For Functional Diversity Assessment

It is largely agreed upon that global trait maps are not complete and are key to monitoring global plant functional diversity from space (Jetz, Cavender-Bares et al. 2016). Since achieving statistically relevant ground sampling schemes in forests are even trickier compared to low growing vegetation, key efforts have been implemented to forward simulate 3D forest canopy behaviour, allowing to validate trait based approaches from remotely sensed data (Morsdorf, Kükenbrink et al. 2017 (in press)). Key effort was an ESA initiated project (3DVegLab - http://www.geo.uzh.ch/microsite/3dveglab/) to reconstruct 3D canopy morphology and physiology using best effort approaches. 3D forward based modelling allows then comparison of top-of-canopy retrieval methods of high spectral resolution (Schneider, Leiterer et al. 2014). Particular focus is put on retrieval of physiological traits (or optical traits,(Homolova, Malenovský et al. 2013)) along with morphological traits, whereas for the latter usually LiDAR instruments are suited better to retrieve canopy architecture attributes. However, limited canopy architectural traits may be retrieved using imaging spectrometers as well (c.f. LAI). Final functional diversity assessment is based on mapping the trait space in several dimensions and applying scale dependent methods to assess functional diversity in forests (Schneider, Morsdorf et al. 2017) (see Figure 3-22).

Figure 3-22 Physiological diversity of a temperate forest as measured using airborne imaging spectroscopy data from APEX (Schaepman, Jehle et al. 2015). Diversity in physiological traits (leaf chlorophyll, carotenoids and water content) of the forest as functional richness at a radial neighbourhood of 90 m is low at ridges and other environmentally constrained areas (Schneider, Morsdorf et al. 2017). (Image data: UZH - Original data for download: http://www.media.uzh.ch/en/Press-Releases/2017/biodiversity-remote-sensing.html).

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3.2.3.2 Coastal And Inland Waters Inland and coastal water ecosystems are environmentally important, provide multiple ecosystem services and are vital for human consumption, irrigation, sanitation, transportation, recreation and industry. Since more than four decades, EO data has been used to acquire timely, frequent synoptic information from local to global scales of aquatic ecosystems. These systems are often a mixture of optically shallow and optically deep waters, with gradients of clear to turbid and oligotrophic to hypertrophic productive waters, and with varying bottom visibility with and without macrophytes, macro-algae, benthic micro-algae or corals. To observe such optical complexity, imaging spectrometry has clear advantages over multispectral sensors; for instance, (Botha, Brando et al. 2013) demonstrated that discriminating different coral colours to increasing depth is possible only when switching from multispectral to hyperspectral data.

Overall, hyperspectral information is expected to provide more accurate assessments of turbidity and transparency measures, chlorophyll, suspended matter and coloured dissolved organic matter concentration to more sophisticated products such as particle size distributions, phytoplankton types and pigments, harmful algal blooms, distinguishing sources of suspended and coloured dissolved matter, estimating water depth and mapping heterogeneous substrates and cover types.

Since 2000, spaceborne hyperspectral sensors (e.g., Hyperion, CHRIS) have showed increasing capabilities in inland, estuarine and coastal water applications (Brando and Dekker 2003, Kutser 2004, Kutser, Miller et al. 2006, Giardino, Brando et al. 2007, Lee, Casey et al. 2007, Santini, Alberotanza et al. 2010, Zhu and Yu 2013), although having a signal-to-noise ratio (SNR) not optimised for water. An exception is for HICO whose improved radiometry allowed to capture a red ciliate bloom in coastal waters (Dierssen, McManus et al. 2015). Airborne systems have been providing optimal resolutions for developing local to regional scale applications such as: inventories of benthic habitats and status e.g. macrophytes, sea grasses and corals as in (Phinn, Roelfsema et al. 2008, Hunter, Gilvear et al. 2010, Joyce, Phinn et al. 2013) (see Figure 3-23), and (Bertels, Vanderstraete et al. 2008); detection of invasive weed species in wetlands e.g. (Hestir, Khanna et al. 2008, Villa, Pinardi et al. 2017); assessment of water quality in rivers e.g. (Olmanson, Brezonik et al. 2013) and simultaneously mapping of water quality parameters, bottom types and depth in both inland and coastal waters e.g. (Lee, Carder et al. 2001, Dekker, Phinn et al. 2011, Jay and Guillaume 2014, Giardino, Bresciani et al. 2015).

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Figure 3-23 Seagrass species maps to 3.0 m depth (Eastern Banks in Moreton Bay, Australia) derived from CASI-2 data. Species names were abbreviated due to space limitations: Ho — Halophila ovalis, Hs — Halophila spinulosa, Zm — Zostera muellerei, Cs — Cymodocea serrulata and Si — Syringodium isoetifolium from (Phinn, Roelfsema et al. 2008).

Water quality-related applications, like the retrieval of chlorophyll content and secondary pigments as proxies for harmful algae blooms, the identification of phytoplankton types, or the benthic habitat mapping would strongly benefit from hyperspectral information (Vahtmäe, Kutser et al. 2006, Hestir, Brando et al. 2015). High SNR and spectral resolution in the VNIR (5-10 nm), and a short revisit time are identified as important for operational water monitoring applications. Measurements in the SWIR are used to estimate total suspended matter (TSM) in extremely turbid waters (Knaeps, Ruddick et al. 2015), to characterise emerging aquatic vegetation and floating materials, and to improve the atmospheric correction. High spatial resolution would also be needed to monitor most of the lakes required by the EU Water Framework Directive and Bathing Water Directive. Those conclusions are supported by the findings of a recent CEOS study towards the assessment of potential hyperspectral mission concepts for aquatic ecosystems, which states “focus for new sensors should be around 5 to 8 nm spectral bands and a GSD of about 17 m, whilst a GSD of 30 m could be a compromise between costs and SNR” (Dekker, Pinnel et al. 2018).

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3.2.3.2 Environmental Degradation And Hazards Natural and anthropogenic hazards have the potential to impact on all aspects of society, including its economy, its habitats health and wellbeing, and the environment. Diagnostic information to inform decisions is critical for hazard management whether as an emergency response, for routine monitoring or assessments of potential risks. Hyperspectral imaging has unique contributions to make via the ability to provide some key quantitative diagnostic information.

Examples where imaging spectroscopy have been used for mapping environmental degradation and hazards other than those provided for the raw materials area include:

1) Hyperspectral imaging were acquired following the 11th September 2001 World Trade Centre (WTC) attack and the data were used for mapping thermal hotspots and potentially hazardous dust (Clark, Swayze et al. 2005) (Figure 3-24);

Figure 3-24 AVIRIS measurements of the WTC site on the 16th of September 2001. Hot spot fire temperatures were calculated from AVIRIS spectra and delivered to ground teams (left). Surface debris composition includes Chrysotile. Mineral asbestos was mapped from AVIRIS spectroscopy by the USGS.

2) Hyperspectral imaging was used to estimate the distribution and volume of the oil slick on the ocean surface (Clark, Swayze et al. 2010) and the impact of oil on the terrestrial ecosystem (Kokaly, Couvillion et al. 2013) from the largest oil spill in U.S.A. history in the Gulf of Mexico (Figure 3-25);

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Figure 3-25 (a and b) AVIRIS spectra were acquired over the gulf and used to estimate the area and thickness of the oil. These were used to deliver the first volume estimates to the U.S. government. AVIRIS imaging spectrometer measurements were acquired over the vulnerable coastal region to determine the initial composition and function of the ecosystems. Oil impacted regions were measured in subsequent year to ass the impact and recovery of the ecosystems.

3) Spaceborne hyperspectral imaging from EO1 Hyperion mapped a very large accidental gas release (Thompson, Thorpe et al. 2016). The detection of gas plumes from other “super-emitters” have also been demonstrated with higher spectral and spatial resolution and higher signal-to-noise sensors such as AVIRIS-NG (Thorpe, Frankenberg et al. 2016) (Figure 3-26);

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Figure 3-26 AVIRIS measurements of the Aliso Canyon, CA gas storage blow out. AVIRIS imaging spectrometer measurements were used to map the plume of this super emitter and confirm absence of methane once it had been capped.

