Copernicus Global Land Operations – Lot 1 Date Issued: 05.11.2020

Issue: I1.10

Copernicus Global Land Operations “Vegetation and Energy” ”CGLOPS-1” Framework Service Contract N° 199494 (JRC)

QUALITY ASSESSMENT REPORT LAND SURFACE TEMPERATURE

VERSION 2.0

Issue I1.10

Organization name of lead contractor for this deliverable: IPMA

Book Captain: João Paulo Martins Contributing Authors: Isabel Trigo Sandra Coelho e Freitas Sandra Gomes

Copernicus Global Land Operations – Lot 1 Date Issued: 05.11.2020

Issue: I1.10

Dissemination Level PU Public PP Restricted to other programme participants (including the Commission Services) X 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

Book captain: João Paulo Martins Sign Date 05.11.2020

Approval: Roselyne Lacaze Sign Date. 12.11 2020

Endorsement: Michael Cherlet Sign Date

Distribution: Public

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Change Record

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

07.02.2020 All Initial version I1.00

All Revision after external review I1.00 05.11.2020 I1.10 69-75 Add intercomparison with Sentinel-3 SLSTR LST

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TABLE OF CONTENTS

Executive Summary ...... 14

1 Background of the document ...... 17

1.1 Scope and Objectives...... 17

1.2 Content of the document...... 17

1.3 Related documents ...... 17 1.3.1 Applicable documents ...... 17 1.3.2 Input ...... 18 1.3.3 Output ...... 18

2 Review of Users Requirements ...... 19

3 Review of the Copernicus Global Land LST and changes in v2.0 ...... 22

Change ...... 25

4 Evaluation of the new product version impact ...... 26

4.1 Overall procedure ...... 26

4.2 Impact of Dynamic FCOVER ...... 26 4.2.1 GOES-16 ...... 26 4.2.2 Himawari-8...... 36 4.3 Impact of new calibration Coefficients ...... 41 4.3.1 GOES-16 ...... 41 4.3.2 Himawari-8...... 44 4.4 Combined effect of New FCOVER and New Coefficients ...... 47 4.4.1 GOES-16 ...... 47 4.4.2 Himawari-8...... 50 4.5 Spatial Coverage ...... 53

4.6 Change of Time slots used for MSG ...... 56

5 Impact on validation statistics ...... 58

5.1 In situ comparisons ...... 58 5.1.1 Station descriptions ...... 60 5.1.2 Evaluation metrics ...... 64 5.1.3 Results ...... 64 5.2 Comparison with Sentinel-3 SLSTR LST ...... 69

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5.2.1 Spatial comparisons ...... 70 5.2.2 In situ comparisons ...... 74

6 Overlapping regions ...... 76

6.1 Intersection between GOES and MSG (A1) ...... 76

6.2 Intersection between MSG 0° and IODC (A2) ...... 79

6.3 Intersection between IODC and Himawari (A3) ...... 82

7 Conclusions ...... 86

8 Recommendations ...... 90

9 References ...... 91

ANNEX 1 - In situ station representativity ...... 93

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List of Figures

Figure 1: Main properties of the calibration database atmospheric profiles used in the determination of the model coefficients for GOES and Himawari-8. (a) Total Column of Water Vapor (TCWV) distribution; (b) 푻푺풌풊풏 distribution; (c) bivariate 푻푪푾푽/푻푺풌풊풏 distribution and (d) geographical distribution (adapted from Martins et al., 2016)...... 23 Figure 2: Illustration of the direction and duration of scanning of GEO satellites for the t UTC data...... 24 Figure 3 – Comparison of Fraction of Vegetation Cover estimates used as input for CGLOPS v1.2. In the top row are the (a) median LSA-SAF FVC for 10-20 January 2019, (b) FCOVER used for GOES-16 – static value – and (c) their difference. In the bottom row, the corresponding estimates for 10-20 July 2019. All data are re-projected on a regular 0.1º grid...... 27 Figure 4 – Impact of the introduction of CGLOPS FCOVER in v2.0, for January (top row) and July (bottom row). In the left, the FCOVER values used for v2.0 emissivity estimation. Central panels illustrate the difference between the new values and the static values used in v1.2. Right panels show the difference between LSA-SAF FVC product and the CGLOPS FCOVER product. ... 28 Figure 5 – Differences in IR1 emissivity in product v1.2. (a) 흐ퟏퟎ. ퟖ for MSG (b) 흐ퟏퟏ. ퟐ for GOES using static FCOVER; (c) Difference between 흐ퟏퟎ. ퟖ (MSG) and 흐ퟏퟏ. ퟐ (GOES) using static FCOVER (v1.2). (d), (e) and (f) show the corresponding values for July...... 29 Figure 6 – Impact of the introduction of CGLOPS FCOVER in 흐푰푹ퟏ, for the January and July periods. New values in v2.0 are shown in the left panels; center panels represent the difference to v1.2 values and right panels represent the difference between the emissivity of the corresponding channel in MSG and 흐ퟏퟏ. ퟐ(GOES)...... 30 Figure 7 – Same as Figure 5, but for IR2 emissivity...... 31 Figure 8 – Same as Figure 6, but for IR2 emissivity...... 31 Figure 9 – Impact of the introduction of CGLOPS FCOVER on GOES-16 LST. Left column shows the median LST using static emissivities for the dekad, while the right column represents the median of the differences due to the introduction of the dynamic emissivity. (a) and (b) January, timeslot 03UTC; (c) and (d) January, timeslot 15UTC; (e) and (f) July, timeslot 03UTC; (g) and (h) July, timeslot 15UTC...... 33 Figure 10- Impact of the introduction of CGLOPS FCOVER on GOES-16 LST uncertainty. Left column shows the median of LST uncertainty using static emissivities for the dekad, while the right column represents the change to that value due to the introduction of the dynamic emissivity. (a) and (b) January, timeslot 03UTC; (c) and (d) January, timeslot 15UTC; (e) and (f) July, timeslot 03UTC; (g) and (h) July, timeslot 15UTC...... 34

