Copernicus Global Land Operations – Lot 2 Date Issued: 18.10.2018 Issue: I1.10

Copernicus Global Land Operations “Cryosphere and Water” ”CGLOPS-2” Framework Service Contract N° 199496 (JRC)

ALGORITHM THEORETHICAL BASIS DOCUMENT

WATER BODIES

PROBA-V 300M

VERSION 1

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Organization name of lead contractor for this deliverable: VITO

Book Captain: I. Reusen (VITO) Contributing Authors: L. Bertels (VITO) D. Wolfs (VITO) Copernicus Global Land Operations – Lot 2 Date Issued: 18.10.2018

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Dissemination Level PU Public X PP Restricted to other programme participants (including the Commission Services) RE Restricted to a group specified by the consortium (including the Commission Services) CO Confidential, only for members of the consortium (including the Commission Services)

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Document Release Sheet

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Change Record

Issue/Rev Date Page(s) Description of Change Release

06.09.2017 All First issue I1.00

I1.00 01.08.2018 All Revision after review meeting I1.10

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TABLE OF CONTENTS

1 Background of the document ...... 12

1.1 Executive Summary ...... 12

1.2 Scope and Objectives...... 12

1.3 Content of the document...... 12

1.4 Related documents ...... 12 1.4.1 Applicable documents ...... 12 1.4.2 Input ...... 12 1.4.3 Output ...... 13 1.4.4 External Documents ...... 13

2 Review of Users Requirements ...... 14

3 Methodology Description ...... 16

3.1 Overview ...... 16

3.2 The retrieval Algorithm ...... 17 3.2.1 Outline ...... 17 3.2.2 Basic underlying assumptions ...... 19 3.2.3 Related and previous applications ...... 19 3.2.4 Alternative methodologies currently in use ...... 21 3.2.5 Input data...... 21 3.2.6 Output product ...... 25 3.2.7 Methodology...... 26 3.2.8 Limitations ...... 51 3.3 Quality Assessment ...... 52

3.4 Risk of failure and Mitigation measures ...... 52

4 References ...... 53

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List of Figures Figure 1: General overview of the Water Bodies Detection Algorithm...... 16 Figure 2: Outline of the Water Bodies Detection Algorithm...... 18 Figure 3: Location in Northern South-Sudan (10°0’N, 31°80’E). a) False water bodies were detected because of dark soils. b) By applying the extra check on the MWEM most of the commission errors could be prevented...... 20 Figure 4: Location in Southern Spain (38°40N, 7°4’W). a) Due to the strict Water Bodies Potential Mask parts of the lake were not detected as water. b) The MWEM overrules the WBPM and as such reduces the omission errors...... 21 Figure 5: a) This Google Earth image shows an area over the Alps (Upper left corner: 48°08’25” N, 5°40’0” E). b) The permanent glacier mask for the same area in (a)...... 23 Figure 6: a) Google Earth image showing part of northern Ethiopia and southern Eritrea with the dark volcanic soils manually delineated (red polygons). b) Dekad MC10_20140521 for the same area with the derived dark volcanic soils shape file overlaid. c) The final volcanic soil mask (white=volcanic soil) obtained after rasterizing the shape file...... 24 Figure 7: a) The high resolution Maximum Water Extent product of JRC’s Global Surface Water dataset shows where in the last 32 years water was ever detected (blue colored). b) The PROBA-V 300 m resolution Maximum Water Extent Mask is derived from the Maximum Water Extent product of JRC’s Global Surface Water dataset. Both images have the PROBA-V 300 m raster overlaid (red). The shown water reservoir is located in India (25°25’N, 77°55’E)...... 25 Figure 8: MC10 algorithm flow...... 28 Figure 9: Decision tree for MC10 observation type classification...... 29 Figure 10: a) Part of the 90 m spatial resolution GLSDEM over Rift Valley in Ethiopia. b) The detected lowest points for this area are colored yellow. The larger sized colored areas are areas of equally low elevation which correspond to some smaller lakes in the region...... 31 Figure 11: Expanding the initially detected lowest point by systematically rising an imaginary water level in steps of 1 m. The corresponding 90 m spatial resolution pixels are indicated by the dots at the bottom...... 32 Figure 12: a) Part of the GLSDEM over Rift Valley. b) The detected potential WBs for this area. The detailed window shows a potential WB in the center (crossing of red lines). The horizontal and vertical profiles are according to the red lines over the images. On the horizontal profile, the wetland (1) near lake Hora (2) and the potential water body (indicated by the long red arrow) are seen. On the vertical profile part of the wetland (1’) near lake Hora (2), the potential water body (indicated by the long red arrow) and lake Bishoftu (3) are seen...... 33 Figure 13: a) Part of image MC10_20131021 taken over the Rift Valley in Ethiopia (SWIR, NIR and Red bands assigned to the RGB channels resp.). b) The WBPM for the same area in (a)

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(white=potential water body, black=no water body possible). The plot shows the vertical GLSDEM profile according to the red line. Marked with the blue boxes are the seven potential WBs. The arrows indicate the location of lake Abijato (1) and lake Langano (2)...... 35 Figure 14: The HSV color space...... 36 Figure 15: The first part of the WBDA comprises invalid data filtering...... 37 Figure 16: The removal of cloud shadow by dilatation of the cloud indicated pixels. a) Dekad MC10_20140511 over Argentina (center at 31°33’50”S, 65°10’37”W). b) The Water Bodies detection result without delate filtering. Due to cloud shadows lots of false WB detections appear. c) After de clouds are dilated by its circular neighborhood of twelve pixels most of the false detections are masked by the dilated cloud. The red oval shows a false detection which could not be reached by the dilated cloud. d) Dekad MC10_20140511 over Argentina (center at 31°45’58”S, 65°4’44”W). e) An existing small lake is shown in the lower left part of the image, besides some false detections can be seen near to the clouds. No cloud dilatation was done yet. f) After cloud dilatation the false detection don’t appear, a small part of the lake is now obstructed by the dilated cloud as indicated by the red oval...... 38 Figure 17: The second part of the WBDA comprises Incompatibility Masking...... 39 Figure 18: The last part of the WBDA comprises actual Water Body Detection based on threshold levels on the HUE and VALUE bands. The thresholds consist of two parabolic functions...... 40 Figure 19: 2D scatter plot of the Vietnam test area showing the final threshold levels. Land pixels are indicated by the dark grey dots, reference WBs (obtained from Landsat 8) are indicated by the colored plus symbols for different ranges of water surface ratios. A further refinement of the thresholds is not possible because WB pixels and land pixels are intermixed...... 43 Figure 20: Location of the Landsat scenes used to define the detection method...... 44 Figure 21: Workflow for the extraction of reference water bodies using Landsat-8 scenes. An as much as possible cloud free scene is searched for the required geographical location and time period. After pre-processing and HSV color transform of the Landsat-8 scene, WB detection was done using a Decision Tree Classifier (DTC). The obtained Landsat-8 WBs are subsequently used to calculate the Water Surface Ratio using the spatial information from the PROBA-V image...... 45 Figure 22: Decision Tree Classifier for WB detection on Landsat-8 images. A check on different bands and on cloud cover removes incompatible pixels from the scene. A check on HUE and VALUE is used to detect water bodies...... 46 Figure 23: 2D scatter plot of the WB frequency (WBf) and the number of continuous temporal WB observations (mctWBs) for the Rift Valley test area calculated for the 31 available dekads. The Water Body Occurrence levels are indicated by the different colored areas...... 51

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List of Tables Table 1: GCOS requirements for areas of water bodies as Lakes and Land cover Essential Climate Variables (GCOS-200, 2016) ...... 15 Table 2: Thresholds for water body detection are applied on the HUE and VALUE, which are obtained after HSV color transformation of the SWIR, NIR and Red bands, on the NDVI and on the VALUE as an additional check besides the NDVI threshold check (NV)...... 19 Table 3: Spectral characteristics of the PROBA-V bands ...... 22 Table 4: Status Map bit mapping of the PROBA-V data ...... 22 Table 5: Output product legend...... 26 Table 6: Output QUAL product legend. The colored entries are the occurrence values...... 26 Table 7: Status Map (SM) bit mapping...... 30 Table 8: Landsat-8 scenes used to define the PROBA-V water bodies detection thresholds...... 44 Table 9: The results for eight different settings of the threshold levels. The overall accuracy (OA), omission errors (OE) and the commission errors (CE) are listed in percentage. The OEs are given for six ranges of minimum WSR . The figures are calculated over the 22 test regions for the period Oct2013 – May2014. The green indicated entries show the best results and are the final threshold levels...... 48 Table 10: Omission and commission errors are caused by several reasons...... 52