4) Pioneering work from (Chabrillat, Goetz et al. 2002) demonstrated the potential of hyperspectral imaging for detecting and mapping swelling clays with a study along the Front Range Urban corridor near Denver, Colorado, USA;

5) A soil pH map of the surface impacted by acid sulphate soils was inferred according to the relationship between the indicative mineral species and measured pH values (Shi, Lau et al. 2014); AVIRIS imaging spectroscopy measurements were used to accelerate remediation of the acid mine drainage EPA superfund site at Leadville, CO (Figure 3-27) (Swayze, Smith et al. 2000);

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Figure 3-27 Map of acid generating mineral (left). Example AVIRIS spectra with diagnostic spectral absorption features from the Leadville site.

6) (Chudnovsky, Ben-Dor et al. 2009) documented the use of spaceborne hyperspectral imaging data acquired by EO-1 Hyperion for determining the composition of the dust at the Bodélé Depression of Northern Chad, considered to be one of the world’s largest sources of atmospheric mineral dust;

7) Airborne hyperspectral data (MIVIS) were used for mapping asbestos-cement roofing sheets as well as their deterioration status, the latter related to the asbestos fiber air dispersion that can cause lung cancer (Bassani, Cavalli et al. 2007, Frassy, Candiani et al. 2014);

8) Imaging spectrometer measurements have in the recent past been used for detailed mapping of fire fuel (Roberts, Dennison et al. 2003), temperature (Green 1996, Dennison, Charoensiri et al. 2006), burn severity (Kokaly, Rockwell et al. 2007), and recovery (Riaño, Chuvieco et al. 2002). Elements fo the contribution of Visible to Short Wavelength InfraRed (VSWIR) imaging spectrometer measurements for a 2017 fire in Southern California are shown in Figure 3-28.

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Figure 3-28 AVIRIS imaging spectrometer measurement over the Thomas fire in Southern California on 5 December 2017 (left). Fire fuel (dry biomass and leaf water) from AVIRIS measurement in the summer of 2017 (top row right). Visible image, infrared image, and spectroscopic fire temperature retrieval (bottom row right).

3.2.3.2 Hydrology / Cryosphere

Water is one of the most important goods on the planet. In addition to agriculture and food, the water cycle has its impact in transportation and energy production. One important aspect in the cycle is the cryosphere, when water is stored as snow or ice. Monitoring of snow cover is of importance for hydrology in high latitude or alpine areas. In low mountain ranges significant snow dynamics occur as well. Due to temperature alteration, the snow pack accumulates and melts off several times during a winter season. In combination with rainfall, the snowmelt is the most critical impact for floods. Microwave observations from SAR (synthetic aperture radar) are used for the detection of wet / melting snow. Those snow parameters are common information for scientists and users of hydrological models.

Potential new hyperspectral products would go towards deeper knowledge of physical snow properties: snow grain size, snow light-absorbing impurities and snow surface properties, including albedo, for better partitioning of the energy fluxes to enhance snow modelling and to improve run-off forecast (Naegeli, Damm et al. 2015). Hyperspectral imagery would allow better observation of snow cover dynamics regarding energy transfer (in and out snow pack) for better results in hydrological forecasts (runoff and energy), and linking small to large scale effects (Dal Farra, Kaspari et al. 2018).

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Due to the ephemeral nature of snow properties, the cryosphere community would mostly benefit from a higher temporal resolution allowing to track changes in snow as well as increasing the chances for acquisitions over frequently clouded areas. Absorption features of snow used to estimate grain size and snow water equivalent are typically broader than 20 nm. Ideally, high spatial resolution observations ~10 m would be used, but a resolution up to 30 m would be allowed.

Detailed knowledge of snow albedo is important to accurately model water runoff from snow melt and predict a possible avalanche event. Imaging spectroscopy in the VSWIR range enables accurate estimation of grain size that controls key elements of snow/ice albedo (Nolin and Dozier 2000), (Painter, Dozier et al. 2003, Painter, Deems et al. 2010) and (Green, Dozier et al. 2002). Spectroscopic measurements in this spectral range allow estimation of dust and black carbon in snow that also effects albedo (Painter, Deems et al. 2010). An example showing the combined impacts of grain size and dust on snow albedo is shown in Figure 3-29.

Figure 3-29 AVIRIS imaging spectrometer measurements of dust impacted snow in the Colorado Rocky Mountains (left). VSWIR spectroscopic leverage used to retrieve gain size (right top) and dust albedo impact (right bottom) to constrain radiative forces and model melting rates and water resource runoff.

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The detailed melting state of snow and ice can be retrieved in the VSWIR spectral range (Green, Painter et al. 2006) (see Figure 3-30).

Figure 3-30AVIRIS imaging spectrometer measurement of snow and ice covered Mount Rainier, Washington state (left). Modelled absorption of water vapor, liquid and solid in the VSWIR spectral region (centre). Retrieved vapor, liquid, and solid phases of water at Mount Rainier as blue, green, and red (right). Melting snow is yellow (green + red).

3.3 Summary Of User Requirements And High Level Observational Requirements From the previous examples and the applications and user requirements presented in the Traceability table of Chapter 4, it is clear that the whole optical spectral range between 400-2500 nm in high spectral resolution is needed to fulfil the two mission application pillars, agriculture and food security, and raw material, which cover the retrieval of a wide range of bio- and geophysical variables. In addition, we can infer the following general conclusions regarding high level observational requirements (especially regarding spatial and temporal resolution) for particular application domains: a. Agriculture and Food Security: In the context of the main elements of food security (i.e. availability, access, utilisation, and stability), remote sensing plays an important role for the availability scenarios, and may offer new perspective to also contribute in term of food nutrition. Spectroscopic techniques can be used to accurately identifiy crops, grasslands species and to exploit spectral absorption features from leaf pigments, water and biochemical compounds in order to characterise plant functioning and type. In addition hyperspectral imaging may support a better soil management to increase crop production and yield. This leads to two wide areas of application for hyperspectral techniques

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regarding to agriculture and food security: i. Vegetation - productivity and functioning, are central to food security applications. Hyperspectral observations are needed for the monitoring of plant productivity and are based on the exploitation of spectral indicators of green biomass, pigment pools, and photosynthetic functioning. Of notable important is the assessment of stress in crops and ecosystems using hyperspectral remote sensing. These applications would benefit from a high temporal and spectral resolution to monitor vegetation growth and for early-stress detection. Spatial resolution should be in accordance to Sentinel-2 (10 – 20m), which would allow the mapping of small agricultural units, but an upper limit up to 30 m is acceptable for many agricultural regions and applications. Vegetation - crop types as well as vegetation structure are recognised as Essential Biodiversity Variables (EBVs) (Pereira, Ferrier et al. 2013). Hyperspectral applications in biodiversity and forestry take advantage of the high potential of hyperspectral information to separate vegetation classes and to retrieve critical plant functional traits. The highest spatial resolution possible would be required for the monitoring of ecosystem composition in the most heterogeneous areas (e.g. for forests, for monitoring of protected habitats and for the monitoring of species occurrence). Also, the detection of invasive species for land management activities would highly benefit from the highest spatial resolution possible (ideally in the range of 20-30 m). On the other hand, a relatively sound temporal resolution would still be necessary to track seasonal changes in plant structure and traits, which is also relevant to biodiversity applications. FLEX hyperspectral resolution across the fluorescence region would be complimentary to CHIME hyperspectral information. ii. Soils - the potential of hyperspectral data for mapping and monitoring soil properties relies mostly on direct chromophores that are soil mineralogy (clay, carbonates and iron oxides) and organic carbon and on soil properties that are highly correlated to those parameters (e.g. Cation Exchange Capacity is highly correlated to clay content and specious). Current multispectral sensors cannot extract mineralogical composition and so hyperspectral imagery is crucial for the derivation of fundamental soil properties linked to food security such as organic carbon content, clay and water topsoil content, soil degradation stages and soil quality status. In the case of soil mineralogical composition, it is relatively stable and therefore high spatial resolution is preferable to high temporal resolution; in the case of soil organic carbon, high spatial resolution would be required for the derivation of soil organic content baseline inventories, but high temporal resolution would also be important for a monitoring system optimised to quantify changes especially during the vegetation- free period. In the case of indirect soil properties, high spectral resolution and knowledge of the relationship between all soil properties matrix is