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Figure 11– Median diurnal cycle of the (spatial) mean differences between GOES-16 and MSG retrievals over South America. Blue curve represents the difference obtained using static vegetation, while the red curve represents the difference obtained with dynamic FCOVER. .. 35 Figure 12– (a) Static FCOVER values used in LST v1.2 (b) CGLOPS FCOVER for the second dekad of January (c) CGLOPS FCOVER for the second dekad of July...... 37 Figure 13 – Impact of the introduction of CGLOPS FCOVER on Himawari-8 emissivity. (a) IR1 emissivity using static vegetation; (b) Difference in IR1 emissivity for January, (c) Difference in IR1 emissivity for July, (d) IR2 emissivity using static vegetation (e) Difference in IR2 emissivity for January and (f) Difference in IR2 emissivity for July. All differences are calculated using dynamic-static emissivities for the second dekad of the given month...... 38 Figure 14 – Impact of the introduction of CGLOPS FCOVER on Himawari-8 LST. Individual time slots are the same as in Figure 9, 15 UTC corresponds to nighttime and 03 UTC corresponds to daytime...... 39 Figure 15 - Impact of the introduction of CGLOPS FCOVER on Himawari-8 LST uncertainty. Individual time slots are the same as in Figure 10, 15 UTC corresponds to nighttime and 03 UTC corresponds to daytime...... 40 Figure 16 – Impact of the change in algorithm coefficients on LST, for the GOES-16 retrieval. Individual time slots are the same as in Figure 9...... 42 Figure 17 - Impact of the change in algorithm coefficients on LST uncertainty, for the GOES-16 retrieval. Individual time slots are the same as in Figure 9...... 43 Figure 18 – Impact of the change in algorithm coefficients on LST, for the Himawari-8 retrieval. Individual time slots are the same as in Figure 9...... 45 Figure 19 – Impact of the change in algorithm coefficients in LST uncertainty, for the Himawari-8 retrieval. Individual time slots are the same as in Figure 9...... 46 Figure 20 – Combined impact of the introduction of CGLOPS FCOVER and the new set of GSW coefficients in the GOES-16 LST retrieval. Individual time slots are the same as in Figure 9. 48 Figure 21 – Combined impact of the introduction of CGLOPS FCOVER and the new set of GSW coefficients in the GOES-16 LST retrieval uncertainty. Individual time slots are the same as in Figure 9...... 49 Figure 22 – Combined impact of the introduction of CGLOPS FCOVER and the new set of GSW coefficients in the Himawari-8 LST retrieval. Individual time slots are the same as in Figure 9...... 51 Figure 23 - Combined impact of the introduction of CGLOPS FCOVER and the new set of GSW coefficients in the Himawari-8 LST retrieval uncertainty. Individual time slots are the same as in Figure 9...... 52

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Figure 24 – Areas covered by GEO satellites. From left to right: GOES, MSG 0°, MSG IODC and Himawari...... 53 Figure 25 – LST for the 15/01/2020 12UTC time slot, for product version V1.2 (top) and V2.0 (bottom)...... 54 Figure 26 – Comparison of the percentage of valid (clear-sky) land pixels for the period from 15 to 30 January 2020 (blue bars) and the percentage of land pixels that are covered by at least one sensor (orange). Black bars denote the +/- standard deviation of the percentage interval for the same period...... 55 Figure 27 – Time series of the LST differences resulting from choosing time slot T (v2.0) with respect to T-15 min (v1.2) in a 20x20 pixels area over the MSG disk (represented as the blue square in the map on the left). Colors represent the MSG 0º LST for the time slot 20200118 1200UTC (units ºC)...... 56 Figure 28 – Time series of LST obtained between 15 April 2019 and 25 April 2019, for nearest pixel of station OZFX_CBL, both with v1.2 (light blue) and v2.0 (orange). Also shown are the corresponding estimates for the pixel actually used for the collocation (“coloc” in red for v2.0 and in dark blue for v1.2). In situ values are represented in black...... 65 Figure 29 – Validation statistics obtained using the neighboring 5x5 pixels around the station (which lies within the central pixel in the diagrams). The top row shows the statistics obtained with v2.0 estimates, while those obtained with v1.2 are shown in the lower row...... 66 Figure 30 – High resolution visible imagery from Google Earth Engine around station OZFX_CBL. The station is located over the red dot. Gray transparent squares represent a 3x3 pixel window around the station...... 66 Figure 31 – Scatterplots of the matchups between CGLOPS LST (v1.2 in the left, v2.0 in the right) and in situ values from station OZFX_CBL...... 67 Figure 32 – Comparison of CGLOPS LST with SLSTR LST for December, January, February (DJF) 2019-2020. The left column shows the mean CGLOPS – SLSTR differences (in K) for daytime v2.0, daytime v1.2, night time v2.0 and night time v1.2. In the right column, the corresponding number of matchups are shown...... 72 Figure 33 – Comparison of CGLOPS LST with SLSTR LST for June, July, August (JJA) 2020. The left column shows the mean CGLOPS – SLSTR differences (in K) for daytime v2.0, daytime v1.2, night time v2.0 and night time v1.2. In the right column, the corresponding number of matchups are shown...... 73 Figure 34 – Time series of the matchups between SLSTR LST, CGLOPS LSTs (v1.2 and v2.0) and in situ LST for the case of station OZFX_CAL, for the period September to December 2019. 74 Figure 35 – Scatterplots comparing SLSTR LST (left), CGLOPS v1.2 (center) and CGLOPS v2.0 (right) with in situ matchups, using all stations in section 5.1.1, for the period September-

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December 2019 (for which all datasets were available). Results are separated into day (top) and night (bottom). Colors represent datapoint density...... 75 Figure 36 – Areas covered by the different satellite missions, taken from the LST V2.0 file corresponding to the timeslot 2020-8-15 15:00 UTC. In magenta, the limits of the overlapping areas under analysis are shown...... 76 Figure 37 – Mean differences between LST from MSG 0º and GOES-16, for January 2020 (top) and July 2020 (bottom). Differences are shown every 3h (UTC)...... 78 Figure 38 – Scatterplots comparing mean LST from MSG with mean LST from GOES-16 in the overlapping region A1. Mean LSTs correspond to the values used in Figure 37 (i.e. every 3h). Scatterplots are colored according to VZA: on the left using the MSG 0º VZA, on the right using the GOES-16 VZA. In the top row are shown the results for January and on the bottom row, results for July...... 79 Figure 39 – Mean differences between LST from MSG 0º and IODC, for January 2020 (top) and July 2020 (bottom). Differences are shown every 3h (UTC)...... 81 Figure 40 – Scatterplots comparing mean LST from MSG 0º with mean LST from IODC in the overlapping region A2. Mean LSTs correspond to the values used in Figure 39 (i.e. every 3h). Scatterplots are colored according to VZA: on the left using the MSG 0º VZA, on the right using the IODC VZA. In the top row are shown the results for January and on the bottom row, results for July...... 82 Figure 41 – Mean differences between LST from IODC and Himawari, for January 2020 (top) and July 2020 (bottom). Differences are shown every 3h (UTC)...... 84 Figure 42 – Scatterplots comparing mean LST from MSG IODC with mean LST from Himawari in the overlapping region A3. Mean LSTs correspond to the values used in Figure 36 (i.e. every 3h). Scatterplots are colored according to VZA: on the left using the MSG 0º VZA, on the right using the IODC VZA. In the top row are shown the results for January and on the bottom row, results for July...... 85