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List of Acronyms

ACCAm Automatic Cloud Cover Assessment - modified ATBD Algorithm Theoretical Basis Document DEM Digital Elevation Model DTC Decision Tree Classifier ECV Essential Climate Variables ESA European Space Agency GCOS Global Climate Observing System GIO GMES Initial Operations GL Global Land GLIMS Global Land Ice Measurements from Space GLSDEM Global Land Survey Digital Elevation Model GMES Global Monitoring for Environment and Security GSW Global Surface Water GWW Global Water Watch HSV Hue, Saturation and Value colour system IFOV Instantaneous Field Of View LC Land Cover MC Mean Compositing MC10 10-day mean compositing synthesis NDVI Normalized Difference Vegetation Index MWE-GSW Maximum Water Extent product of JRC’s Global Surface Water MWEM Maximum Water Extent Mask NIR Near Infrared NMOD Number of valid Observations used in 10-daily compositing period NOBS Number of satellite overpasses within the 10-daily compositing period NSIDC National Snow and Ice Data Centre PGM Permanent Glaciers Mask PROBA-V Vegetation instrument on board of PROBA satellite QUAL Quality Layer RGB Red, Green and Blue colour system S1-TOC The PROBA-V daily Top-Of-Canopy synthesis products SM Status Map SMAC Simplified Model for Atmospheric Correction SPOT Satellite Pour l’Observation de la Terre SRTM Shuttle Radar Topography Mission SVP Service Validation Plan SWIR Short Wavelength Infrared SZA Solar Zenith Angle TOA Top of Atmosphere TOC Top of Canopy

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UNFCCC United Nations Framework Convention on Climate Change USGS United States Geological Survey Vlaamse Instelling voor Technologisch Onderzoek (Flemish Institute for VITO Technological Research), Belgium VSM Volcanic Soils Mask WB Water Body WBDA Water Bodies Detection Algorithm WBO Water Bodies Occurrence WBPM Water Bodies Potential Mask WBPV PROBA-V Water Bodies product WSR Water Surface Ratio 2D Two dimensional

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1 BACKGROUND OF THE DOCUMENT

1.1 EXECUTIVE SUMMARY

The Copernicus Land Service continuously monitors the status of land territories to generate geo- information at both local and global scale. Its Global Land component provides several bio- geophysical products describing the status of the land surface on a global scale and its evolution. Production and delivery of the parameters are to take place in a timely manner and are complemented by the constitution of long time series. This Algorithm Theoretical Basis Document (ATBD) describes the method for detecting Water Bodies (WB) using PROBA-V at 333m resolution on a global scale. The method for deriving the thresholds and the evaluation of the performance of the algorithm are based on the approach developed by Pekel et al., 2014 and on the methods developed for the PROBA-V 1 km product as described in GIOGL1_ATBD_WB1km-PROBAV-V2.

1.2 SCOPE AND OBJECTIVES

The scope of this document is to describe the theoretical basis and justification that underpins the implementation of the Copernicus Global Land Water Bodies product . It details the methodology applied on the PROBA-V 333m data. Moreover, it provides an evaluation of the algorithm performance and a description of the limitations.

1.3 CONTENT OF THE DOCUMENT

This ATBD is structured as follows:

• Chapter 1 (this chapter) introduces the product • Chapter 2 reviews the user requirements • Chapter 3 describes the methodology used to generate the Water Bodies product • Chapter 4 lists the references used in this document

1.4 RELATED DOCUMENTS

1.4.1 Applicable documents

AD1: Annex I – Technical Specifications JRC/IPR/2015/H.5/0026/OC to Contract Notice 2015/S 151-277962 of 7th August 2015 AD2: Appendix 1 – Copernicus Global land Component Product and Service Detailed Technical requirements to Technical Annex to Contract Notice 2015/S 151-277962 of 7th August 2015

1.4.2 Input

Document ID Descriptor

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CGLOPS2_SSD Service Specifications of the Global Component of the Copernicus Land Service.

1.4.3 Output

Document ID Descriptor

CGLOPS2_PUM_WB300m_V1 Product User Manual for version 1 of the 300 m Commenté [BL1]: RID 1408: document updated Water Bodies product from PROBA-V. CGLOPS2_QAR_WB300m_V1 Validation Report describing the results of the scientific quality assessment for version 1 of the 300 m Water Bodies product from PROBA-V.

1.4.4 External Documents

Document ID Descriptor

PROBA-V http://proba-v.vgt.vito.be/ PROBA-V PUM Products User Manual of PROBA-V data, available on http://proba-v.vgt.vito.be/sites/proba-v.vgt.vito.be/files/probav- products_user_manual_v2.2_0.pdf

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2 REVIEW OF USERS REQUIREMENTS

According to the applicable document [AD2], the user’s requirements relevant for Water Bodies PROBA-V 300 m are:

• Definition: permanent and seasonal water bodies, natural and man-made, independently of their size. Include but are not restricted to the lakes of the Global terrestrial Network for lakes.

• Geometric properties: o Location accuracy shall be 1/3rd of the at-nadir instantaneous field of view. o Pixel co-ordinates shall be given for center of pixel

• Geographical coverage: o Geographic projection: regular latitude/longitude o Geodetical datum: WGS84 o Coordinate position: center pixel o Pixel size: 1/336° - accuracy: min 10 digits o Global window coordinates: ▪ upper left: 180°W - 75°N ▪ bottom right: 180°E - 56°S

• Ancillary information: o the per-pixel date of the individual measurements or the start-end dates of the period actually covered o quality indicators, with explicit per-pixel identification of the cause of anomalous parameter result

• Accuracy requirements: o Baseline: wherever applicable the bio-geophysical parameters should meet the internationally agreed accuracy standards laid down in the document “Systematic Observation Requirements for Satellite-Based Products for Climate”. Additional details to the satellite based component of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC are available in the document WMO (GCOS-#200, 2016) (see Table 1). Commenté [BL2]: RID 1383 o Target: considering data usage by that part of the user community focused on operational monitoring at (sub-) national scale, accuracy standards may apply not on averages at global scale, but at a finer geographic resolution and in any event at least at biome level.

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Regarding this latter accuracy requirement, the water surface biophysical variable corresponds to different Essential Climate Variables (ECV), i.e. the “land cover” and the “lakes”.

Table 1: GCOS requirements for areas of water bodies as Lakes and Land cover Essential Climate Variables (GCOS-200, 2016) Variable Horizontal Temporal Accuracy Stability resolution resolution Areas of Equivalent Monthly 5% (maximum error of 5% (maximum error of lakes to 250m omission and commission omission and in lake area maps); commission in lake area location accuracy better maps); location accuracy than 1/3 of instantaneous better than 1/3 of field-of-view (IFOV) with instantaneous IFOV with 250m target IFOV 250m target IFOV Maps of 250m 1 year 15% (maximum error of 15% (maximum error of land-cover omission and commission omission and type in mapping individual commission in mapping classes); location accuracy individual classes); better than 1/3 of location accuracy better instantaneous field-of-view than 1/3 of instantaneous (IFOV) with 250m target field-of-view (IFOV) with IFOV 250m target IFOV

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3 METHODOLOGY DESCRIPTION

3.1 OVERVIEW

The Global Land Water Bodies 300 m Version 1 product is a 10-daily synthesis product derived from Top of Canopy (TOC) PROBA-V 333 m data. The product consists of 2 layers, the basic Water Bodies (WB) layer which tells which pixels contain water and which not, and the Quality Layer (QUAL) which tells something about the water body’s occurrences and as such can be used to retrieve additional information to the basic detection layer. As shown in Figure 1, the Water Bodies Detection Algorithm (WBDA) consists of four major steps.

• The PROBA-V daily Top-Of-Canopy synthesis products (S1-TOC) are first composited in a 10-daily synthesis (MEAN COMPOSITE, MC10) according the method of Vancutsem et al. (2007). During the compositing step the MC10 Status Map is calculated. • The SWIR, NIR and RED bands are then transformed to HUE, SATURATION and VALUE using a RGB to HSV transformation (COLOR TRANSFORM). • Subsequently, the application of specific threshold values on HUE and VALUE applied per pixel while taking into account the MC10 Status Map, WB Potential Mask (WBPM), Permanent Glacier Mask (PGM), the Volcanic Soils Mask (VSM) and the Maximum Water Extent Mask (MWEM), allows water body detection (WB DETECTION). • Finally an occurrence is calculated and, as such, provides additional qualitative information (WB OCCURRENCE).

Figure 1: General overview of the Water Bodies Detection Algorithm.

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3.2 THE RETRIEVAL ALGORITHM

3.2.1 Outline

A schematic overview of the Water Body Detection Algorithm (WBDA) is shown by the decision tree in Figure 2. Developing the WBDA involved three main steps which are described in the following paragraphs:

• Invalid data filtering • Incompatibility masking • Water body detection Although WB detection is based on thresholds on the HUE and VALUE, the description of the different steps of the Decision Tree is given in their actual sequence.

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Figure 2: Outline of the Water Bodies Detection Algorithm. Copernicus Global Land Operations – Lot 2 Date Issued: 18.10.2018 Issue: I1.10

3.2.2 Basic underlying assumptions

Water bodies have specific spectral features that mostly distinguish them from the other Earth surface objects. The red, NIR and SWIR bands are HSV-color transformed. After that a threshold on the HUE and VALUE bands are applied for the detection of water bodies. To avoid confusion with other objects on the Earth’s surface which have identical spectral properties as water bodies, additional data layers are used: the permanent glacier mask, volcanic soil mask, water body potential mask and maximum water extent mask are used to reduce commission errors. 3.2.3 Related and previous applications

The water bodies detection algorithm for the PROBA-V 300 m collection is related to the water bodies detection algorithm developed for the PROBA-V 1 km and SPOT/VGT collection. Two additional changes have been implemented.