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important. This can be extracted from Global Soil Spectral Libraries that are now very popular and globally wide spread for potential users (Rossel, Behrens et al. 2016). While the multitemporal domain is a necessary requirement for food availability and production, specific information extracted at regular intervals by a spaceborne hyperspectral imager will provide a unique opportunity to extend the analysis also for food nutrition. Soil quality indices can be derived from combining several important properties, as demonstrated by (Paz-Kagan, Zaady et al. 2015). High quality maps of crops biodiversity and distribution, invasive species, pest disease and outbreak, rangeland species distinguishing between palatable or not palatable and their properties in term of nitrogen content and biomass, represent key parameters for defining nutrition condition and food utilisation.

b. Raw Materials: The potential to map mineral resources and other raw materials is one of the most compelling cases for imaging spectroscopy compared to multispectral systems. Currently, no multi-spectral system is able to resolve mineralogical compositional information. At best, ASTER and WV3 are able to provide relative mineral groups but this is none-specific. In general, geological applications benefit the most from high spatial resolution as well as from a relatively high spectral resolution (<12 nm) and SNR in the VNIR-SWIR, where many minerals and raw materials present absorption features. Mineral mapping and geological applications typically occur over relatively stable areas, so temporal resolution is not considered as a limiting factor. Mining applications such as resource will benefit from moderate temporal resolution and high spatial resolution. Mine environmental monitoring including mine waste, rehabilitation, dust contamination and other surface pollution are applications requiring a moderate temporal resolution of several months, but a higher spatial resolution would be preferred over high temporal resolution. The specific case where high temporal resolution is required is for emergency responses in the event of an accidental release such as a tailings dam breach. Here, a high revisit time and real-time product delivery is required.

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4 TRACEABILITY OF HYPERSPECTRAL IMAGING MISSION POLICY NEEDS/APPLICATIONS, USERS & STAKEHOLDERS AND OBSERVATIONAL REQUIREMENTS

The potential contribution of a hyperspectral mission to the Copernicus Programme should be demonstrated highlighting how the identified data products, and more generally the applications addressing user needs can respond to specific EU policies and directives issued to regulate at European level several thematic areas.

The following traceability matrix (Table 4-1) can be considered as the link between user needs and requirements, defined on the basis of high-level EU policies and the mission requirements (Chapter 6).

The table is organised as follows:

• 1st column: EU policies, directives or convention that can be addressed by EO products or applications derived by hyperspectral data • 2nd column: market areas of interest • 3rd column: the end- or intermediate user communities and stakeholders interested in the use of data. They can be split in to: − public institutional sector: their needs and requirements are on monitoring or reporting obligations derived from EU policies or national law − private sector: their requirements linked to operational needs to improve their own processes − research institutions or academia (e.g. geoscientists) can be considered as stakeholders in all the domains. • 4th column: description of the user needs • 5th column: EO data products that can be generated exclusively with a hyperspectral sensor, or have a scientifically proven added valued with hyperspectral information in comparison with other EO techniques • 6th column: the key observational requirements are those mainly related to the users. The mission requirements should directly be derived from these high level observational requirements.

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1. Policy/Directives/ 2. Market Area 3. Stakeholders 4. User needs 5. EO Data products 6. Key observational Conventions/initiatives Requirements EU Common Agriculture Agriculture & Food Public Sector: Spatio-temporal monitoring Vegetation properties: Spatial resolution: HIGH Policy (CAP) security EC-DG-Agri of crops & harvest regional - Fraction of Photosynthetically Spectral resolution: HIGH - Crop phenology & EC_DG_Grow management Active Radiation Temporal resolution: Nitrates European directive productivity European - Leaf Area Index MODERATE - Stress & disease Environmental Agency Agricultural statistics - Leaf Pigment content (Chl, EU Thematic Strategy for - Smart farming EC – Joint Research Car, Anth) Sentinel 2 compatibility: YES Soil Protection - Food nutrition Centre (JRC) Food security scenarios, - Canopy water/ dry matter - Livestock nutrition and early warning content EU Plant Health Law (n. Production National - Canopy nitrogen uptake 2016/2031) Administrations Monitoring of plant traits - Crop phenology, EWT (cm) (biochemical/phenological - Leaf Mass Area 2015 European consensus International features to characterise - Fraction of Non- for Development Organisations (e.g. FAO, plants and detect crop Photosynthetic Vegetation WFP) stresses and disease) - Light use efficiency Soil Framework Directive National Environmental Support of smart farming/ Vegetation types (classification) Food Security and Authorities precision farming activities - Species identification (e.g. Nutrition(FAO) Fertilization and plant crop type, invasive species, Global Livestock Production Water Protection protection management palatable/non palatable) System, Rangeland and Agencies - Crop health and damage Pasture Productivity - Grazing patterns (FAO/ILRI) Private Sector: - Detection of weeds Individual Farmers, Irrigation Management Farming Organisations Topsoil properties : Sustainable Development Farming Advisors - Texture, Goal No.2, No. 12, No. 15 Seeding Companies - Organic carbon, Agrochemical Quantification of stabilising - Rock mineral composition, Companies features such as dry matter, - Soil salinity litter, organic and mineral Renewable Energy Industry crusts. Food Industries Agricultural Insurances

Financial Industries Characterisation of soils with respect to fertility and degradation

EU Raw Materials Minerals and Public sector; Discovery of economically - Mineral (Ferric and ferrous Spatial resolution: HIGH Initiative, European Mining: regulators, valuable deposits, ore oxides, phyllosilicates, Spectral resolution: HIGH Innovation Partnership on Raw materials/ administration, national resource modelling, carbonates, sulphates) Temporal resolution: Raw Materials, Thematic Greenfield/ geological surveys, land identification and composition, chemistries and MODERATE Page 55/95 Copernicus Hyperspectral Imaging Mission for the Environment - Mission Requirements Document Issue Date 23/07/2019 Ref ESA-EOPSM-CHIM-MRD-3216

1. Policy/Directives/ 2. Market Area 3. Stakeholders 4. User needs 5. EO Data products 6. Key observational Conventions/initiatives Requirements Strategy on the sustainable Brownfield mineral use planning, risk monitoring of mine-waste abundances use of natural resources, EU resource mitigation, and other impacts, legacy - Surface rock types Main driver for high spectral Mine Waste Directive, characterisation and environmental impact mines type-, extent-, and - pH maps resolution (≤ 10nm) in VNIR – UN Convention for modelling, studies concentration and - Heavy metals, effluorescent SWIR, and spectral coverage in Combating Desertification extraction/ mining, corresponding salts VNIR – SWIR and spatial and Land Degradation, remediation/ Science: dissemination pathways and - Dust and other pollution resolution is for mine Directive on Sustainable rehabilitation/ Geologists, Petrologists, modelling of risks, (asbestos) mapping environmental applications and Use of Phosphorus, legacy mining Mineralogists, Environmental license to - Acidic water quality mining. (Brownfield exploration Sustainable Development issues/environment Metallurgists, operate and regulatory monitoring driving temporal sampling due to Goal No. 12, No. 15 al compliance to Environmental requirements for raw difficult weather conditions and regulation scientists materials exploitation incl. frequent vegetation coverage) environmental baseline Health Insurance: Private Sector: assessments, monitoring, Environmental Mining Companies, ecosystem valuing, Health Impact Exploration and Mining remediation and Service Providers, rehabilitation monitoring: economists, - Areas of economically Environmental valuable interest Consultants - Characterisation of environmental baseline/value - Quantifying and monitoring progress of rehabilitation/revegetation - Acid Mine Drainage affected areas - Tailings and other mine waste mineralogy and chemistry, pH maps, modelling of heavy metal risks

EU Soil Thematic Strategy, Agriculture and Ecologists, public Environmental Protection; - Soil mineralogical Spatial resolution: HIGH Soil Framework Directive, arable lands, soils; services (administration, Soil conservation /Land composition (clay, carbon, iron Spectral resolution: HIGH UN SEEA (System of Public, local and national geological degradation (e.g. erosion oxides, carbonates, gypsum) Temporal resolution: Environmental-Economic regional planners surveys, land use intensity); Soil Attributes - Soil variables (Corg, texture, MODERATE