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List of Tables Table 1 – GCOS requirements for Land Surface Temperature as Essential Climate Variable [GCOS- 154, 2011] ...... 20 Table 2 – CGLOPS uncertainty levels for LST products...... 20 Table 3 – WMOs requirements for global LST products (From http://www.wmo- sat.info/oscar/requirements); G=goal, B=breakthrough, T=threshold...... 21 Table 4 – Satellite channels used for LST estimation using the GSW algorithm...... 22 Table 5 – Summary of changes in LST v2.0...... 25 Table 6 – ASTER bands used to estimate surface broadband emissivity ...... 60 Table 7 – List of BSRN stations used in this report. Land cover information was retrieved from http://maps.elie.ucl.ac.be/CCI/viewer/index.php, and Köppen-Geiger climate classification data was obtained from http://koeppen-geiger.vu-wien.ac.at/...... 60 Table 8 - List of SURFRAD stations used in this report. Land cover information was retrieved from http://maps.elie.ucl.ac.be/CCI/viewer/index.php, and Köppen-Geiger climate classification data was obtained from http://koeppen-geiger.vu-wien.ac.at/ ...... 61 Table 9 – List of GBOV stations used in this report. Land cover information was retrieved from http://maps.elie.ucl.ac.be/CCI/viewer/index.php, and Köppen-Geiger climate classification data was obtained from http://koeppen-geiger.vu-wien.ac.at/...... 62 Table 10 - List of OZFlux stations used in this report. Land cover information was retrieved from http://maps.elie.ucl.ac.be/CCI/viewer/index.php, and Köppen-Geiger climate classification data was obtained from http://koeppen-geiger.vu-wien.ac.at/...... 62 Table 11 - List of EFDC stations used in this report. Land cover information was retrieved from http://maps.elie.ucl.ac.be/CCI/viewer/index.php, and Köppen-Geiger climate classification data was obtained from http://koeppen-geiger.vu-wien.ac.at/...... 63 Table 12 - Summary statistics for the 32 stations used in this report, for CGLOPS LST v1.2 and for v2.0 (differences are CGLOPS LST – in situ LST). Stations are grouped by land cover type. Global summary statistics, using all matchups from all stations, are provided in the last row. Accuracy (μ) compliance with requirements in Table 1 is colored as: “within required accuracy” as green, “within achievable accuracy” as yellow, “above achievable accuracy” as red. Uncertainty (RMSD) compliance with Table 2 is colored as: “within target uncertainty” in green; “within threshold uncertainty” in yellow, “above threshold uncertainty” in red...... 68

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List of Acronyms ABI Advanced Baseline Imager AD Applicable Document AHI Advanced Himawari Imager ASCII American Standard Code for Information Interchange ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer ATBD Algorithm Theoretical Basis Document BSRN Baseline Surface Radiation Network CEOS Committee on Earth Observation Satellites CGLOPS Copernicus Global Land Operations CSV Comma-Separated Values DJF December – January - February EFDC European Flux Database Cluster EUMETSAT European Agency for the Exploitation of Meteorological Satellites FCOVER Fraction of Vegetation Cover FLUXNET Flux Network GBOV Copernicus Ground-Based Observations for Validation GCOS Global Climate Observing System GED Global Emissivity Data GEO Geostationary satellites GEWEX Global Energy and Water Exchanges GOES Geostationary Operational Environmental Satellite GSW Generalized Split-Window IODC Indian Ocean Data Coverage IPMA Instituto Português do Mar e da Atmosfera IR Infra-Red JJA June-July-August JMA Japan Meteorological Agency JRC Joint Research Council LSA-SAF EUMETSAT Satellite Application Facility on Land Surface Analysis LST Land Surface Temperature LST10 Land Surface Temperature-10 daily composite LPV Land Product Validation MSG Meteosat Second Generation MTSAT Multifunctional Transport Satellite NetCDF Network Common Data Form NOAA National Oceanic and Atmospheric Administration NRT Near Real Time OzFlux Australian and New Zealand Flux Research Monitoring PROBA-V Project for On-Board Autonomy - Vegetation PUM Product User Manual RMSD Root Mean-Squared Difference

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SAF Satellite Application Facility SEVIRI Spinning Enhanced Visible Infra-Red Imager SLSTR Sea and Land Surface Temperature Radiometer SQE Scientific Quality Evaluation SSD Service Specifications Document SURFRAD Surface Radiation network of stations SVP Service Validation Plan TCWV Total Column Water Vapor TIR Thermal Infra-Red TOA Top of Atmosphere UTC Coordinated Universal Time VR Validation Report WGS World Geodetic System WMO World Meteorological Organization