• The thresholds for water body detection have been changed. Table 2 gives an overview of the thresholds for the different collections, i.e. PROBA-V 1 km, SPOT/VGT and PROBA-V 300 m. There is a small difference for the thresholds between the PROBA-V 300 m Commenté [BL3]: RID: 1384 collection and the PROBA-V 1 km collection. In this case the HUE threshold for the 300 m collection had to be increased from HUE = 100 (1 km) to HUE = 120. This is explained by the higher spatial resolution. Small objects or areas which have a significant influence on the overall spectral signature of the PROBA-V pixel become more important at higher resolution. In case they have a spectral signature identical to water bodies they might cause the pixel to be detected as a water body. At lower resolution their spectral contribution is less and the pixel is not detected as a WB.

Table 2: Thresholds for water body detection are applied on the HUE and VALUE, which are obtained after HSV color transformation of the SWIR, NIR and Red bands, on the NDVI and on the VALUE as an additional check besides the NDVI threshold check (NV).

PROBA-V PROBA-V Thresholds SPOT-VGT 1 km 300 m VALUE 0.14 0.15 0.14 HUE 100 100 120 NDVI 0.32 0.42 0.32 NV 0.11 0.13 0.11

• The Maximum Water Extent product of JRC’s Global Surface Water (MWE-GSW) Dataset provides information on all the locations ever detected as water over the last 32-year period (1984 - 2015), (Pekel et al., 2016). This dataset is used to derive the Maximum Water Extent Mask (MWEM) at PROBA-V 300 m resolution. This mask is used as an extra check on detected water bodies to reduce commission errors, i.e. water bodies can only be present on those locations ever detected as water over the 32-year period. Figure 3 shows the WB occurrence results for an area in Northern South-Sudan. Without the MWEM (a) Copernicus Global Land Operations – Lot 2 Date Issued: 18.10.2018

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false detections occur due to dark soils. With the MWEM (b) the false detections don’t occur and commission errors will be drastically reduced. To reduce omission errors, the mask overrules the Water Bodies Potential Mask (WBPM). The WBPM, which was derived from the Global Land Survey Digital Elevation Model (GLSDEM) (USGS, 2008), indicates on which locations water bodies might be present. Because the GLSDEM was a snapshot in time it might be too strict in some cases. Figure 4 shows the WB occurrence results for an area in Spain. Without the MWEM (a) the water reservoir is poorly detected. With the MWEM (b) the water reservoir is detected in its full size and omission errors will be drastically reduced.

Figure 3: Location in Northern South-Sudan (10°0’N, 31°80’E). a) False water bodies were detected because of dark soils. b) By applying the extra check on the MWEM most of the commission errors could be prevented.

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Figure 4: Location in Southern Spain (38°40N, 7°4’W). a) Due to the strict Water Bodies Potential Mask parts of the lake were not detected as water. b) The MWEM overrules the WBPM and as such reduces the omission errors.

3.2.4 Alternative methodologies currently in use Commenté [BL4]: RID 1386. Need to revise?

The Global Surface Water Dataset of JRC (Pekel et al., 2016) is a collection of water products derived from the Landsat archive (1984 – 2015). The dataset contains different files, i.e. Water Occurrence, Occurrence Change Intensity, Seasonality, Recurrence, Transitions, Maximum Water Extent and Water History. All files are made freely available. The Global Surface Water Dataset of JRC is a reference to water occurrence in the past while the PROBA-V WB 300 m is a near real time product, reflecting the recent status of global water occurrence. 3.2.5 Input data

Six different sets of input data are used in the Water Bodies Detection Algorithm, i.e. S1-TOC, MC10 Status Map, WB Potential Mask (WBPM) derived from the Global Land Survey Digital Elevation Model (GLSDEM), Permanent Glaciers Mask (PGM), Volcanic Soils Mask (VSM) and Maximum Water Extent Mask (MWEM) derived from the Global Surface Water – Maximum Water Extent data layer 3.2.5.1 The top of canopy daily synthesis reflectances (S1 TOC) and status map (MC10)

Global PROBA-V level 3 Top of Canopy (S1-TOC) daily synthesis products are used as input for the mean compositing. More details can be found on the website:

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Issue: I1.10 https://earth.esa.int/web/guest/data-access/browse-data-products/-/article/proba-v-1km-synthesis- products-s1-toa-s1-toc-and-s10-toc. The PROBA-V S1-TOC synthesis product ensures a daily coverage between Lat. 35° N and 75° N, and between 35° S and 56° S, and a full coverage every two days around the equator (between 35°S and 35°N). The S1 Level 3 Top of Canopy (atmospheric corrections applied) daily synthesis product is provided at 333 m spatial resolution. Surface reflectance is available for four spectral bands corresponding to the selected measurement (Table 3). The atmospheric correction is performed using SMAC 4.0 (Rahman and Dedieu, 1994). Standard input data layers includes Normalized Difference Vegetation Index (NDVI), geometric viewing and illumination conditions, reference to date and time of observations four reflectance bands (Table 3) and a status map containing identification of snow, ice, shadow, clouds, land/sea for every pixel (Table 4). The data is stored in a szip compressed hdf5 file.

Table 3: Spectral characteristics of the PROBA-V bands

Spectral band Wavelength BLUE 0.447 – 0.493 µm RED 0.610 – 0.690 µm NIR 0.770 – 0.893 µm SWIR 1.570 – 1.650 µm

Table 4: Status Map bit mapping of the PROBA-V data

Bit Name Description 000: clear 010: undefined 1 -3 Observation 011: cloud 100: snow/ice 0: sea 4 Land/sea mask 1: land 0: invalid data 5 SWIR quality flag 1: valid data 0: invalid data 6 NIR quality flag 1: valid data 0: invalid data 7 RED quality flag 1: valid data 0: invalid data 8 (Most significant) BLUE quality flag 1: valid data

Besides the mean compositing of the reflectance bands, a status map is constructed. It is used as an extra check in the WB detection algorithm to filter out invalid pixels.

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3.2.5.2 The Water Bodies Potential Mask (WBPM)

Pixels in hilly terrain having low reflectance values due to shadow or dark vegetation are often confused with WBs. To minimize these commission errors, a Water Bodies Potential Mask (WBPM) is derived from the Global Land Survey Digital Elevation Model (GLSDEM) (USGS, 2008). 3.2.5.3 The Permanent Glaciers Mask (PGM)

To avoid commission errors due to confusion with permanent glaciers, which often have similar spectral properties than WBs, a Permanent Glacier Mask (PGM) was constructed. The most recent permanent glacier data was downloaded from the National Snow and Ice Data Centre (NSIDC, December 2016) as a shape file which was subsequently rasterized to the PROBA-V image world Commenté [BL5]: RID 1387 size in order to obtain the Permanent Glacier Mask (PGM). When a pixel is indicated as permanent glacier, it cannot be a water body. Figure 5(a) shows a Google Earth image over the Alps, the corresponding PGM is shown in (b). Because the extent of glaciers worldwide are strongly influenced by climate (global warming), the PGM needs to be frequently updated. An update of the permanent glacier data becomes frequently available at the NSIDC.

Figure 5: a) This Google Earth image shows an area over the Alps (Upper left corner: 48°08’25” N, 5°40’0” E). b) The permanent glacier mask for the same area in (a). 3.2.5.4 The Volcanic Soils Mask (VSM)

Locations comprised of dark volcanic soils which are spread over wider flat areas are easily confused with water bodies. To account for these potential commission errors, a Volcanic Soil Mask (VSM) was made. The geographical locations obtained from the Holocene Volcano List, set- up by the Global Volcanism Program of the Smithsonian Institution, National Museum of Natural History (http://www.volcano.si.edu/), were used to locate volcanoes and their dark volcanic soils. Volcanic soils can be easily located on Google Earth using the information from the Holocene Volcano List. As shown in Figure 6(a), these areas were manually delineated on the Google Earth

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image (February 2015). The polygons were exported to shape files as shown in (b) and Commenté [BL6]: RID: 1387 subsequently rasterized to a VSM (c). Small errors made during delineation disappear when rasterizing to the lower resolution PROBA-V pixel size. The geological situation in the volcanic areas of southern America, i.e. Chili and Peru, is very complex and delineating the volcanic soils in these areas was not always easy. It is therefore very well possible that some smaller regions in this area are still missing in the VSM. On the other hand future eruptions might produce new dark volcanic soils leading to false detected WBs. In both cases an update of the VSM is needed.