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1. Policy/Directives/ 2. Market Area 3. Stakeholders 4. User needs 5. EO Data products 6. Key observational Conventions/initiatives Requirements Accounting), UN for conservation planning, risk (Nitrogen, Carbon, salinity, nutrients, Data latency: MEDIUM/LONG Convention for Combating management mitigation, Carbonate, and Organic hydrophobicity, contamination, Desertification and Land environmental impact matter); Soil Restoration infiltration rate, soil sealing, Driving temporal sampling at Degradation Common studies), NGOs soil contamination top-soil moisture, soil local and regional scales Agricultural Policy (CAP), areas prone to soil erosion compaction) to quantify erosive loss under Fertilizers Regulation, - Soil carbon storage (inorganic varying weather conditions EU Biodiversity Strategy and –organic carbon, 2020, hydrocarbons) Regional Policies - Heavy metal type and extent (INTERREG), - Organic matter discrimination EFSA strategy 2020, (dry matter, litter to humus) Sustainable Development - Soil quality index Goal No. 15 Natura 2000, EU regulation Biodiversity and Local / regional nature Biodiversity in Vegetation properties: Spatial resolution: conservation agencies agricultural landscapes - Fraction of Photosynthetically 1143/2014 for monitoring conservation, National environmental - Mapping & monitoring Active Radiation HIGH/MEDIUM and early warning system sustainable reporting agencies Essential Biodiversity - Leaf Area Index Spectral resolution: HIGH for Invasive Alien Species, development Intergovernmental Variables (EBV) globally - Leaf Pigment content (Chl, Temporal resolution: HIGH Science-Policy Platform - National reporting for Car, Anth) UN Convention on on Biodiversity and UNCBD - Canopy water/ dry matter Biodiversity (Aichi targets), Ecosystem Services - Monitoring of Invasive content Data latency: SHORT/MEDIUM Sustainable Development (IPBES) Alien Species - Canopy nitrogen uptake - National Biodiversity - Crop phenology, EWT (cm) Goal No. 15 Strategies and Action - Leaf Mass Area Temporal resolution as driver Plans (NBSAPs) - Fraction of Non- - National reports on Photosynthetic Vegetation Biodiversity to the UNCBD - Light use efficiency UNEP GEO (Global - Agricultural supply chain Environment Outlook for certification report Floristic gradients in grasslands the UN Environment - Forest management Crop diversity certification by Forest Programme) Stewardship Council (FSC) - IPBES Regional and Global assessments

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1. Policy/Directives/ 2. Market Area 3. Stakeholders 4. User needs 5. EO Data products 6. Key observational Conventions/initiatives Requirements EU Forest Strategy Forest management Public sector: Forest Inventories & Vegetation properties Spatial resolution: HIGH National forest status monitoring UN-REDD Programme administrations, Nature - Mapping & monitoring of - Fraction of Photosynthetically Spectral resolution: HIGH conservation agencies, tree species Active Radiation Temporal resolution: FAO Global Forest NGOs, Timber - forest condition (e.g., - Leaf Area Index MODERATE resources Assessment processing industry health, water stress, fuel - Leaf Pigment content (Chl, Governmental and type), Invasive species, Car, Anth) Global Forest Convention private forest owners biotic & abiotic damage - Canopy water/ dry matter Data latency: MEDIUM/LONG (associations). content - Canopy nitrogen uptake Spectral domain: VNIR-SWIR Private sector: - Crop phenology, Forestry Companies, - Equivalent water thickness - Leaf Mass Area - Fraction of Non- Photosynthetic Vegetation - Light use efficiency

Species occurrence and abundance

EU Biodiversity Strategy, Biodiversity & Local / regional nature Biodiversity monitoring - Species occurrence and Spatial resolution: HIGH conservation agencies in natural ecosystems abundance Natura 2000, conservation for National environmental - Identification & - Plant traits such as specific Spectral resolution: HIGH natural areas reporting agencies monitoring of biodiversity leaf area and leaf pigment Temporal resolution: EU regulation 1143/2014 Intergovernmental hotspots content MODERATE for monitoring and early Science-Policy Platform - Mapping & monitoring warning system for Invasive on Biodiversity and Essential Biodiversity Data latency: MEDIUM/LONG Alien Species, Ecosystem Services Variables (EBV) globally (IPBES) - National reporting for UN Environment Global UN Environment UNCBD Environment Outlook - Monitoring of Invasive Alien Species - National Biodiversity UN Convention on Strategies and Action Biodiversity (Aichi targets), Plans (NBSAPs) - National reports on Sustainable Development Biodiversity to the UNCBD Goal No. 15 - Agricultural supply chain certification report - Forest management certification by Forest Stewardship Council (FSC) - IPBES Regional and Global assessments

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1. Policy/Directives/ 2. Market Area 3. Stakeholders 4. User needs 5. EO Data products 6. Key observational Conventions/initiatives Requirements UN Convention to combat Environmental Agricultural consulting, Characterisation and Hydrocarbon spill, Spatial resolution: desertification (UNCCD), degradation and Rangeland management Monitoring of oil spills, Dry biomass abundance, HIGH/MODERATE hazards Concentration and extent of Clay mineralogy (type and Spectral resolution: HIGH Aarhus Convention, EU Joint Research Methane plumes, Solid extent, abundance), Temporal resolution: HIGH Centre Hydrocarbon contamination Heavy metal, Data latency: SHORT/MEDIUM UN / FAO / UNEP and (species and content), Toxic Oil and gas spills and related institutions, waste disposals (status and seeps;,Location of CH4, CO2 Temporal resolution as driver especially in context of extent e.g. for asbestos) the UNCCD (UN Wild fire fuel and load Trade-off analysis to be Convention to combat (Species type, dry biomass considered wrt. high spatial desertification), abundance, canopy leaf resolution vs. high radiometric Mining authorities and water) performance requirement. companies, Public Mineral dust sources health service (composition, particle size) authorities, Swelling soil hazards , (type, Environmental extent and degree), soil protection agencies pollution and Regional, National and contamination International authorities, Civil Protection, Copernicus EMS EU Water Framework Inland- and coastal Water Protection Water quality assessment Spectral water leaving radiance, Spatial resolution: Directive. waters Agencies, Water for e.g. inland and coastal inherent optical properties, HIGH/MODERATE EU Bathing Water authorities, Aquaculture water management, light penetration depth, Spectral resolution: HIGH Directive, EU Urban Waste industry, Dredging aquaculture practices, phytoplankton pigments (chl-a, Temporal resolution: Water Treatment Directive, industry, Recreation and fishery; PC, PE) and size class, HIGH/MODERATE EU Biodiversity Strategy, tourism sectors, Water pollution suspended matters (with their Natura 2000, United management for e.g. inorganic/organic fractions), Data latency: MEDIUM/LONG Nations Convention on harmful algae blooms; coloured dissolved organic Biological Diversity, United Ecosystem assessment in matter, bottom properties High SNR in the VNIR (plus Nations Sustainable terms of e.g. phytoplankton (substrate types, status and SWIR for extreme turbid waters, Development Goal No. 6 typing and phenology, depth), type and properties of emerging vegetation and floating Ramsar Convention on underwater light field; aquatic vegetation (submerged, materials, improving Wetlands Mass transport for e.g. floating and emergent atmospheric correction);

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1. Policy/Directives/ 2. Market Area 3. Stakeholders 4. User needs 5. EO Data products 6. Key observational Conventions/initiatives Requirements coastal erosion; vegetation), type and properties Coral reef assessment; of floating materials (e.g. oils, Understanding roles of cyanobacterial scum) lakes in the global carbon cycle EU Arctic Policy Snow, ice and EU External Action Environmental degradation Snow albedo Spatial resolution: MODERATE hydrology and hazards, Permafrost Accurate snow covered area Spectral resolution: HIGH degradation, Snow Meltwater ponds mapping. Temporal resolution: Snow grain size, light absorbing HIGH/MODERATE National, regional and Predicted amount and rate impurities (e.g. black carbon, Data latency: SHORT local water resource of release of water from dust), cryoconites, cryophilic Climate Change Initiative managers, hydropower snow/ice. Water availability algae. industry, tourism area, at catchment scale. Early avalanche warning warning for avalanche risk. authorities. Mitigation options to increase snow/ice water resource availability (reduction of black carbon and dust sources). Environmental degradation and hazards, Permafrost degradation, Snow cover

Table 4-1 Traceability matrix between the user needs and mission requirements.