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EXECUTIVE SUMMARY The Copernicus Global Land Operations (CGLOPS) is earmarked as a component of the Land service to operate “a multi-purpose service component” that provides a series of bio-geophysical products on the status and evolution of land surface at global scale. Production and delivery of the parameters take place in a timely manner and are complemented by the constitution of long-term time series. Quality Assessment and continuous Quality Monitoring constitute the only means of guaranteeing the compliance of generated products with user requirements: • The former concerns the new products/version which must pass an exhaustive scientific evaluation before to be implemented operationally. • The latter concerns the operational products to check their quality keeps the same level along the time. Both follow, as much as possible, the guidelines, protocols and metrics defined by the Land Product Validation (LPV) group of the Committee on Earth Observation Satellite (CEOS) for the validation of satellite-derived land products. This document reports on the impact of changes in the CGLOPS LST v2.0 with respect to previous product version (v1.2). The CGLOPS LST product combines LST estimates from sensors onboard geostationary platforms operated by different meteorological satellites agencies (GOES, operated by NOAA; MSG, operated by EUMETSAT and Himawari, operated by JMA). Both JMA and NOAA have launched their 3rd generation meteorological satellites since the beginning of CGLOPS, and the product has adapted to accommodate these changes. With the availability of the Indian Ocean Data Coverage (IODC) mission, operated by EUMETSAT, a fourth mission (MSG-1) could be used by the CGLOPS LST product, increasing spatial coverage over the Middle East and also increasing the area of overlap between different sensor’s fields of view. Harmonizing estimates from these different missions has raised the need to reconfigure the CGLOPS product, justifying a version upgrade. The product used LST estimates for the MSG 0º mission provided by the Land Surface Analysis Satellite Application Facility (LSA-SAF). Their algorithm and suite of products allowed the use of dynamic vegetation to estimate surface emissivity. The availability of a similar global dataset within CGLOPS (the 1 km FCOVER product based in PROBA-V estimates) now allows to do the same in the case of the sensors that are processed by the CGLOPS processing chain (GOES-16 and Himawari-8). In the upgrade to version v2.0, the calibration of the coefficients of the Generalized Split Windows LST retrieval algorithm used for these sensors was also recalculated. All sensors now allow the use of this algorithm, since the two adjacent thermal infrared channels for each sensor are available through EUMETCAST. Therefore, there was the need to homogenize the treatment of each sensor. A recently developed calibration methodology (Martins et al., 2016) allows a comprehensive coverage of atmospheric and surface conditions and was adopted to recalibrate algorithm coefficients.

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In the new v2.0 version, the MSG timeslot used to produce the hourly LST estimates was changed with respect to previous versions. This new configuration minimizes acquisition time differences within each file, but it introduces a discontinuity in the time series obtained over the MSG 0º mission’s field of view. Therefore, users should not mix product versions while working with long time series. The assessment of the impact of these changes is performed first individually and then as a whole. Two representative 11-day periods in January and July 2019 were used, so that seasonal changes in vegetation and atmospheric conditions are properly accounted for. In the case of GOES-16, some additional attention is given to the area of overlap between the GOES-16 and MSG 0º disks, given this is an area which allows an in-depth discussion of the changes in vegetation cover, its effects on surface emissivity estimation, and their impact on the final LST estimate. Results indicate that the new v2.0 version LST may differ on average up to +/- 2 K with respect to the previous version (v1.2), with some areas over Australia where differences actually exceed 2 K, due to cumulative effects of all changes. In general, LSTs are increased when CGLOPS FCOVER is lower than the (static) prescribed values, which usually happens during the dry season of a given area. If the opposite happens (i.e. underestimation of FCOVER in the previous version), the impact is normally not as high since, for moister atmospheres, the algorithm is less sensitive to changes in emissivity. Thus, impacts due to FCOVER are in general associated to drier situations. As for the impact of changing algorithm coefficients, it affects GOES-16 and Himawari-8 differently, since GOES-16 initial calibration already used a very similar methodology to the current product version (however still using a different set of atmospheric profiles). As for Himawari-8, the calibration used in the previous product version was less comprehensive and did not include some extreme situations such as very dry and warm atmospheres/surfaces. Therefore, the new calibration strategy has a significant impact in retrievals over the drier areas of Australia. An assessment of the impact on product uncertainty provided to the users is also carried out, showing increases up to 1 K over drier areas and a decrease where vegetation cover was underestimated in the previous product version. There are also decreases over areas of the Himawari-8 disk where retrievals are performed over moister atmospheres with high viewing angles, which may be not realistic (uncertainties are indeed high in those situations). Since IODC now covers the Middle East, an assessment of the increase in spatial coverage was performed, indicating an increase from 28.2% (in v1.2) to 30.5% (in v2.0) in the percentage of total land pixels containing valid LST estimates. The number of land pixels that are within the field of view of, at least, one of the sensors used in CGLOPS LST shows a sharper increase from 53.2% in v1.2 to 60.0% in v2.0. There is some temporal variability in these values, which is attributable to the amount of clear sky scenes, the wobbling of MSG-1 and number of measurements excluded by the quality control. Finally, an extensive quality assessment was performed by comparing LST estimates from versions v1.2 and v2.0 to in situ estimates, obtained from the set of stations of the Baseline Surface Radiation Network (BSRN) and Surface Radiation networks (SURFRAD), European Flux Database Cluster (EFDC), the Copernicus Ground-Based Observations for Validation (GBOV) and from the Australian and New Zealand Flux Research Monitoring (OZFlux) for the period from January to December Document-No. CGLOPS1_QAR_LST-V2.0 © C-GLOPS Lot1 consortium Issue: I1.10 Date: 05.11.2020 Page: 15 of 125

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2019. Results indicate an overall improvement of the statistics, especially in terms of the magnitude of the median differences, which is lowered from -0.3 K to -0.0 K, while the median of the absolute residuals and the Root Mean Squared Difference, remain constant (1.4 K and 2.9 K, respectively). Spatial and temporal consistency were further assessed by thorough comparisons with an independent satellite LST product – the official ESA LST, derived from SLSTR onboard Sentinel-3 – , as well as through comparisons between LSTs derived from individual geostationary missions used in CGLOPS LST v2.0 within the overlapping regions. Results reveal the importance of differences in viewing and illumination geometries in explaining discrepancies between LST estimates from different remote sensors.

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1 BACKGROUND OF THE DOCUMENT

1.1 SCOPE AND OBJECTIVES

The document describes the impact of the changes made upon the upgrade of CGLOPS LST to its version v2.0. Changes include: • the inclusion of CGLOPS FCOVER as input to the processing chain for GOES-16 and Himawari-8 retrievals; • Recalibration of the Generalized Split-Window LST retrieval algorithm for these sensors; • Inclusion of MSG IODC LST estimates in the global product; • Change in the timeslot used for MSG. The assessment is performed using sets of retrievals for 11-day periods in January and July 2019, where the impact of each change is studied separately. An overall assessment of the total impact is performed using the same periods and a set of retrievals including all changes. Finally, the quality of the new version is assessed by comparing estimates from product versions v1.2 and v2.0 with in situ data from a comprehensive set of 32 stations from the BSRN, SURFRAD, GBOV, OZFlux and EFDC networks. The comparison was made for the period January to December 2019, when all datasets (satellite and in situ) were available. The analysis is complemented with a consistency check against the Sentinel-3 SLSTR LST product.