Figure 6: a) Google Earth image showing part of northern Ethiopia and southern Eritrea with the dark volcanic soils manually delineated (red polygons). b) Dekad MC10_20140521 for the same area with the derived dark volcanic soils shape file overlaid. c) The final volcanic soil mask (white=volcanic soil) obtained after rasterizing the shape file. 3.2.5.5 The Maximum Water Extent Mask (MWEM)

The Maximum Water Extent product of JRC’s Global Surface Water (MWE-GSW) dataset is used to create the Maximum Water Extent Mask (MWEM). This dataset provides information on all the Document-No. CGLOPS2_ATBD_WB300m_V1 © C-GLOPS2 consortium

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Issue: I1.10 locations ever detected as water over the last 32-year period. The GSW dataset has a spatial resolution of 1/4000° and therefore needs to be resampled to the PROBA-V 300 m, 1/336° spatial resolution. For each PROBA-V 300 m pixel, the corresponding pixels in the MWE_GSM are searched for. A pixel is set in the MWEM if at least one of the corresponding pixels was found to have ever contained water in the MWE-GSM product (Figure 7a, b).

Figure 7: a) The high resolution Maximum Water Extent product of JRC’s Global Surface Water dataset shows where in the last 32 years water was ever detected (blue colored). b) The PROBA-V 300 m resolution Maximum Water Extent Mask is derived from the Maximum Water Extent product of JRC’s Global Surface Water dataset. Both images have the PROBA-V 300 m raster overlaid (red). The shown water reservoir is located in India (25°25’N, 77°55’E). 3.2.6 Output product

The water bodies product consists of two layers:

• The Water Bodies (WB) The values of the digital numbers in the first band (WB) are summarized in Table 5. The values are kept consistent with the version 1, 1 km product over Africa.

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Table 5: Output product legend.

Value Label 0 Sea 70 Water 251 No data 255 No water

• The Quality Layer (QUAL) Next to the Water Bodies layer, a second Band that acts as a Quality Layer (QUAL) is added in the product and can be used to retrieve additional information to the basic detection layer (WB). The Quality Layer represents Water Bodies Occurrence (WBO) if the base layer indicates “Water” or the source of masking if the base layer indicates “No water” or “No data”. The values of the digital numbers in the QUAL are summarized in Table 6.

Table 6: Output QUAL product legend. The colored entries are the occurrence values.

Value Occurrence 0 Sea 71 Very Low 72 Low 73 Medium 74 High 75 Very High 76 Permanent 151 Lowland vegetation 152 Mountain vegetation 241 Glacier 242 Volcanic 243 Mountain no vegetation 244 Lowland no vegetation 251 No data 252 Cloud 253 Snow 254 SZA > 65°

3.2.7 Methodology

In order to come to the Water Bodies product the collected PROBA-V imagery needs to be processed in a number of steps:

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3.2.7.1 Mean compositing

The MC10 composites (SWIR, NIR, RED, BLUE) are obtained using the mean compositing (MC) method (Vancutsem et al., 2007) applied on the PROBA-V S1 daily reflectance values over a 10- days window. The mean compositing algorithm improves the radiometric quality of the temporal synthesis by averaging the reflectances and therefore reduces the random component of the noise. Figure 8 depicts the processing flow of the MC10 algorithm. The mean composite is calculated, pixel by pixel, across the four spectral bands simultaneously. For each pixel location, the input data over a time period of 10 days is handled sequentially. The Mean Compositing step keeps track of the number of observations used to perform the compositing operation. The first number of observations (NOBS) represents the original number of overpasses available during the compositing period, irrespectively of the actual radiation values or observation status.

The compositing algorithm always tries to return the most optimal result. To achieve this, only the Commenté [BL7]: RID 1338 most optimal observations are used. The observation type is determined as shown in Figure 9. If one or more radiometric bands quality flags in the S1-TOC SM show a bad quality, the new observation type will be undefined. Next, the observation bits of the S1-TOC are taken into account. If the observation of the S1-TOC is not ‘clear’, the observation is copied. The original cloud detection of the PROBA-V collection 0 data, proved to do an under detection of clouds. Hence, for each pixel with a ‘clear’ observation in the S1-TOC SM, an additional classification is done base on the method of Vancutsemet al., 2007. The thresholds values have been adapted to reflect the spectral difference between the SPOT-VGT 1km collection used in the reference method and the PROBA-V 333m collection. To determine if the classified observation is a better observation type or not, the following convention is used: clear observation pixels are considered to be more optimal then snow. Snow pixels are considered to be more optimal then cloud pixels. Cloud pixels are more optimal then undefined observation pixels. If for example the algorithm would detect within a given dekad a total of 7 observations with 3 cloudy and 4 clear pixels, only the latter 4 are used in the compositing. As such, only identical observation type pixels of the most optimal type are accumulated in the sum and contribute to a second set of ‘number of observations’, indicating the total observations of the most optimal observation type. This number of observations (NMOD) is used to calculate the composite mean value. Both NOBS and NMOD are stored in their own dataset in the MC10 file.

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Figure 8: MC10 algorithm flow.

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Figure 9: Decision tree for MC10 observation type classification.

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Once all daily input data of a pixel is calculated, the status map is generated and the mean composite value for all four spectral bands is calculated. In addition to the mean reflectances for each band, the NMOD and the NOBS, the Mean Compositing step also provides a status map (SM) (Table 7) for each pixel. The status map mimics the bit positions as used in the PROBA-V input data Status Map.

Table 7: Status Map (SM) bit mapping.

Bit Name Description 000: clear 010: undefined 1 -3 Observation 011: cloud 100: snow/ice 0: sea 4 Land/sea mask 1: land 0: no mean composite 5 Mean composite SWIR quality flag 1: valid mean composite 0: no mean composite 6 Mean composite NIR quality flag 1: valid mean composite 0: no mean composite 7 Mean composite RED quality flag 1: valid mean composite 8 (Most 0: no mean composite Mean composite BLUE quality flag significant) 1: valid mean composite

The observation status (bits 1 to 3) is based upon the most optimal observation used for the mean composite. The land/sea mask bit (bit 4) is an exact copy of the PROBA-V land/sea mask. Bits 5, 6, 7 and 8 are quality flags for the mean composite spectral bands. The quality flag is set to 1 when a mean composite could be calculated and is set to 0 in case no compositing is done. The latter only occurs when there was no valid observation to be used in the compositing (NMOD equals 0).

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3.2.7.2 Construction of the Water Bodies Potential Mask

This mask was derived from the GLSDEM, which has a 90 m horizontal and 1 m vertical resolution, in three steps:

1. Search for the lowest points in the terrain.

A pixel is a candidate lowest point and a potential water body when the pixel elevation is lower than or equal to its eight neighbours. Therefore, the 8 pixels neighbourhood of each GLSDEM pixel is evaluated. Figure 10a shows part of the GLSDEM over the Rift Valley in Ethiopia, in Figure 10b the detected lowest points are shown (in yellow).

Figure 10: a) Part of the 90 m spatial resolution GLSDEM over Rift Valley in Ethiopia. b) The detected lowest points for this area are colored yellow. The larger sized colored areas are areas of equally low elevation which correspond to some smaller lakes in the region.

2. Filtering and expanding the detected lowest points.

The next step in generating the WBPM is expanding the detected lowest points depending on the topography. For each detected lowest point, an imaginary water level is raised in steps of 1m till the maximum rise of 5m or the flooding1 condition is reached. As long as the edge of the potential WB is not flooded, its area is extended according to the raised level, i.e. all neighbouring pixels having the additional elevation are added to the potential WB area. This is schematically shown in

1 Flooding means detection of a pixel in the expanded area with an elevation lower than the actual rise level, e.g. if the imaginary water level is raised to 405 m and there is a pixel in the expanded area which has an elevation of 404 m, than the flooding condition is reached.

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Issue: I1.10 a two dimensional representation in Figure 11. The initial detected lowest point contains two pixels. Rising the water level 1 m will expand the potential WB with an additional 5 pixels. Because no flooding occurs the water level is increased again with 1 m. Now, 3 additional pixels are added to the potential WB. Further, rising the water level will start flooding the WB because the first pixel elevation right to the expanded WB is now lower. The pixels initially detected as lowest point and surrounded by eight neighbours of equal height are marked as ”Level-1” in the potential WBs map. The other initially detected lowest points and the expanded pixels are marked as “Level-2”. The two levels are used in the last step when resizing the potential WBs map to the PROBA-V resolution to obtain the final WBPM.

Figure 11: Expanding the initially detected lowest point by systematically rising an imaginary water level in steps of 1 m. The corresponding 90 m spatial resolution pixels are indicated by the dots at the bottom. Figure 12 shows a detail from an area over Rift Valley. The horizontal and vertical profile plots are taken according to the red lines in the images. The focus location of the plots is placed in the center of a detected potential WB as shown by the crossing red lines in the detail images. In the plots, this location is marked by the vertical red line. As seen in the detail window on the horizontal profile, one additional potential WB was detected marked (1). On the vertical profile two additional potential WBs were detected marked (1’) and (3).