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5 MISSION OBJECTIVES

Scientific and technological advances in hyperspectral imaging have been made in Europe since the late eighties, owing to the investments made mainly in the area of instruments (airborne and scientific spaceborne missions) and related data exploitation studies. An operational hyperspectral imaging mission in the context of the expansion of the Copernicus space component infrastructure could potentially be based on mid-size agile satellites, with specifications optimised for maximum synergy with existing missions, such as Sentinel-2. The specifications, e.g. number of spectral bands, spectral resolution, signal- to-noise ratio and the spatial resolution, can be selected over wide ranges in relation to specific applications and services, and would be compatible with science-oriented national projects such as the EnMAP and PRISMA missions benefitting from the cumulative experience of implementing these missions.

The Hyperspectral Imaging Mission shall provide detailed observations of key properties of terrestrial surface, inland water bodies and coastal regions. These properties can be assimilated in and analysed by various applications and services for an improved management of natural resources at the local to regional scale. The operational deliverables of the mission will be generated from an analysis of the remote sensing data with respective models. This will lead to robust, ecosystem-specific parameterisations and, therefore, a better quantification of application specific constituents.

In the area of mineral resources including soils and raw materials, only a hyperspectral imager with contiguous bands can provide the spectral signatures in the VNIR and SWIR needed to identify and separate observed minerals and chemical composition, as well as dry vegetation cover associated with litter or arid vegetation. This is also the case for identifying and monitoring of mining operations and mine-waste management activities. The high spectral resolution of hyperspectral imaging can provide information about featureless properties (such as in soils) by using the indirect relationship with feature properties and thus provide information none of the current (and future) multi- and superspectral orbital sensors can provide.

A hyperspectral imaging mission thus aims to provide precise spectroscopic measurements to derive quantitative surface characteristics supporting the monitoring, implementation and improvement of a range of policies in the domain of agriculture, food security, raw materials, soils, biodiversity, environmental degradation and hazards, inland and coastal waters, and forestry.

The prime objective of this mission is therefore to provide routine hyperspectral observations through the Copernicus Programme in support of EU- and related policies for the management of natural resources, assets and benefits. This unique visible-to- shortwave infrared spectroscopy based observational capability will in particular support new and enhanced services for food security, agriculture and raw materials.

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6 MISSION REQUIREMENTS

This chapter provides a quantitative description and justification of the geophysical requirements that would allow to fulfil the mission objectives and the associated observation requirements, including the level of priority, the rationale for their derivation - either explicitly or by reference to previous studies, experiences or publications, - in such a way that each observation requirement is traceable to the geophysical requirements and that the consequence of a possible partial compliance of the observing system with the observation requirement can be assessed.

The observational requirements of the mission are driven by the primary objectives i.e. agriculture, soils, food security and raw materials, and are based on experience, state-of- the art technology and results of previous hyperspectral airborne and experimental spaceborne systems. They were drafted by an international group of experts. These baseline observational requirements consider trade-offs and dependencies between parameters such as spectral resolution and radiometric performance. These high level requirements are:

6.1 Spatial coverage and geometry

In terms of spatial coverage, a global (all land masses) and continuous spatial coverage is required for a Copernicus mission. For consistency with Sentinel-2, global refers to all continental land surfaces (including inland waters) between latitudes 56° south and 84° north, all coastal waters up to 50 m depth from the shore, and all islands greater than 100 km2 or part of the EU.

MR-ID Applicability Value

L1B All land surfaces and inland water bodies between latitudes - 56° S and +84° N including islands greater than 100 km2, MR-010 coastal zones within 50 km distance from land and open water wherever the depth does not exceed 50 meters.

MR-ID Applicability Value

L1B The observation shall be nadir-looking with the field of view MR-020 (FOV) symmetrical with respect to the orbital plane.

MR-ID Applicability Value

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L1B The capability to provide measurements of the spectral MR-030 radiance over land surface areas whenever the Sun Zenith Angle (SZA) is lower than 84°.

6.2 Observation Time

For the observation (overpass) time a mean local solar time around 10:30 is chosen for compatibility with Sentinel-2. This time is a good compromise between illumination conditions (better towards noon), cloud coverage (higher towards noon), and angular effects (higher for low sun elevation angles in the morning).

MR-ID Applicability Value

L1B The time of observation shall be between 10:30 and 11:30 MR-040 hours LTDN

6.3 Timeliness

Concerning timeliness (latency), for consistency with Sentinel-2, the delivery of products up to Level 1C is required within 5 hours for the disaster monitoring service and 12 hours for all other operational services (TBC).

MR-ID Applicability Value

L1C Radiometry Calibrated and Geolocated Level-1c data and MR-050 selected Level-2 products shall be made available to users within 5 hours (G)/ 12 hours(T) after sensor acquisition.

6.4 Revisit Time

In terms of temporal resolution (revisit time) observations are needed every 10 to 12.5 days in line with the constraints as imposed by the temporal evolution of agricultural vegetation. Optimisation of the CHIME orbit is desired for synergistic acquisition with the Sentinel-2 constellation.

MR-ID Applicability Value

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L1B Geometrical coverage shall be achieved at the equator after 10 MR-060 days (G)/12.5 days (T).

6.5 Spatial Requirements For spatial resolution, a range between 20 m – 30 m, is needed, optimized for intra-field variability for European and African field sizes. The L1C products should be resampled to the Sentinel-2 grid of 20m (utilising the same global reference image and cartographic projection).

MR-ID Applicability Value

L1B The Spatial Sampling Distance (SSD) shall be 20 m (G)/30 m MR-070 (T), optimised for intra-field variability for European and African field sizes.

Note: The L1C products should be resampled to the Sentinel-2 grid (utilising the same global reference image and cartographic projection).

MR-ID Applicability Value

L1B The along- and across-track SSD shall be identical to within 5% MR-080 in all spectral channels.

MR-ID Applicability Value

L1B The Modulation Transfer Function (MTF) in along- and across- MR-085 track direction shall be MTF > 0.2 (T) and MTF > 0.3 (G) for all spectral channels at the Nyquist frequency.

MR-ID Applicability Value

L1B The spatial co-registration of all spectral channels shall be MR-086 smaller than 0.1 (T) and 0.05 (G) SSD.

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6.6 Spectral Requirements From the list of core applications and the associated high-level observational requirements, the ideal mission configuration (understood as the one fulfilling the bulk of core applications in (Table 3-1) would measure in the 400-2500 nm spectral range with a continuous spectral sampling ≥10 nm.

MR-ID Applicability Value

L1B The mission shall provide contiguous spectral coverage MR-090 between 400 and 2500 nm.

MR-ID Applicability Value

L1B The Spectral Sampling Interval (SSI) shall be less or equal 10 MR-100 nm, driven by the raw materials and agricultural applications.

MR-ID Applicability Value

L1B The spectral resolution defined as Full-Width at Half MR-110 Maximum (FWHM) of the Instrument Spectral Response Function (ISRF) shall be less or equal 10 nm.

MR-ID Applicability Value

L1B The spectral co-registration of all spectral channels shall be MR-120 smaller than 0.1 (T) and 0.05 (G) SSI.

MR-ID Applicability Value

L1B The centre wavelengths for each spectral channel shall be MR-125 determined and known with an uncertainty <0.5 nm over the specified satellite in-orbit lifetime.