1.2 CONTENT OF THE DOCUMENT

This document is structured as follows:

• Chapter 2 recalls the users’ requirements, and the expected performance • Chapter 3 reviews the changes made in this product version and the expected impact of each change • Chapter 4 describes the methodology and presents the results of the impact for each satellite, and the impact in spatial coverage. • Chapter 5 presents the impact of the changes in the comparison with in-situ measurements and with Sentinel-3 SLSTR LST products. • Chapter 6 focuses on the overlapping regions • Chapter 7 summarizes the main conclusions of the study • Chapter 8 presents some recommendations

1.3 RELATED DOCUMENTS

1.3.1 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 Document-No. CGLOPS1_QAR_LST-V2.0 © C-GLOPS Lot1 consortium Issue: I1.10 Date: 05.11.2020 Page: 17 of 125

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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 AD3: GIO Copernicus Global Land – Technical User Group – Service Specification and Product Requirements Proposal – SPB-GIO-3017-TUG-SS-004 – Issue I1.0 – 26 May 2015.

1.3.2 Input

Document ID Descriptor GIOGL1_SSD Service Specifications of the Global Component of the Copernicus Land Service. CGLOPS1_SVP Service Validation Plan of the Copernicus Global Land Service CGLOPS1_ATBD_LST-V2.0 Algorithm Theoretical Basis Document for Land Surface Temperature Version 2.0 CGLOPS1_ATBD_LST10-V2.0 Algorithm Theoretical Basis Document of the 10-daily Land Surface Temperature product Version 2.0 CGLOPS1_ATBD_FCOVER1km- Algorithm Theoretical Basis Document of the V2 Collection 1km Version 2 FCOVER product. GIOGL1_VR_LST Quality assessment report containing the results of the scientific validation of the Land Surface Temperature V1.2 product

1.3.3 Output

Document ID Descriptor CGLOPS1_PUM_LST-V2.0 Product User Manual summarizing all information about the Land Surface Temperature V2.0 product

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2 REVIEW OF USERS REQUIREMENTS

According to the applicable document [AD2] and [AD3], the user’s requirements relevant for the Land Surface Temperature product are:

• Definition: o temperature of the apparent surface of land (bare soil or vegetation) o physical unit: [K]

• Geometric properties: o pixel size of output data shall be defined on a per-product basis so as to facilitate the multi-parameter analysis and exploitation o The target baseline location accuracy shall be 1/3 of the at-nadir instantaneous field of view o Pixel co-ordinates shall be given for centre of pixel

• Geographical coverage: o Geographic projection: lat long, o Geodetical datum: WGS84 o Accuracy pixel size: min 10 digits o Coordinate position: pixel centre o Global window coordinates: ▪ Upper Left: 180°W, 75°N ▪ Bottom Right: 180°E 56°S

• Ancillary information: o The number of measurements per pixel used to generate any synthesis product o The per-pixel date of the individual measurement 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 document “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”. GCOS-#154, 2011” (Table 1).

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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.

Table 1 – GCOS requirements for Land Surface Temperature as Essential Climate Variable [GCOS- 154, 2011]

Variable: Land Surface Horizontal Vertical Temporal Accuracy Stability Temperature Resolution Resolution Resolution Requirements 1 km N/A 1 hour 1 K N/A

Currently achievable 5 km N/A 1 hour 2 – 3 K 1 – 2 K performance

Additionally, the Technical User Group of the Copernicus Global Land Service [AD3] has recommended the uncertainty levels for LST (Table 2). Furthermore, there is a general requirement for all the Global Land products to be aligned in terms of pixel size, and therefore GEO-derived products should be distributed with a resolution of 5/112°. In addition to the user’s requirements expressed for LST in the applicable document [AD2], the rationale to have a 10-days frequency LST is to ease the joint use of CGLOPS products by providing them with the same temporal resolution to end-users.

Table 2 – CGLOPS uncertainty levels for LST products.

Optimal Target Threshold

LST 1 K 2 K 4 K

• Additional user requirements The GCOS requirements are supplemented by application specific requirements identified by the WMO (Table 3). These specific requirements are defined at goal (ideal), breakthrough (optimum in terms of cost-benefit), and threshold (minimum acceptable).

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Table 3 – WMOs requirements for global LST products (From http://www.wmo- sat.info/oscar/requirements); G=goal, B=breakthrough, T=threshold.

Accuracy (K) Spatial resolution (km) Temporal resolution Application G B T G B T G B T GEWEX 1 2 4 50 100 250 3h 6h 12h Global Weather 0.5 1 4 5 15 250 30min 3h 6h Prediction Regional Weather 1 2 4 1 5 20 30min 1h 6h Prediction Hydrology 0.3 0.6 3 0.01 0.3 250 1h 6h 7days Agricultural 0.3 0.6 2 0.1 0.5 10 1h 3h 3days Meteorology Nowcasting 0.5 1 3 1 5 30 10min 30min 1h Climate 1 1.3 2 1 10 500 24h 2days 3days

A word of caution must be added in case of using in-situ data to evaluate the LST accuracy, since there are very few ground stations designed for the validation of satellite LST products. Given the high spatial variability of LST, significant mismatches will always occur unless the station data are representative of the satellite pixel. On top of radiometric measurements of surface leaving radiation and downward fluxes within the infrared domain, a careful characterization of the land cover and surface emissivity (in the same band operated by the station radiometers) of the area surrounding the station is required.

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3 REVIEW OF THE COPERNICUS GLOBAL LAND LST AND CHANGES IN V2.0

The Land Surface Temperature (CGLOPS LST) product is retrieved from a constellation of geostationary satellites, operated by different agencies (Freitas et al., 2013). Since the operational missions by each agency have evolved since the original design of the CGLOPS LST product, some adjustments had to be made, justifying a new product version. The original product used data from Meteosat Second Generation (MSG), Geostationary Operational Environmental Satellite (GOES) and the Multi-Function Transport Satellite – MTSAT. Since then, MTSAT was replaced by a third generation platform in November 2015, the Himawari-8 satellite; and the same happened in the GOES East coverage, with GOES-16 entering in operations in December 2017. While the MSG nominal 0º mission remained unchanged, the first MSG (Meteosat-8) was moved to the 41.5° E longitude, ensuring the Indian Ocean Data Coverage (IODC) mission. This new mission allows filling in the data gap over the Middle East in previous product version, which existed since no suitable geostationary instrument covering that area was available when CGLOPS LST was initially conceived. Both GOES-16 and Himawari-8 allow the use of a more accurate LST retrieval algorithm when compared to their predecessors, since the necessary channels for the Generalized Split Windows (GSW) algorithms are available [CGLOPS1_ATBD_LST-V2.0]. The Dual Algorithm had to be used for the previous generation sensors, with a simpler formulation using one Thermal InfraRed (TIR) channel during daytime and a combination of this channel with the Middle InfraRed (MIR) channel during night time for increased accuracy.