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Figure 12: a) Part of the GLSDEM over Rift Valley. b) The detected potential WBs for this area. The detailed window shows a potential WB in the center (crossing of red lines). The horizontal and vertical profiles are according to the red lines over the images. On the horizontal profile, the wetland (1) near lake Hora (2) and the potential water body (indicated by the long red arrow) are seen. On the vertical profile part of the wetland (1’) near lake Hora (2), the potential water body (indicated by the long red arrow) and lake Bishoftu (3) are seen.

3. Deriving the WBPM

In the final step, the 90 m spatial resolution potential WBs map is re-sampled to the PROBA-V 300 m spatial resolution. For each pixel in the PROBA-V image, the corresponding pixels in the 90m potential WBs map are located (which is 121 pixels). The resized (300 m) WBPM pixel is indicated as a potential WB in two cases:

- At least one of the corresponding pixels was labelled as “Level-1”; - At least nine of the corresponding pixels (7%, a figure empirically defined) were labelled as “Level-2”.

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Figure 13(a) shows a visual representation of the PROBA-V image over the Rift Valley test area and the derived WBPM for the same area in (b). Indicated are the lakes Abijato and Langano. A profile according to the red line is taken over the area for which the GLSDEM profile is shown in the plot. The seven detected potential WBs over this profile are indicated by the transparent colored boxes. The WBPM was made based on the GLSDEM for which data was collected in the early 2000s. Because of the high resolution of the GLSDEM dataset (90 m spatial and 1 m vertical), the derived WBPM (300 m spatial resolution) reveals the finest detail as shown in Figure 13(b). Although the Earth’s topography changes only notably over large geological time scales, natural events (earthquakes) or anthropogenic activities (dam building) can influence the topography on shorter time scales. Therefore, when such events take place, the WBPM needs to be updated. The resulting 300 m WBPM (1=”potential water body”, 0=”no water body possible”) is used as an extra input in the WB DETECTION processing step. The PROBA-V 300 m world image contains 120.960 samples x 47.040 lines or 5.7*109 pixels. Of those amount 29% (1.7*109 pixels) consists of land mass (i.e. not ocean), 50.5% (833.3*106 pixels) of those land mass is indicated as potential water body.

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Figure 13: a) Part of image MC10_20131021 taken over the Rift Valley in Ethiopia (SWIR, NIR and Red bands assigned to the RGB channels resp.). b) The WBPM for the same area in (a) (white=potential water body, black=no water body possible). The plot shows the vertical GLSDEM profile according to the red line. Marked with the blue boxes are the seven potential WBs. The arrows indicate the location of lake Abijato (1) and lake Langano (2). 3.2.7.3 Colorimetric transformation

The Global Water Watch (GWW) algorithm (Pekel et al., 2005) was originally developed and optimized for a window extending from Senegal (17.7° West) to India (85° East) and from the equator to the Mediterranean sea (37.4°North) in the framework of the Global Watch project (Pekel et al., 2005). Later on, the methodology was improved and adapted to MODIS and validated over Africa (Pekel et al., 2014). The approach is based on a transformation of the RGB (Red, Green and Blue) color space into HSV (Hue, Saturation and Value) that decouples chromaticity and luminance (Figure 14). The HSV color space is also commonly used in image processing. It is a nonlinear transformation of the RGB color space using equations 1, 2 and 3 presented in Figure 14, with R mapped to SWIR band, G to NIR band and B to the RED band. The Hue (H) is defined as the dominant wavelength

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Issue: I1.10 of the perceived color. It is the visual perceptual property corresponding to the categories called yellow, blue, green, etc. Hue is considered as an angle between a reference line and the color point, going from 0° to 360°. The Saturation (S) is defined as the degree of purity of the color and may be intuitively considered as the amount of white mixed in a color. This component represents the radial distance from the cone center going from 0 to 1. The nearer the point is to the center, the lighter is the color. The Value (V), which is a brightness approximation, represents the height of the axis of the HSV cone, going from 0 to 1. This axis describes the gray levels.

푉 = 푚푎푥(푅, 퐺, 퐵) (1)

푉−푚푖푛 푅,퐺,퐵 푆 = ( ) (2) 푉 퐺−퐵 (60° ∗ + 360°) 푚표푑 360° 𝑖푓 푉 = 푅 푉−푚푖푛(푅,퐺,퐵) 퐵−푅 퐻 = 60° ∗ + 120° 𝑖푓 푉 = 퐺 (3) 푉−푚푖푛(푅,퐺,퐵) 푅−퐺 60° ∗ + 240° 𝑖푓 푉 = 퐵 { 푉−푚푖푛(푅,퐺,퐵)

Figure 14: The HSV color space. This color space presents three interesting properties: (i) it is intuitive as it represents the colors as interpreted by human brain; (ii) the chromaticity (H and S) and Value (V) are decoupled, reducing the problem of brightness level inconsistencies of the pixel perceived color (i.e. spatially and temporally) resulting from various observation conditions. This property is fundamental, as the automatic detection technique has to be independent of all effects affecting the received spectral signal. Practically, a way to solve this issue is to collapse the 3-dimensional space into a 2- dimensional sub-space keeping only the Hue and Value components of the perceived color. Among the 2 components, (iii) H represents a qualitative spectral index. By analyzing the distribution of the pixels in the Hue - Value space, the pixels can be classified as “water” and as “no water” based on thresholds (see Pekel et al., 2014).

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3.2.7.4 Invalid data filtering

Figure 15: The first part of the WBDA comprises invalid data filtering. The first part of the Water Body Detection Algorithm (WBDA) comprises invalid data filtering (Figure 15). The status of each image pixel is defined by the Status Map of MC10. Pixels indicated as “undefined” in the MC10 Status Map are classified here as “No Data”. A low sun elevation causes elongated shadows which are observed in the remote sensing imagery as darkened Earth surfaces. These might be detected as water bodies when their VALUE drops below the threshold level. A check on the Solar Zenith Angle (SZA) prohibits the detection of invalid WBs, i.e. when a pixel has a SZA larger than 65° no WB detection is done and the pixel is classified as “No Data”. The next step in the WBDA is filtering the clouds, the cloud shadows and snow, using the information from the MC10 Status Map. Pixels indicated as “Cloud” or “Snow” are not considered in the WB detection algorithm. Cloud shadows on the Earth’s surface often cause the VALUE to drop below the threshold and are as such regularly classified as WBs. Therefore, to avoid confusion due to cloud shadow in the immediate neighbourhood of the initial defined cloud pixel, an additional dilate filtering is performed. In case a pixel is indicated as “Cloud” in the MC10 status map, its surrounding twelve neighbouring pixels (the circular neighbourhood with radius 2 pixels, i.e. they total twelve pixels) are considered as “Cloud” as well and are therefore not taken into account for WB detection. Commenté [BL8]: RID 1389 Figure 16 shows the result of cloud shadow masking over two locations in Argentina having a dark soil background (a, b). Before cloud dilation was performed numerous false detected water bodies can be seen (b,d). These false detections are caused by the cloud’s shadow. The cloud dilatation, by a circular neighbourhood of twelve pixels around the cloud pixels, most false detections are prohibited. The circular neighbourhood of twelve pixels was empirical defined and is a trade-off between commission and omission errors (c,f).

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Figure 16: The removal of cloud shadow by dilatation of the cloud indicated pixels. a) Dekad MC10_20140511 over Argentina (center at 31°33’50”S, 65°10’37”W). b) The Water Bodies detection result without delate filtering. Due to cloud shadows lots of false WB detections appear. c) After de clouds are dilated by its circular neighborhood of twelve pixels most of the false detections are masked by the dilated cloud. The red oval shows a false detection which could not be reached by the dilated cloud. d) Dekad MC10_20140511 over Argentina (center at 31°45’58”S, 65°4’44”W). e) An existing small lake is shown in the lower left part of the image, besides some false detections can be seen near to the clouds. No cloud dilatation was done yet. f) After cloud dilatation the false detection don’t appear, a small part of the lake is now obstructed by the dilated cloud as indicated by the red oval.

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3.2.7.5 Incompatibility masking

Figure 17: The second part of the WBDA comprises Incompatibility Masking. The second part of the WBDA comprises incompatibility masking (Figure 17) to mask out natural and man-made objects on the earth’s surface, having spectral properties similar to WBs, which cause false detection and commission errors. Glaciers are relatively bright surfaces with different shades of blue and grey which regularly result in spectral properties similar to WBs. The permanent glacier mask (PGM) excludes these areas from WB detection. An important source of false WB detection is caused by dark soils, mainly dark volcanic soils. The volcanic soil mask (VSM) excludes these areas from WB detection. Because areas with distinct topographic features such as slopes or extreme height differences can impossibly hold WBs, these areas are also excluded from WB detection. This is done using the water body potential mask (WBPM) in combination with the Maximum Water Extent Mask (MWEM). The potential WB pixels have value 1 in these masks; those which cannot be potential WBs have value 0 in both masks and are therefore not considered. This means that the MWEM overrules the WBPM, i.e. if the WBPM indicates a potential WB and the MWEM indicates that these was never water, than it will not be a potential WB. An additional check on the NDVI (4) value (the threshold of 0.32 was empirically defined) is used to distinguish between ‘Mountain no vegetation’ and ‘Mountain vegetation’.