6.7 Radiometric Requirements The radiometric requirements are applicable to a Level 1 product expressed as Top of Atmosphere (TOA) radiance (W.m-2.sr-1.µm-1) with all the radiometric and spectral calibration applied. For this the product shall show a linear response between given minimum and maximum top-of-atmosphere radiance levels (Lmin and Lmax, respectively). These are defined so that it is guaranteed that the whole span of possible radiometric signals from the Earth surface is

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covered, as required by a mission intended to sample a wide range of application domains. This is ensured by Lmin (Lmax) radiance levels defined as the equivalents to a spectrally- constant surface albedo of 3% (100%) and a Solar Zenith Angle (SZA) of 60º (10º). The Noise Equivalent Delta Radiance (NEdL) is the standard deviation of a time series of radiance measurements at a reference radiance level Lref (Reflectance = 20%, 30º SZA) for a specific spectral channel. The required NEdL for a land surface target at Lref_L is provided in Table 6-1 and plotted together with the Lmin and Lmax values in Figure 6-1.

For water observation a much lower Lref_W (Water) radiance exists and the corresponding SNR_W at Lref_W (calculated as SNR=Lref/NEdL) can be depicted from Table 6-2. The requirement is making use of the Sentinel-3 OLCI spectral band definition. For which a spectral binning up to the spectral bands defined and spatial binning can be applied in order to achieve the required SNR_W.

Figure 6-1 CHIME radiometric performance – reference radiance values (Lmin, Lref and Lmax ) and NEDL

MR-ID Applicability Value

L1B The noise equivalent delta radiance (NEdL) shall be lower than MR-140 the values specified in Table 6.1 for Earth radiance measurements at Lref_L.

wavelength (nm) NEdL (W/sr/m2/micron) 400 ≤ λ ≤ 430 0.4 430 ≤ λ ≤ 500 0.4 – (λ-430) 0.12/70 500 ≤ λ ≤600 0.28 – (λ-500) 0.07/100 600 ≤ λ ≤ 830 0.21 – (λ-600) 0.02/230 830 ≤ λ ≤ 890 0.19 – (λ-830) 0.02/60

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890 ≤ λ ≤ 940 0.17 – (λ-890) 0.03/50 940 ≤ λ ≤ 1130 0.14 – (λ-940) 0.04/190 1130 ≤ λ ≤ 1280 0.1 – (λ-1130) 0.015/150 1280 ≤ λ ≤1330 0.085 – (λ-1280) 0.015/50 1330 ≤ λ ≤ 1480 best effort 1480 ≤ λ ≤ 1520 0.05 +(λ-1480) 0.015/40 0.065 – (λ-1520) 1520 ≤ λ ≤ 1760 0.015/240 1760 ≤ λ ≤ 1950 best effort 1950 ≤ λ ≤ 2050 0.05 – (λ-1950) 0.015/100 0.035 – (λ-2050) 2050 ≤ λ ≤ 2450 0.007/400 2450 ≤ λ ≤ 2500 best effort

Table 6-1: Noise-equivalent radiance requirement at the Lref_L (Land) radiance scene.

MR-ID Applicability Value

L1B For water observation, i.e., Earth radiance measurements at MR-150 Lref_W, the Signal-to-Noise (SNR_W) shall be higher than the values specified in Table 6.2.

SNR_W after Wavelength Width L ref_W spectral/spatial (nm) (nm) (W/sr/m2/micron) binning 400 15 62.95 2188 412.5 10 74.14 2061 442.5 10 65.61 1811 490 10 51.21 1541 510 10 44.39 1488 560 10 31.49 1280 753.5 10 10.33 605 778.75 15 9.18 812

Table 6-2: Water observation requirement using Sentinel-3 OLCI spectral band definition. The SNR_W requirement is applicable at the Lref_W (Water) radiance level.

MR-ID Applicability Value

L1B The absolute radiometric accuracy of the measured spectral radiance at Lref L shall be better than 2% (G)/5%(T) (TBC) MR-160 traceable to International standards. Note: Within the spectral ranges 1760nm to 1950nm and 2450nm to 2500nm this requirement is on a best-effort basis.

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MR-ID Applicability Value

L1B The relative spatial radiometric accuracy shall be better than 1% (TBC). MR-170 Note: Within the spectral ranges 1760nm to 1950nm and 2450nm to 2500nm this requirement is on a best-effort basis.

MR-ID Applicability Value

L1B The relative spectral radiometric accuracy shall be smaller than 1% (T) and 0.2% (G). MR-180 Note: Within the spectral ranges 1760nm to 1950nm and 2450nm to 2500nm this requirement is on a best-effort basis.

MR-ID Applicability Value

L0 The maximum polarization sensitivity shall be less than 1% (G) MR-190 and 5% (T) for the entire spectral range from 400 nm to 2500 nm.

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7 CALIBRATION REQUIREMENTS

7.1 Radiometric calibration The requirements for very high radiometric accuracy as well as the adherence to traceability to international standards will impose a high level of attention to absolute radiometric calibration.

Pre-flight calibration to fully characterise the instrument to a high level of accuracy to ensure fidelity to the radiometric requirements is essential. Comprehensive radiance calibration under controlled laboratory conditions ensuring traceability to national / international standards is mandatory. In addition, a full characterisation of all on-board calibration facilities (e.g. solar diffuser BRDF, radiometric & spectral characterisation of on-board integrating spheres, focal plan LEDs, etc.) as well as a characterisation of the integrated instrument for straylight and ghosting is necessary.

On-board absolute radiometric calibration is essential. For absolute calibration, sun calibration with calibrated space-grade diffusers at set regular intervals is accepted good practices. It may be beneficial to consider the adoption of a second diffuser to track the degradation of the primary diffuser. In addition, the adoption of lunar calibration should be strongly considered especially as the unknowns such as the absolute scale of the lunar model, etc. may be resolved by the time of CHIME’s launch. The frequency of these on- board measurements is also critical, as many are life-limited items (e.g. degradation of lamps and solar diffuser), and may range from ~2 weeks intervals for primary solar diffuser measurements to ~3 months intervals for secondary diffuser measurements.

Vicarious radiometric calibration is now accepted good practices supporting the on-board calibration. Vicarious calibration allows cross validation of on-board calibration as well as ensuring continuity in the event of on-board calibration failure and is essential for harmonisation between multiple missions (especially cross-calibration within the different optical sensors of the various Sentinels and contributing missions). Routine acquisition of data over automated radiometric calibration sites such as CEOS Radiometric Calibration Network (RadCalNet) is highly recommended together with automated assessments/ analysis of data incorporated in the processing system/chain.

When/where available, the incorporation of radiometric calibration mission data such as the ones from the planned TRUTHS (Traceable Radiometry Underpinning Terrestrial- and Helio- Studies) mission into the radiometric calibration assessment will be considered.

7.2 Spectral Calibration Spectral fidelity is especially important for this mission where the essential properties/variables are heavily reliant on the spectral accuracy of the data and the high spectral accuracy required. Comprehensive pre-flight calibration under controlled laboratory condition to fully characterise sensor characteristics such as spectral co- registration, actual spectral band position, out-of-band signal, etc. are essential.

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Routine on-board spectral calibration is essential. Some options for on-board spectral calibration to be considered are the use of on-board spectral calibration using multi- component rare earth doped calibration plate, a doped integrating sphere or spectral calibration lamps/LEDs. Also the usage of the atmospheric/solar Fraunhofer lines using Earth Observations in full spectral resolution can be regarded as a valid option. The frequency of calibrations depends on the stability of the instrument, and can be assumed to be required approximately at 3 months intervals.

7.3 Dark Current Measurements Dark current measurements obtained e.g. by closure of the entrance shutter mechanism of the instrument and viewing into deep space can be required as part of the calibration methodology. The closure of the shutter mechanism may be achieved at more frequent time intervals while the deep space may be collected with less frequency to monitor the results from the closure of the shutter mechanism.

7.4 Geometric Calibration As with the radiometric and spectral properties of the instrument, a through laboratory characterization of the instrument is mandatory to characterise the spatial mis- registration, and to derive an instrument pointing vector model (IPVM) on spacecraft level (incl. star tracker). An initial validated assumption about in-flight Thermo-Elastic Model (TEM) quaternions is essential to allow a solid geometric calibration in-flight.