Table 4 – Satellite channels used for LST estimation using the GSW algorithm.

MSG/SEVIRI GOES-16/ABI Himawari-8/AHI

TIR1 10.0 – 11.5 m 10.8 – 11.6 m 10.8 – 11.6 m

TIR2 11.2 – 12.8 m 11.8 – 12.8 m 11.7 - 13.1 m

Since the inclusion of Himawari-8 in product version v1.2, research on a more optimized way to calibrate a GSW algorithm such as the one used in CGLOPS LST has been published (Martins et al., 2016). This study proposed a methodology to calibrate the LST retrieval algorithm covering more realistically all possible land surface, atmospheric and viewing geometry conditions, and therefore was adopted as the reference calibration methodology for CGLOPS LST v2.0. The choice of meteorological profiles used in the calibration procedure of the sensors processed within CGLOPS is illustrated in Figure 1.

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Figure 1: Main properties of the calibration database atmospheric profiles used in the determination of the model coefficients for GOES and Himawari-8. (a) Total Column of Water Vapor (TCWV) distribution; (b) 푻푺풌풊풏 distribution; (c) bivariate 푻푪푾푽/푻푺풌풊풏 distribution and (d) geographical distribution (adapted from Martins et al., 2016).

The main idea of the calibration scheme is to build a comprehensive radiative transfer simulation database of Top Of Atmosphere (TOA) radiances, to calibrate the coefficients of the model relating LST and TOA brightness temperatures. The scheme ensures that the skin temperature / TCWV phase space obtained from the joint distribution of these two parameters over a clear-sky atmospheric profile database is fully covered. Both Himawari-8 and GOES-16 were calibrated using the same database. However, since the procedure takes into account each sensor’s response functions, the coefficients cannot not be exactly the same for both sensors. Moreover, the product uses LST from LSA-SAF for the MSG (0º and IODC missions), which uses a simpler (but also comprehensive) rationale in the calibration of their operational products (Trigo et al., 2008a), and therefore the radiative transfer simulations calibration database is different than the one used here for GOES-16 and Himawari-8. Another major difference in CGLOPS LST v2.0 lies in the estimation of surface emissivity and its uncertainty. In v1.2 emissivity was estimated using the Vegetation Cover Method, i.e. for each land Document-No. CGLOPS1_QAR_LST-V2.0 © C-GLOPS Lot1 consortium Issue: I1.10 Date: 05.11.2020 Page: 23 of 125

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Issue: I1.10 cover type, bare ground and vegetation emissivities were determined from a comprehensive library of spectral emissivities (Trigo et al., 2008b). The channel emissivity for each pixel is obtained by weighting these two quantities by the fraction of vegetation cover (FCOVER). While v1.2 used predetermined (fixed) FCOVER values for each land cover type, v2.0 relies on the CGLOPS 1 km FCOVER product (Verger et al., 2014), interpolated to the sensor geostationary grid once per dekad. This allows a better translation of vegetation changes in the final product, particularly over regions with strong vegetation seasonal cycles. It also has the advantage of increasing consistency in regions covered by more than one sensor, since the LSA-SAF LST already uses dynamic emissivities in their retrieval (García-Haro et al., 2005; Roujean et al., 1992; Trigo et al., 2008b). Moreover, this also constitutes a step towards higher internal consistency among the different CGLOPS products. Finally, due to the new swath configuration of each of the sensors used in CGLOPS LST, it was decided that the timeslots used to produce the hourly files should change. The use of GOES-16 with the previous configuration would introduce acquisition time differences higher than 20 min over the same file, which could be minimized if a different choice of time slots was adopted. This is particularly important in regions where measurements from more than one sensor overlap to produce the final LST estimate. Therefore, there are discontinuities in time series obtained by combining data from v1.2 and v2.0, especially for regions over the MSG disk (the timeslot changed from T-15 min to T, T being the reference time for each file). The choice of time slots for each satellite in V2.0 is illustrated in Figure 2.

t = 0 min t = 12 min t = 12 min t = 0min

MSG-1 Himawari-8 GOES-16 MSG-4 IODC -75º E 0º E 140.7º E 41.5º E

t = 10 min t = 0 min t = 0 min t = 10 min

Figure 2: Illustration of the direction and duration of scanning of GEO satellites for the t UTC data.

Table 5 summarizes the differences between v1.2 and v2.0 in terms of the 1) difference in calibration methodologies for individual sensors; 2) estimation of surface emissivity, 3) choice of the individual time slots for each sensor and 4) Change in spatial cover due to the inclusion of the MSG IODC mission.

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Table 5 – Summary of changes in LST v2.0.

Change V1.2 V2.0

Algorithm Selection of the atmospheric profiles Selection of the atmospheric profiles Calibration used for algorithm calibration: used for algorithm calibration: Coefficients • Follows Trigo et al. (2008a) for • Follows Trigo et al. (2008a) for MSG; MSG 0º and IODC;

• Complete coverage of the 푇푠푘푖푛/ • Complete coverage of the 푇푠푘푖푛/ 푇퐶푊푉 phase space (Martins et al., 푇퐶푊푉 phase space (Martins et al., 2016) for GOES-16 and Himawari. 2016) for GOES-16 and Himawari. New optimized set of profiles.

Emissivity Static (except within the MSG disk), Dynamic, estimated using the estimation based on land cover information and Vegetation Cover Method (Trigo et al., the Vegetation Cover Method (Trigo et 2008b). The weighting between bare al., 2008b). The weighting between ground and vegetation emissivities bare ground and vegetation uses the dekadal CGLOPS 1 km emissivities uses a fixed value for each FCOVER product. In the case of the IGBP land cover type. For MSG, the MSG 0º and IODC missions, the weight weight is given by the LSA-SAF daily is given by the LSA-SAF daily FVC FVC product. product.