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휌 −휌 푁퐷푉퐼 = 푁퐼푅 푅퐸퐷 (4) 휌푁퐼푅+휌푅퐸퐷 Finally, vegetated surfaces, e.g. large overhanging trees or floating plants, prohibit the detection of WBs underneath. Besides, vegetated surfaces often cause confusion with WBs. Therefore, the NDVI is used to identify the vegetated areas. Pixels having an NDVI value higher or equal to 0.32 (the threshold was empirically defined) and having a WBPM pixel set (locating lowland areas where holding a WB is possible) are considered to be “Lowland Vegetation”. Some WBs have a high NDVI value (due to algae load or adjacency effect of the surrounding vegetation) and are as such classified as “Lowland Vegetation”. To avoid these omission errors an additional check on VALUE is added, this threshold level is denoted as NV. If the VALUE of a pixel Commenté [BL9]: RID 1333 which is designated as “Lowland Vegetation”, is less than 0.11 (a value empirically defined) it is considered as “Water” depending on the thresholds of HUE and VALUE. 3.2.7.6 Water body detection

In the previous steps, i.e. ‘Invalid Data Filtering’ and ‘Incompatibility Masking’ the PROBA-V MC10 images are filtered before the actual WB detection takes place. Only pixels having following conditions are considered for WB detection: - SZA less than 65°; - SM does not indicate cloud or snow; - Not indicated as “Permanent Glacier” or “Dark Volcanic soil” - Indicated as a maximum water extent pixel; Commenté [BL10]: RID 1336 - NDVI value less than 0.32 or, if not, VALUE less than 0.11.

The pixels surviving the previous conditions are in the finals step compared against different threshold levels on the HUE and VALUE bands to finally decide if they belong to water bodies or lowland (Figure 18)

Figure 18: The last part of the WBDA comprises actual Water Body Detection based on threshold levels on the HUE and VALUE bands. The thresholds consist of two parabolic functions. Document-No. CGLOPS2_ATBD_WB300m_V1 © C-GLOPS2 consortium

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Threshold values on the HUE and VALUE bands are used for detecting WBs worldwide. The threshold consists of two quadratic functions which were empirically defined. The left part (HUE = 0 till 34) can be written as:

(34−퐻푢푒 )2∗푆푐푎푙푒 푉푎푙푢푒 ≤ 퐿 퐿 + 푂푓푓푠푒푡 † (5) 퐿 2 퐿 With:

퐻푢푒퐿 = [0 … 34] 0.41 푆푐푎푙푒 = 퐿 342

OffsetL = 0.14 The right part (HUE = 34 till 120) is a bit more complex because it is based on a parabola rotate over 0.2° for which the rotated coordinates can be written as:

푥 = 퐻푢푒 ∗ cos(훼) − 퐻푢푒2 ∗ 푠𝑖푛(훼) (6) 푦 = 퐻푢푒 ∗ 푠𝑖푛(훼) + 퐻푢푒2 ∗ cos (훼) (7) This equation can be solved using the standard form:

−푏±√푏2−4푎푐 푥 = (8) 2푎

And from which only one solution is used to calculate the corresponding VALUE threshold:

−(− cos(훼)) − √(−cos (훼))2−4∗푠푖푛(훼)∗퐻푢푒 푥 = 푅 (9) 2∗푠푖푛(훼)

(푥∗푠푖푛(훼)+ 푥2∗cos (훼))∗푆푐푎푙푒 푉푎푙푢푒 ≤ 푅 + 푂푓푓푠푒푡 † (10) 푅 2 푅 With: 훼 = 0.2°

퐻푢푒푅 = [34 … 360] − 48.5 ‡ 1 푆푐푎푙푒 = ‡‡ 푅 95000

OffsetR = 0.14

† The division by two was done to sharpen the parabolic curve in order to reduce the commission errors.

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‡ The value 48.5 was empirically defined; it is used to broaden the parabolic curve (higher values will broaden the curve). ‡‡ The value 95000 in the scale factor was empirically defined; it defines the height of the parabolic curve (higher values will lower the curve).

The parabolic thresholds consists of a left and a right part. The OffsetL and OffsetR directly Commenté [BL11]: RID 1330 influence the “minimum level” of the parabolic function. They must at all times have identical values otherwise there will be a ‘jump’ in the function. The maximum HUE is always in the right part and is contained in the HueR parameter. This one immediately defines the max HUE value. In other words these parameters directly define the shape and level of the parabolic function as inherent defined by the math functions. Figure 19 shows the 2D scatter plots of the Vietnam test areas. The 2D scatter plot has the HUE on the x-axis and VALUE on the y-axis. The location of the pixels from the reference WBs are indicated by the colored plus symbols and this for three ranges of Water Surface Ratio (WSR), i.e. blue for WSR = 0.95 to 1, cyan for WSR = 0.8 to 0.95 and magenta for WSR = 0.7 to 0.8. The other WSR ranges are not indicated as they would overrule the figure. The non-WB pixels, which are denoted as land, are indicated by the dark grey dot symbols. The final thresholds levels are indicated by the red lines. Although the final thresholds are tuned to detect as much WBs as possible, not all WBs can be detected because they are intermixed with land pixels.

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Figure 19: 2D scatter plot of the Vietnam test area showing the final threshold levels. Land pixels are indicated by the dark grey dots, reference WBs (obtained from Landsat 8) are indicated by the colored plus symbols for different ranges of water surface ratios. A further refinement of the thresholds is not possible because WB pixels and land pixels are intermixed. 3.2.7.7 Extracting reference water bodies

Defining the thresholds and demonstrating the maturity of the PROBA-V WBDA was done by comparing the obtained WB detection results against reference water bodies derived from high spatial resolution Landsat 8 images. In total 22 areas were selected worldwide within the time period end of October 2013 till end of May 2014 (Table 8, Figure 20).

These 22 images were pre-processed and WB detection was done based on threshold values on Commenté [BL12]: RID 1390 HUE and VALUE which were empirically defined. This could easily be done as the images were cloud free, i.e. no cloud shadows and contained no structures that caused confusion with water bodies. So a simple approach could be used as described below. The obtained L8 reference WBs were subsequently manually checked against Google Earth images. Using this approach it was fast and easy to obtain reference WBs based on Landsat 8. Document-No. CGLOPS2_ATBD_WB300m_V1 © C-GLOPS2 consortium

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Table 8: Landsat-8 scenes used to define the PROBA-V water bodies detection thresholds.

2013/ 14 Upper Left Coordinate SPOT-VGT N° Area Landsat 8 scene Dekad Lat Lon ns nl 1 Argentina1 LC82270862014111LGN00 20140421 -36.40625000 -63.94196429 299 239 2 Argentina2 LC82310932014011LGN00 20140111 -46.29910714 -73.83482143 376 257 3 Australia LC81110782014131LGN00 20140511 -24.94196429 118.49553571 267 236 4 Bangladesh LC81380442014112LGN00 20140421 24.17410714 87.74553571 244 240 5 Bolivia LC82330692013310LGN00 20131101 -11.95089286 -66.85267857 232 239 6 Brazil LC82220742014028LGN00 20140121 -19.17410714 -51.55803571 248 237 7 Canada LC80180272014151LGN00 20140521 48.53125000 -80.06696429 332 246 8 China LC81210382014121LGN00 20140501 32.79910714 116.05803571 271 240 9 Congo LC81740662014140LGN00 20140521 -7.62053571 25.22767857 235 238 10 India LC81460422014072LGN00 20140311 27.04910714 76.05803571 249 240 11 Mali LC81950502013332LGN00 20131121 15.51339286 -2.25446429 234 239 12 Mexico LC80270422014118LGN00 20140421 27.04017857 -100.12053571 260 238 13 Poland LC81870222014087LGN00 20140401 55.60267857 21.60267857 393 250 14 RiftValley1 LC81680542013335LGN00 20131201 9.72767857 38.20089286 232 238 15 RiftValley2 LC81680552013335LGN00 20131201 8.28125000 37.89732143 231 238 16 RiftValley3 LC81680562013335LGN00 20131201 6.83482143 37.59375000 230 238 17 Rusia LC81770212014129LGN00 20140501 56.99553571 37.60267857 426 248 18 SouthAfrica LC81690802014121LGN00 20140501 -27.77232143 28.12946429 277 246 19 Spain LC82010332014121LGN00 20140501 39.95089286 -5.66517857 306 239 20 Turkey LC81730332013306LGN00 20131101 39.95982143 37.65625000 299 240 21 USA LC80430342014134LGN00 20140511 38.54017857 -121.87946429 281 242 22 Vietnam LC81240512014062LGN00 20140301 14.08482143 107.11160714 241 242

Figure 20: Location of the Landsat scenes used to define the detection method.

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Figure 21 gives a schematic overview of the workflow used to extract the reference water bodies from Landsat-8 scenes.