During the commissioning phase, the IPVM and TEM models needs to be verified and if necessary adjusted. For this, e.g., Level 1 products are matched with a Ground Control Point Database. As outcome of this matching the L1 geolocation accuracy incl. any TEM quaternions and/or IPVM variations with its statistics will be reported.

7.5 Instrument Monitoring and Data Quality Control Within the ground segment, also subsystems for the continuous monitoring of the CHIME based on housekeeping data (incl. temperatures, voltages, dark current statistics) as well the monitoring of the CHIME and pre-processors based on Earth data take statistics are essential. Thus, only the quality of the generated data and the correct functioning of the instrument can be monitored, allowing to analyse and potentially trigger additional activities (e.g., calibration, root cause investigations), whenever anomalies are occurring.

7.6 Vicarious Calibration It is considered as good practice for a mission to complement its performance validation with vicarious calibration and validation campaigns. This may be via collaboration with other institutions or via wider groups such as CEOS WGCV. In addition, the collection of data across CEOS WGCV Pseudo-invariant Calibration Sites (PICS) may be considered as an added monitoring/verification of the performance of the sensor. Alternatively, radiative

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transfer based forward modelling supports performance validation across extended time scales, complementing in-situ instantaneous efforts. Again, this is more appropriate for monitoring the trends. New procedures to use artificial targets for vicarious calibration as executed from airborne hyperspectral imaging systems (Brook and Ben-Dor 2015) may also be adopted.

8 CAMPAIGNS

This chapter provides the justification and description of related campaign activities in view of the observational requirements described in Chapter 6 and the applications and user requirements presented in Chapters 3 and 4.

In order to verify that the instrument being considered for the mission is capable of providing the remotely sensed information with required accuracy, real data, measurements and images acquired by airborne sensors, ground-based equipment and/or laboratory instruments are required.

Furthermore, acquired data are instrumental to find answers to important questions related to technical parameters of the new mission that satisfy user requirements such as choice of orbit, wavelength, spatial resolution, spectral resolution, temporal resolution and sensitivity. They also provide feedback on key issues related to product quality of the spaceborne mission.

For any potential campaign activity it is crucial to make use of instruments that are comparable to the later spaceborne instrument. In addition, any activity should make use of existing spaceborne data not only to study mission performance but also examine trade- off (e.g. Sentinel-2) and synergies (e.g. PRISMA and EnMAP) for existing and planned scientific missions.

It should also be noted that in many applications the end user can only be satisfied with higher than Level-2 products, whereby the Level-2 product is merged (assimilated) with other, non-satellite data. Campaigns have proven to be the ideal vehicle for involving the end-user community in the early development of these assimilation schemes.

The basic setup for any campaign activity would focus on collecting airborne hyperspectral data over representative monitoring sites, concurrent with ground-based measurements for time intervals compatible with the spaceborne mission over a longer period (e.g. 2 - 3 months) in order to sample variability in space (including atmosphere, vegetation and soils) and time. In order to study trade-off and synergies one should allow for oversampling during so called Intensive Observation Periods (IOP) to simulate temporal and spatial sampling with repeated over flights to the extent possible.

In such a way a potential campaign activity would directly support the trade-off analysis related to the geometrical/co-registration requirements: • GSD between 20 to 30m (as part of a European field size trade-off) • Spatial uniformity (between 5 and 20%)

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In addition the following key analyses would be supported: • Confirm the spectral sampling requirements (10 nm at FWHM) for the target applications and related products, incl. support to product quality specification • Elaborate efficient raw data cloud-screening algorithms • Generation of test data sets for future ingestion in an end-to-end simulator • Support evaluation of smile/keystone correction • Simulate temporal sampling with repeat overflights to extend where possible

In order to properly cover the above-mentioned analyses, a strong collaboration is needed with different communities that build on the experience from already planned activities in the context of mission development and calibration/validation activities for existing (e.g. Sentinel-2, Sentinel-3) and future missions (e.g. FLEX).

The overall aim of such an activity focusing on the end-user community would be to start building a sound transdisciplinary data base, with a creative approach towards the alignment of national programmes and the input of multiple actors and stakeholders. In such a way it is ensured that the different application pillars of the hyperspectral imaging mission in Chapters 3 and 4 are properly acknowledged during Phase A/B1 of the mission by also allowing further analyses and exploitation during later mission implementation.

9 PRELIMINARY SYSTEM CONCEPT(S)

The analysis of requirements and identification of a preliminary system concept is an activity performed by the CHIME Phase A/B1 contract to be kicked off in early 2018.

As a result of CHIME Phase A, a parametric analysis identifying the critical parameters and defining a set of performance requirements corresponding to the optimum in cost vs. benefits for a number of to be proposed mission concepts will be provided. As part of the contract, a variety of concepts covering a range of mission implementations going from a lower to higher implementation complexity will be evaluated. High-level preliminary implementations, which could be foreseen include concepts based either on a single instrument module design, or a combination of modules on a single satellite or a small constellation of identical satellites. In the last case, a deployment strategy will also be proposed.

With the technological advances of large FOV optics and large format 2D detector arrays, an imaging spectrometer based on push-broom acquisition is a good candidate to fulfil most of the observation requirements. These type of instruments have been implemented by ESA in the past with CHRIS on the PROBA-1 as technology demonstrator (in 2001), MERIS on-board ENVISAT (in 2002) and OLCI on-board Sentinel-3 (in 2016). All those instruments are imaging grating spectrometers acquiring spectrally resolved images of the Earth surface in the VIS/NIR spectral range.

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Compared to CHRIS/MERIS/OLCI imaging spectrometers, an increase of the spectral range and the Ground Sampling Distance (GSD) is necessary to comply with CHIME observation requirements.

In order to enlarge the spectral range, different gratings and detectors have to be considered. To widen the FOV, MERIS and OLCI were designed with fan-shaped arrangement of 5 dedicated imaging spectrometers. A similar instrument architecture could be envisioned to comply with a high the revisit time requirement. As part of Phase A/B1 activities– besides many other concepts - those design solutions were analysed.

10 DATA PRODUCTS, USAGE AND ACCESS

10.1 Core Data Products As far as information products are concerned, considering that many of the applications exploiting hyperspectral data rely on surface reflectance spectra, Level-2A products (geometrically corrected surface reflectance) are requested as the main data product to be distributed to users by the mission core ground segment. The main features of the Level-2A product are: - Bottom-of-Atmosphere (BOA) reflectance pixel-level information. - Ortho-rectified geometry including the usage of a Digital Elevation Model (DEM); - Appended pixel classification (side product from the atmospheric correction process) allowing to distinguish opaque clouds, thin clouds, cloud shadows, vegetation, etc. Additionally, access to Level-1B and Level-1C products are required to give the possibility to advanced users to perform their own atmospheric correction. Level-1B products are Top-of-Atmosphere (TOA) radiance products. The Level-1B product consists of quality controlled Level-1A data that have been reformatted but not resampled in segments. Pixel quantity expressed as Top of Atmosphere (TOA) radiance (W.m-2.sr- 1.µm-1) with all the radiometric and spectral calibration applied. Geometric information (e.g. refined viewing model) computed, appended but not applied. Level-1C products are Top-of-Atmosphere (TOA) reflectance products in ortho-rectified geometry (same cartographic projection as Level-2A products). The TOA reflectance images are resampled in cartographic reference frame UTM/WGS84 and framed tiles. Level 1c products are geometrically refined including geolocation correction from any residual bias and thermo-elastic distortion (intra-orbit and seasonal) and multi-temporal registration. 10.2 Higher Level Data Products Complementing the core products above, a set of higher-level products is proposed to users as part of the mission catalogue or as service offer from the EO value adding industry. The downstream industry will thus have the chance to market high level and user-oriented

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products based on the mission’s core products. For this business development it is important that well-defined core products will be available as free service at the very beginning of the mission that a competitive market can evolve.

A set of example – higher-level products is listed below by application domain. Priority products are marked with an asterisk.