Individual • MSG: T-15 min • MSG: T = 0 min time slots for • GOES-16: T = 0 min • GOES-16: T = 0 min each sensor • Himawari: T = 0 min • Himawari: T = 0 min

Coverage • MSG 0º, GOES-16 and Himawari-8 • MSG 0º, MSG IODC, GOES-16 and Himawari-8

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4 EVALUATION OF THE NEW PRODUCT VERSION IMPACT

4.1 OVERALL PROCEDURE

The impact of each change in the LST processing is discussed individually, and a final impact assessment including all changes is performed. Section 4.2 discusses the impact of the dynamic FCOVER, illustrating the changes in the inputs of the processing chain and its impact on the LST estimates. Section 4.3 discusses the impact of changing the GSW algorithm coefficients. Finally, the combined effect of both changes is illustrated in section 4.4. The previous assessments are done for each satellite disk, comparing estimates obtained with a version of the processing chain including the proposed change with estimates from a version that does not include that change. Two periods were used for these comparisons: 10-20 January 2019 and 10-20 July 2019. The impact of the new product version on the spatial coverage is assessed in section 4.5, since measurements from the IODC mission are now closing the data gap over the Middle East. The impact of the change of the MSG timeslot used to produce the hourly CGLOPS LST files is detailed in section 4.6. The impact of the new version on the validation statistics is discussed in chapter 5.

4.2 IMPACT OF DYNAMIC FCOVER

4.2.1 GOES-16

In the case of GOES-16, the inclusion of a dynamic vegetation dataset as input to the processing chain allows the implementation of dynamic emissivity maps. The impact of this change may be analyzed over the overlapping region with the MSG 0º disk over South America. LSA-SAF already used dynamic vegetation in their MSG LST retrieval, and therefore systematic differences between LST estimates from each satellite could in principle be smoothed, if the effect of vegetation dynamics was included in the GOES-16 retrieval as well. Figure 3 illustrates the differences between the vegetation cover inputs used in v1.2 over the MSG/GOES overlapping region, for the above- mentioned January and July 11-day periods. This area exhibits strong seasonal vegetation variability, and therefore it is an appropriate study area to demonstrate the effect of including a dynamic FCOVER in the LST retrieval. The left panels show the FVC product of LSA-SAF, while in the center, the static FCOVER values used for the GOES disk (corresponding to the retrieval of version 1.2) are shown. It should be noted that the fields were re-projected using a nearest neighbor approach to a 0.1º regular grid covering the overlapping region of the GOES-16 and MSG 0º disks (averaging all valid pixels in the original grid in each final grid point). Since the SEVIRI geostationary projection presents a relatively low resolution over the westernmost part of the domain and no “cosmetic fill” was applied, the final LSA-SAF FCover field shows a few missing pixels over that area, which are only associated with the original product spatial resolution. For the purpose of the intercomparison presented here, we consider that no further filling is required. There are large differences between these fields (explicitly represented in the right panels). Over the Cerrado and Atlantic Forest regions (South-eastern/Eastern Brazil) the static values strongly underestimated

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FCOVER values from LSA-SAF and from CGLOPS in January (by up to 0.5), while in the dry season a negative bias of roughly the same magnitude occurred. Over the Caatinga region (the easternmost part of Brazil) the static vegetation cover had a persistent overestimation of about 0.4 (seen in the comparisons with both the LSA-SAF and CGLOPS FCOVER values), which likely resulted from an oversimplified land cover classification in the case of this very peculiar ecosystem. Over West Africa, a relatively homogeneous value of around 0.5 was prescribed in the static FCOVER field, while the LSA-SAF retrieval in July shows a sharp zonal contrast from values between 0 and 0.2 in the Sahel, to values approaching 1 in the southernmost regions of West Africa. In January the vegetation cover is much lower, so both LSA-SAF and CGLOPS products show negative differences to the prescribed static values of up to 0.4 in the Sahel.

Figure 3 – Comparison of Fraction of Vegetation Cover estimates used as input for CGLOPS v1.2. In the top row are the (a) median LSA-SAF FVC for 10-20 January 2019, (b) FCOVER used for GOES-16 – static value – and (c) their difference. In the bottom row, the corresponding estimates for 10-20 July 2019. All data are re-projected on a regular 0.1º grid. The CGLOPS FCOVER product allows the coverage of the South American region with high detail, and the values themselves are similar to the LSA-SAF FVC product estimates Figure 4 (left panels). The center panels highlight the differences between the new input (v2.0) and the static values used in v1.2. A similar pattern to the one found for the differences between LSA-SAF FVC and the static FCOVER values is clear (compare to Figure 3, panels c) and f)). In fact, if we compare the LSA-SAF FVC with the CGLOPS FCOVER values (right panels in Figure 4), some differences between these products remain, namely an underestimation of the LSA-SAF FCOVER over Amazonia of about 0.1

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Issue: I1.10 and a slight overestimation of the same magnitude over the Cerrado area. However, the improvement with respect to the previous product version is significant, with much better agreement of FCOVER estimates for the overlapping area.

Figure 4 – Impact of the introduction of CGLOPS FCOVER in v2.0, for January (top row) and July (bottom row). In the left, the FCOVER values used for v2.0 emissivity estimation. Central panels illustrate the difference between the new values and the static values used in v1.2. Right panels show the difference between LSA-SAF FVC product and the CGLOPS FCOVER product. By weighting the fraction occupied by vegetation and bare ground in each pixel, the FCOVER changes will necessarily impact channel emissivity values. The impact of the new FCOVER on the channel emissivity estimated for GOES-16 is illustrated in Figures 5 to 8. In these figures, we compare the IR1 and IR2 emissivities, for the January and July cases, and in the overlapping area of the MSG and GOES-16 disks. When comparing channel emissivities from different sensors, we should note that emissivity is a spectral quantity; therefore, each channel is centered over slightly different bands. Thus, systematic differences are expected over regions where materials with stronger spectral emissivity variability are abundant. Desert areas exhibit this kind of behavior (e.g., Hulley et al., 2009): most minerals therein show a sharp emissivity increase with increasing wavelength, namely in the spectral region between 9 and 13 µm. Therefore, if a channel is centered around a higher wavelength, its bare ground emissivity value will be higher. The impact of the changes in the IR1 channel emissivity in January is illustrated in Figure 5. Comparing 휖10.8 for MSG (Figure 5a) and 휖11.2 for GOES using static FCOVER (Figure 5b), significant differences may already be observed (Figure 5c). In particular, the Caatinga region shows a much lower value for MSG when

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Issue: I1.10 compared the value used for GOES. In the bottom row, the corresponding values for July are shown. The negative difference region over Caatinga extends further south, while Cerrado shows lower emissivities for MSG, when compared to the static values used in v1.2 for GOES. Over west Africa, higher vegetation cover in this time of year increases emissivity, therefore increasing the difference to static values used for GOES in v1.2.