Figure 21: Workflow for the extraction of reference water bodies using Landsat-8 scenes. An as much as possible cloud free scene is searched for the required geographical location and time period. After pre-processing and HSV color transform of the Landsat-8 scene, WB detection was done using a Decision Tree Classifier (DTC). The obtained Landsat-8 WBs are subsequently used to calculate the Water Surface Ratio using the spatial information from the PROBA-V image. The USGS ‘EarthExplorer’ was used to search for Landsat-8 OLI/TIRS cloud free scenes over randomly selected geographical coordinates worldwide. Subsequently one cloud free scene or one which is as much as possible cloud free, was downloaded and unpacked. The data was subsequently pre-processed to convert the eleven bands to TOA radiance, the 9 OLI bands to TOA reflectance and the 2 TIRS bands to at-satellite brightness temperature. The ‘Automatic Cloud Cover Assessment – modified’ (ACCAm) was used for cloud detection. ACCAm is a modified version of the ACCA (Irish et al., 2006) developed for Landsat-7. In this modified cloud detection algorithm only the spectral cloud identification is retained and an additional check on the cirrus band is added. In the next step the ‘SWIR 1’, ‘NIR’ and ‘Red’ bands are color transformed to the HSV-color system. This allows for an easy water bodies detection using thresholds on the HUE and VALUE band. As shown by the Decision Tree Classifier (DTC) in Figure 22, the class ‘NoData’ is based on a zero value in the TIR 1, SWIR 1, NIR or Red band. The ‘Cloud’ class is set according the cloud mask generated by ACCAm (1 means cloud detected). In the final step water is separated from Document-No. CGLOPS2_ATBD_WB300m_V1 © C-GLOPS2 consortium

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land by a threshold value on HUE (≥ 160) and VALUE (< 0.4). These thresholds were empirically Commenté [BL13]: RID 1331 defined. Because as much as possible cloud free images were used and the validation areas didn’t contain surfaces that confuse with water bodies, water bodies detection could easily be done by only applying these thresholds.

Figure 22: Decision Tree Classifier for WB detection on Landsat-8 images. A check on different bands and on cloud cover removes incompatible pixels from the scene. A check on HUE and VALUE is used to detect water bodies. In the next step, the Landsat-8 corner coordinates were used to extract the corresponding area from the PROBA-V Water Bodies (WBPV) product and this for the corresponding dekad by taking into account the Landsat-8 acquisition date. Subsequently, for each pixel of the extracted WBPV product, the Water Surface Ratio (WSRxy) was calculated. Therefore, for each WBPV pixel the corresponding pixels from the Landsat-8 DTC result (Kxy) were extracted and the WSR was calculated according (11):

(푁° 표푓 푤푎푡푒푟 푝푖푥푒푙푠)퐾푥푦 푊푆푅푥푦 = (11) (푇표푡푎푙 푁° 표푓 푣푎푙푖푑 푝푖푥푒푙푠)퐾푥푦

The WSR has a value greater than zero and less or equal to one when ‘Water’ classified pixels are present in the corresponding DTC result pixels. The WSR is not calculated when the cloud cover of the corresponding Landsat-8 DTC pixels is more than 10% or if more than 10% of the pixels contain no data. Pixels in the WSR image are indicated as ‘Cloud’ (WSR = 3) or ‘No Data’ (WSR = 0) respectively. When no ‘Water’ classified pixels are present in the corresponding DTC pixels the WSR pixel is set to ‘Land’ (WSR = 2).

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In the final step a confusion matrix (CMX) is calculated as described by Roy and Boschetti (2009) and Padila et al. 2014. From these CMX the overall accuracy (OA), commission error (CE) and omission error (OE) is calculated. The OE is calculated per minimum WSR range (WSRm), i.e. 0.95, 0.8, 0.7, 0.6 and 0.5 and per test area. The confusion matrix can be written as:

Reference

WB NoWB

WB p11 p12 WBPV NoWB p21 p22 With: − p11 = the number of pixels indicated as WB in both reference and WBPV and having a WSR larger or equal to the WSRm; − p12 = the number of pixels not indicated as WB in the reference but indicated as WB in WBPV and having a WSR larger or equal to the WSRm; − p21 = the number of pixels indicated as WB in the reference but not in WBPV and having a WSR larger or equal to the WSRm; − p22 = the number of pixels not indicated as WB in both reference and WBPV and having a WSR larger or equal to the WSRm. From the CMX the Overall Accuracy (OA), Commission Error (CE) and Omission Error (OE) can be calculated as:

푇표푡푎푙 푛푢푚푏푒푟 표푓 푝𝑖푥푒푙푠 = 푝11 + 푝12 + 푝21 + 푝22 (12)

푝11+푝22 푂퐴 = ∗ 100 (13) 푇표푡푎푙 푛푢푚푏푒푟 표푓 푝푖푥푒푙푠

푝12 퐶퐸 = ∗ 100 (14) 푝11 + 푝12

푝21 푂퐸 = ∗ 100 (15) 푝11 + 푝21 The average values for OA, CE and OE are calculated as the mean of the OA, CE and OE for all test areas. 3.2.7.8 Defining the water body detection thresholds

The threshold levels were iteratively and empirically defined by different decision tree classifications with different settings for the threshold levels and by comparison of the results by using the reference WBs obtained from Landsat 8 images. Table 9 shows the comparison results for different settings of the threshold levels which are calculated over the 22 test regions. The figures give the mean calculated over all test regions. The Commission Error (CE) and Omission Error (OE) are listed in the table. The OEs are calculated for six minimum Water Surface Ratios (WSR) with the minimum WSR indicated in the table (e.g. a minimum WSR = 0.95 means only WB Document-No. CGLOPS2_ATBD_WB300m_V1 © C-GLOPS2 consortium

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Issue: I1.10 pixels with a WSR >= 0.95 are considered as reference WB). Because the overall accuracy is always extremely high (more than 97 %), due to the fact that the non-WB pixels outnumbers the WB pixels, it is not an appropriate metric and not reported.

Table 9: The results for eight different settings of the threshold levels. The overall accuracy (OA), omission errors (OE) and the commission errors (CE) are listed in percentage. The OEs are given for six ranges of minimum WSR . The figures are calculated over the 22 test regions for the period Oct2013 – May2014. The green indicated entries show the best results and are the final threshold levels.

OE (%) PROBA-V 333m (2013-14) CE (%) Minimum WSR 0.95 0.9 0.8 0.7 0.6 0.5 VALUE = 0.14, HUE=100, 0 NDVI=0.32, NV = 0.11, 1.9 0.6 0.9 2.3 4.6 7.9 12.4 Parabolic VALUE = 0.15, HUE=100, 1 NDVI=0.32, NV = 0.11, 2 0.5 0.9 2 4.1 7.1 11.4 Parabolic VALUE = 0.13, HUE=100, 2 NDVI=0.32, NV = 0.11, 1.8 0.6 1.1 2.6 5.2 8.9 13.6 Parabolic VALUE = 0.14, HUE=110, 3 NDVI=0.32, NV = 0.11, 1.7 0.6 1 2.4 4.8 8.3 13 Parabolic VALUE = 0.14, HUE=120, 4 NDVI=0.32, NV = 0.11, 1.3 0.6 1 2.5 5 8.6 13.4 Parabolic VALUE = 0.14, HUE=125, 5 NDVI=0.32, NV = 0.11, 1.2 0.6 1.1 2.5 5.1 8.7 13.5 Parabolic VALUE = 0.14, HUE=120, 6 NDVI=0.3, NV = 0.11, 1.3 0.6 1.1 2.6 5.2 8.9 13.8 Parabolic VALUE = 0.14, HUE=120, 7 NDVI=0.32, NV = 0.13, 1.6 0.4 0.7 1.7 3.7 6.8 11 Parabolic

First the thresholds are set according those used for WB detection on the PROBA-V 1 km collection (VALUE = 0.14, HUE = 100, NDVI = 0.32 and the additional VALUE check beside the NDVI check (NV) is set to 0.11). This first assessment gives a CE of 1.9 % and an OE of 12.4 % for WSR 0.5 (entry 0). Notice that the user requirements (Table 1) define a maximum OE and CE

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Issue: I1.10 of 15%. The VALUE threshold is subsequently increased to 0.15 which results in an increased CE (entry 1), and decreased to 0.13 which results in increased OEs (entry 2). Because the CE or OE errors increase, these settings were not retained. Increasing the HUE threshold to 110 and 120 (entry 3 and 4 vs entry 0) decreases the commission error with a slight increase of omission error. Further increasing the HUE threshold to 125 (entry 5) slightly improves the commission error but also the omission errors increases and therefore this option was not retained. Subsequently the NDVI threshold was decreased to 0.3 (entry 6 vs entry 4) resulting in both increased CE and OEs. Finally the NV value was increased showing better OEs, however the CE increased (entry 7 vs entry 4). The last two settings were therefore not retained, so the final settings are according entry 4, which are identical to the settings for the PROBA-V 1 km collection except for the HUE threshold which is increased to 120. 3.2.7.9 Water body occurrence estimation