Sustainable Agriculture and Food Security: • Quantification on non-photosynthetic vegetation / biomass* • Canopy water content* • Leaf and canopy pigment content (Chlorophyll*, Carotenoids, Anthocyanins, separately) • Canopy nitrogen content* (uptake) • Yield quality (protein content* of grains/cobs, energy content, fodder quality) • Crop development stages / phenology • Specific Leaf Area (SLA) • Micronutrients (phosphorus, potassium, sulphur)

• Soil organic carbon content* • Soil textural and structural composition* (e.g clay, silt, sand, iron oxides, gypsum and carbonate contents) • Soil Quality Index for agriculture practices • Top Soil Moisture content

Raw Materials: • Composition and abundance of non renewable (mainly mineral) raw materials: ferric oxide content and hematite-goethite ratio* covering goethite*, hematite*; kaolin content, composition and crystallinity* covering kaolinite*, dickite*, hallyosite*, nacrite*; white mica composition, content and crystallinity* covering illite*, muscovite*, paragonite*, brammalite*, phengite*, lepidolite*, margarite*; other minerals detectable are smectite*, alunite, nontronite, hectorite, calcite, dolomite*, topaz, pyrophyllite, saponite, chlorite, epidote, biotite, talc, amphibole, antigorite, phlogopite, gypsum, opaline silica, etc.,

• Non renewable resources raw material surface contamination and atmospheric deposition: eg. acid mine drainage secondary minerals (goethite*, hematite*, ferrihydrite, jarosite*, schwertmanite) and derived product pH, mineral dust distribution and levels (eg. Iron oxide, gypsum, etc.)

• Composition and abundance of renewable raw materials and related environmental impacts such as dust: lignin/cellulose

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• Biological variables related to sustainable agriculture and food security are directly applicable for establishing environmental baselines pre-mining, monitoring of progress of rehabilitation towards mine closure objectives, and, monitoring vegetation damage

Additional Application Products: Inland and coastal waters: • Phytoplankton pigments: e.g. chlorophyll-a, phycocyanin, phycoerythrin (which also means HABs*, which provide trophic status, support the water quality managers) • Total suspended matter with its organic and inorganic fractions (as e.g. a tracer of mass transport, coastal erosion) • Submerged habitats classification (e.g. of corals, macrophytes, seagrass, in support to ecosystems assessment in fragile environment)

Biodiversity: • Physiological Traits* (Chlorophyll Content / Protein / Nitrogen, Water Content, Dry Matter Content, Pigments (Xanthophyll (Carotenoids), Anthocyanin, etc.), Cellulose, Lignin, Polyphenols, Taninns) • Morphological Traits (Specific Leaf Area, Leaf Area Index (LAI)) • Functional Traits (GPP) • Species composition (Species richness, Species abundance/distribution) • Higher level products (Land Surface Phenology, Trait Fragmentation)

Snow, ice and hydrology: • Snow grain size, albedo (water resources) • Light absorbing impurities* (impact on melting and water release)

Environmental degradation and hazards: • Invasive species, oil spill area and volume, wild fire fuels*, surface contaminants, minerals related to landslides, large gas leaks, etc.

10.3 Contribution to EU Policies and Copernicus Services In this section only the high priority products are addressed as part of the agriculture policies (CAP), the EU Raw Materials Initiative and EU soil thematic strategy.

Relevant applications for policies in the field of Sustainable Agriculture and Food Security are: • Yield Assessment and Forecast • Species identification (e.g. crop type, invasive species, palatable/non palatable)

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• Crop health and damage (water and nutrient stress) • Detection of weeds • Crop water requirement • Mapping soil properties: structure and texture • Estimation of carbon storage in soils • Soil Erosion and degradation mapping • Detection of soil pollution and soil contamination

Relevant applications for policies in the field of Raw Materials are: • Mineral mapping for discovery of raw materials for informed exploration • Mineral compositions, chemistries and abundances for raw materials, characterisation and quantification in mining/extraction • Monitoring and characterisation of surface contamination, atmospheric pollution, vegetation damage for active mining activities, mine dumps and legacy mines • Establishing environmental baselines pre-mining, monitoring of progress of rehabilitation towards mine closure objectives

For biodiversity research, remotely sensed observations (remote sensing enabled EBV’s) are complemented with in-situ data or other available date and integrated in EBV classes. EBVs inform either biodiversity indicators, biodiversity monitoring (Navarro, Fernández et al. 2017), or ecosystem services (Braun, Damm et al. 2017) for policy impact (Krug, Schaepman et al. 2017).

The mission shall provide access to Level-2A, Level-1C and Level-1B products. MR-200 Additionally the mission can optionally provide a set of downstream products related to the different mission applications.

Considering the evolution of the ground segment towards cloud-based solutions where algorithms are brought together with the products, it is requested to make the mission products available through a cloud-based IT platform. The cloud environment shall provide the tools to perform basic and advanced processing of the mission core products. The cloud platform shall also allow the testing of different atmospheric correction approaches or geophysical parameter retrieval algorithms provided by users. It is also requested to have a platform allowing the inter-operability with other missions including Sentinel-2, Sentinel-3, FLEX, EnMAP, PRISMA, SHALOM and EMIT.

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Data access should be provided through the Copernicus Data and MR-210 Information Access System (DIAS). User interfaces and processing tools (e.g. API’s) need to be developed and provided through the system.

Furthermore, High Level Data Products in the field of agriculture and food security can have an important impact in the Copernicus Land Monitoring Service (CLMS) for the 3 different components (global, European and local). Also, the Additional Application products can support the Land service and, in particular the coastal water products can be of interest for the coastal zone monitoring as the interface between the Land Monitoring Service and the Copernicus Marine Environment Monitoring Service (CMEMS).

11 SYNERGIES AND INTERNATIONAL CONTEXT

In terms of potential for synergies with other missions and especially with Sentinel-2, a hyperspectral mission would be complementary to Sentinel-2 for many fields of applications, and in particular for vegetation-related and land cover mapping applications for which Sentinel-2 is optimised. Synergies with Sentinel-2 could help slightly relax the requirements for high temporal resolution in the case of e.g. agriculture and in-land water applications, and the spatial sharpening of hyperspectral images with Sentinel-2 data could also help some of the applications requiring a high spatial resolution. The CHIME mission will have to take full account of the achievements made by the national missions EnMAP (DE) and PRISMA (IT) and ensure exploitation of potential synergies with concurrent orbiting missions as well as airborne sub-orbital missions, such as APEX (Schaepman, Jehle et al. 2015), in order to profit from a rich and well developed legacy of existing expertise.

In terms of supporting priority missions within the Copernicus context, a hyperspectral imaging mission will provide a reliable reference for soil organic carbon (SOC), since agricultural soils are among the planet's largest reservoirs of carbon and hold potential for expanded carbon sequestration, thus providing a prospective way of mitigating the increasing atmospheric concentration of carbon dioxide. Since SOC is highly dynamic there is a need to know how much SOC is stored in the Earth’s soils and monitor changes around the world, especially in vulnerable hotspots. The quantity and quality of SOC is typically difficult to map and in many cases cannot be observed by current multispectral sensor technologies. Also for a Copernicus Thermal Infrared (TIR) imaging mission CHIME would, through its diagnostic power, provide valuable input for separating temperature from emissivity. TIR data yields strong synergistic potential with optical hyperspectral data for a number of application domains. These include the monitoring of vegetation functioning (the photosynthetic potential characterised by hyperspectral data can be complemented by evapotranspiration derived from TIR measurements), natural hazards (wildfires and volcanic eruptions) and mineralogical composition mapping (better mineral discrimination

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potential by combination optical spectroscopy and emissivity at several wavelengths) including soil organic carbon, soil mineralogy, and raw material identification (non-OH bearing silicates). Such a synergy of co-located optical hyperspectral and TIR observations is the basis of the HyspIRI mission concept currently under development by NASA. CHIME would also strongly benefit from co-located TIR data derived from a potential Copernicus TIR mission.

In addition, a hyperspectral mission, such as described here will provide the necessary reference and baseline information for ensuring high quality interoperability of optical sensors not only of the Sentinel family, such as Sentinel-2, but also the missions of international partners, e.g. Landsat-8, for instance to support quantitative analysis ready data (ARD) in the Data Cube development. It would additionally provide a reference standard for high resolution multi-spectral missions, without on-board calibration, such as Urthecast daily in terms of calibration, data validation and atmospheric data correction.

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