To qualify the occurrence of the detected WBs, a Water Body Occurrence (WBO) is calculated for each detected WB pixel in each dekad. This measure gives an idea about the permanency or seasonality of the detected WBs. The WBO is added as a Quality Layer (QUAL) in the final output product. To obtain a WBO map for each dekad, per pixel statistics are calculated by temporal- sequential processing of the available dekads. If more than sixty-four2 cloud free MC10 composite observations are available, only the last 64 cloud free observations including the actual observation are used for calculating the statistics. This means that for the first dekads (observation started Oct. 2013) there are none or only a few dekads available for statistics. The per-pixel temporal-sequential statistics are calculated over the available observation dekads (max 64) and can be summarized as:

− Total number of temporal cloud free observations (ntObs)* − Total number of temporal WB detections (ntWBs) − Maximum number of continuous temporal WB detections (mctWBs) * Note that after some time when many dekads are available ntObs will become fixed to 64. From these statistics, the frequency of detected WBs can be calculated as: TotalnumberoftemporalWBdetections(ntWBs) WBf = 100(%) (16) Totalnumberoftemporalcloudfreeobservations(ntObs)

As shown in Figure 23, the calculated WB frequency (WBf) and the maximum number of continuous temporal WB detections (mctWBs) can be visualized in a 2D scatter plot which, in his turn, is used for defining the occurrence threshold levels. The different occurrence levels are

2 To get an idea of the WB occurrence the observation period over which the statistics are calculated has to be long enough. An observation period over two years requires 72 dekads. However, for computational reason only 64 dekads are observed, information which can be stored in one “long word” (64 bits). This can be justified by the fact that per pixel not each dekad holds valid data due to cloud or cloud shadow, snow or an invalid SZA. This results in an observation period for most, if not all, of the pixels of more than 2 years. Document-No. CGLOPS2_ATBD_WB300m_V1 © C-GLOPS2 consortium

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Issue: I1.10 indicated by the different colored regions in the figure. The slope and position of the four threshold lines, marked L, M, H and VH, define the WBO threshold levels and are designed as such in the first phase. These thresholds were defined independently of the calibration areas. They were merely defined by common sense reasoning, i.e. WBs that appear with a high frequency (WBf) or with long continuous observation periods (mctWBs) are most likely stable WBs that exist for a longer period and therefore have a higher occurrence than those with lower frequency (WBf) of shorter continuous observation periods (mctWBs). The advantage of defining the thresholds in this way is that they can be easily adapted if needed. The defined occurrence threshold levels can be expressed as:

푉푒푟푦 퐿표푤 = (푚푐푡푊퐵푠 > 0) & (푚푐푡푊퐵푠 < (2 + 훼퐿 ∗ 푊퐵푓)) (17)

퐿표푤 = (푚푐푡푊퐵푠 ≥ (2 + 훼퐿 ∗ 푊퐵푓)) & (푚푐푡 푊퐵푠 < (3 + 훼푀 ∗ 푊퐵푓)) (18)

푀푒푑𝑖푢푚 = (푚푐푡푊퐵푠 ≥ (3 + 훼푀 ∗ 푊퐵푓)) & (푚푐푡 푊퐵푠 < (4 + 훼퐻 ∗ 푊퐵푓)) (19)

퐻𝑖푔ℎ = (푚푐푡푊퐵푠 ≥ (4 + 훼퐻 ∗ 푊퐵푓)) & (푚푐푡 푊퐵푠 < (5 + 훼푉퐻 ∗ 푊퐵푓)) (20)

푉푒푟푦 퐻𝑖푔ℎ = 푚푐푡푊퐵푠 ≥ (5 + 훼푉퐻 ∗ 푊퐵푓) (21) 푃푒푟푚푎푛푒푛푡 = 푊퐵푓 ≥ 95 (22) Where αx is the slope of the threshold line x, e.g.

5−0 훼푉퐻 = (23) 0−60 The red line (o1) shows the minimum WBf in relation to the mctWBs. The slope depends on the maximum number of observations (ntObs) made which in this case is thirty-one3 when all dekads have valid data. The plot shows that a certain pixel is classified as a ‘Very High’ WBO if it is identified as WB in at least five consecutive dekads (point ‘m’ in the plot). Note that this number is independent of the ntObs. As an example, if a pixel was detected as a WB in three consecutive dekads (with nObs = 31) its WBf will be 9.7 % and therefore will have a WBO “Medium” (point “a”). If this pixel would have subsequently four additional WB detections in separate single dekads its WBf will rise to 22.6 % and its WBO will increase to “High” (point “b”). Note also that if pixels have a WBf of at least 95% their WBO will be “Permanent”.

3 31 MC10 images were used, from 21 Oct. 2013 till 21 Aug. 2014. Document-No. CGLOPS2_ATBD_WB300m_V1 © C-GLOPS2 consortium

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Figure 23: 2D scatter plot of the WB frequency (WBf) and the number of continuous temporal WB observations (mctWBs) for the Rift Valley test area calculated for the 31 available dekads. The Water Body Occurrence levels are indicated by the different colored areas.

3.2.8 Limitations

Although minimization of omission and commission errors is achieved by the use of additional data layers, WBPM, VSM, PGM, MWEM and NDVI they sometimes are inevitably. An overview of reasons for omission and commission errors is listed in Table 10.

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Table 10: Omission and commission errors are caused by several reasons.

Omission errors due to: 1 Small sized WBs in the order of the size of one PROBA-V pixel; 2 WBs with spectral properties that fall outside the defined thresholds; 3 New WBs not covered by the MWEM (32 years, 1984-2015).

Commission errors due to: 1 Cloud shadow caused by thin undetected clouds especially over dark dense vegetation and in mountainous areas; 2 Dark soils especially volcanic soils (which are not masked by the VSM); 3 Dark areas caused by anthropogenic activity, e.g. heavy industry; 4 Dark areas caused by shadow, e.g. high buildings in large cities; 5 Anthropogenic structures with spectral signatures equal to WBs, e.g. some agricultural fields, build-up areas; 6 Natural surfaces with spectral signatures very close to WBs, e.g. salt lakes;

To minimize confusion the user should use the product with attention to the QUAL which reflects the history of detected WBs.

3.3 QUALITY ASSESSMENT

See CGLOPS2_QAR_WB300m_V1 upon public release.

3.4 RISK OF FAILURE AND MITIGATION MEASURES

In case the quality of the PROBA-V sensor degrades, the spectral response correction needs to be adapted and the defined thresholds for Water Body detection need to be updated if necessary.

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4 REFERENCES

GCOS-200 (2016). Systematic Observation Requirements for Satellite-based Products for Climate Supplemental details to the satellite-based component of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC - 2016 Update, WMO, Geneva, Switzerland. http://www.wmo.int/pages/prog/gcos/Publications/gcos-200.pdf Irish, R. R., Barker, J. L., Goward, S. N., & Arvidson, T. J., (2006). Characterization of the Landsat- 7 ETM Automated Cloud-Cover Assessment (ACCA) algorithm. Photogrammetric Engineering & Remote Sensing, 72, 1179−1188. NSIDC (2014): Global Land Ice Measurements from Space (GLIMS) glacier database. Compiled and made available by the international GLIMS community and the National Snow and Ice Data Center, Boulder CO, U.S.A. (DOI:10.7265/N5V98602). http://glims.colorado.edu/glacierdata/ Pekel, J.-F., Bogaert, P., Adans, S., Rasson, J. P., & Defourny, P., (2005). STEREO : GlobalWatch - Détection, Traitement automatique de séries temporelles optiques pour la cartographie et la et la détection de changement. Intermediary report n°2 (Vol. Intermedia). Pekel, J.-F., Vancutsem, C., Bastin, L., Clerici, M., Vanbogaert, E., Bartholomé, E., & Defourny, P., (2014). A near real-time water surface detection method based on HSV transformation of MODIS multi-spectral time series data. Remote Sensing of Environment, 140, 704–716. (doi:10.1016/j.rse.2013.10.008)

Pekel, J.-F., Cottam, A., Gorelick, N., Belward, A.S., (2016). High-resolution mapping of global Commenté [BL14]: RID 1391 surface water and its long-term changes. Nature 540, 418-422. (doi:10.1038/nature20584) Rahman, H. and Dedieu, G., (1994). SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum. International Journal of Remote Sensing, 15(1): 123-143 Roy, D.P.; Boschetti, L. Southern Africa validation of the MODIS, L3JRC, and GlobCarbon burned- area products. IEEE Trans. Geosci. Remote Sens. 2009, 47, 1032–1044 USGS (2008), GLSDEM, 90m scene GLSDEM_p123r024_utmz13, Global Land Cover Facility, University of Maryland, College Park, Maryland. (http://glcf.umd.edu/data/glsdem/) Vancutsem, C., Pekel, J.‐F., Bogaert, P., & Defourny, P. (2007). Mean Compositing, an alternative strategy for producing temporal syntheses. Concepts and performance assessment for SPOT VEGETATION time series. International Journal of Remote Sensing, 28(22), 5123– 5141. doi:10.1080/01431160701253212

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