Figure 5 – Differences in IR1 emissivity in product v1.2. (a) 흐ퟏퟎ.ퟖ for MSG (b) 흐ퟏퟏ.ퟐ for GOES using static FCOVER; (c) Difference between 흐ퟏퟎ.ퟖ (MSG) and 흐ퟏퟏ.ퟐ (GOES) using static FCOVER (v1.2). (d), (e) and (f) show the corresponding values for July. When dynamic vegetation is introduced (Figure 6), emissivities for MSG and GOES become much more similar. The introduction of dynamic vegetation introduces significant changes to the GOES 휖11.2 field (Figure 6 - center): in the Cerrado and Atlantic Forest regions (South-eastern Brazil) values increase around 0.008, for the Caatinga (Eastern tip of Brazil) they decrease up to 0.010 (as well as for the Sahel), while for Amazonia an increase of about 0.003 is observed. Panels (c) and (f) show that the emissivity estimates for both sensors become much closer when CGLOPS FCOVER replaces the prescribed static values. Given the spectral differences between similar channels in GOES-R/ABI and MSG/SEVIRI imagers, it is expected that significant differences remain, even in areas where there are no differences in the estimation of FCOVER. This is the case of the strong negative difference between IR1 emissivities used for MSG and for GOES over the Sahara, which are associated to the spectral behavior of the emissivity within this band for bare ground land cover, as explained above. Emissivities for GOES-16 and MSG are provided by land cover and IR channel in the Annex 1 of the product ATBD [CGLOPS1_ATBD_LST-V2.0]. In the case of land cover class

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16 (barren), the IR1 emissivity for bare ground takes the value 0.9551 for GOES-16, while for MSG the value is 0.9469; this difference (of -0.0075) thus explains the systematic difference observed in Figure 6 (right panels). In July there are some notable differences to the January case: the introduction of CGLOPS FCOVER decreases IR1 emissivity over Cerrado and Atlantic Forest between 0.002 and 0.007 and even stronger decreases over the Caatinga and Southern Argentina and Chile. Over West Africa, a more realistic meridional gradient is introduced, with decreased values over the Sahel and increased values over south West Africa. The differences between MSG and GOES estimates are lower over the Caatinga region in July when compared to January, while for south West Africa the negative differences are increased. In Figure 7 and Figure 8, the corresponding analysis is performed for IR2 emissivity. The differences between MSG and GOES were similar to IR1, however slightly smaller over the Cerrado (differences are around 0.005) and with an opposite sign over the Sahara (for IR2 the difference is 0.0046).

Figure 6 – Impact of the introduction of CGLOPS FCOVER in 흐푰푹ퟏ, for the January and July periods. New values in v2.0 are shown in the left panels; center panels represent the difference to v1.2 values and right panels represent the difference between the emissivity of the corresponding channel in MSG and 흐ퟏퟏ.ퟐ(GOES).

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Figure 7 – Same as Figure 5, but for IR2 emissivity.

Figure 8 – Same as Figure 6, but for IR2 emissivity.

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Figure 9 shows the median differences in LSTs obtained with and without CGLOPS FCOVER, separated into night (3 UTC) and daytime (15 UTC) retrievals, and for January and July, now for the whole GOES-16 disk. These results indicate that the inclusion of a dynamic FCOVER introduces a seasonally varying difference, generally within the range -1 to +1 K. Over North America, LST generally increases between 0.5 and 1.5 K, over the Rocky Mountains, in forest areas of Canada to the north of the Great Plains, and western parts of Mexico. Similar increases are observed over the Andes, especially towards the south. In July, when vegetation cover increases over the Southern Great Plains, emissivity increases and LST decreases. The opposite pattern occurs over the Cerrado region: the rainy season there occurs from October to March, while the months between May and September are very dry. Therefore, decreased FCOVER in July decreases emissivity and consequently LSTs are increased by about 0.5 K. The opposite occurs in January, but the decreases in LST are more modest. The sensitivity of LST retrievals to surface emissivity is larger towards drier regions, because there is less compensation from downward longwave radiance to the emitted surface radiance. This effect explains the asymmetry of the impact of the inclusion of CGLOPS FCOVER in January and July, with a higher impact over the dry season when compared to the impact over the rainy season.

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Figure 9 – Impact of the introduction of CGLOPS FCOVER on GOES-16 LST. Left column shows the median LST using static emissivities for the dekad, while the right column represents the median of the differences due to the introduction of the dynamic emissivity. (a) and (b) January, timeslot 03UTC; (c) and (d) January, timeslot 15UTC; (e) and (f) July, timeslot 03UTC; (g) and (h) July, timeslot 15UTC.

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Figure 10- Impact of the introduction of CGLOPS FCOVER on GOES-16 LST uncertainty. Left column shows the median of LST uncertainty using static emissivities for the dekad, while the right column represents the change to that value due to the introduction of the dynamic emissivity. (a) and (b) January, timeslot 03UTC; (c) and (d) January, timeslot 15UTC; (e) and (f) July, timeslot 03UTC; (g) and (h) July, timeslot 15UTC.

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The introduction of dynamic vegetation in the LST processing chain also impacts the product uncertainty (Figure 10). Previous product versions assumed a fixed value for FCOVER uncertainty, set to 0.1. However, that fixed value was not very realistic, since there are situations that increase FCOVER determination uncertainty to higher values, namely extensive cloud cover, changes in land cover classification, etc. The CGLOPS FCOVER 1 km product provides an error bar based on product variability within the given dekad, which is propagated to the final LST uncertainty estimate [CGLOPS1_ATBD_FCOVER1km-V2]. The impact itself shows seasonal variability, following vegetation cycles. Decreases in FCOVER are generally associated to increases in total LST uncertainty, as can be seen over North America in January and over the Cerrado in July. The largest increases are located over mountain regions, where increases over 0.5 K are introduced. Since they do not change much across the year, it may be assumed that this effect comes from a systematic mismatch between FCOVER and its uncertainty for these regions in previous product versions.

Figure 11– Median diurnal cycle of the (spatial) mean differences between GOES-16 and MSG retrievals over South America. Blue curve represents the difference obtained using static vegetation, while the red curve represents the difference obtained with dynamic FCOVER.

Figure 11 represents the median diurnal cycle of the spatially averaged differences between GOES- 16 and MSG, for January and July. Daytime estimates from both satellites differ mainly due to directional effects: in the early morning, the Sun is towards the East and while GOES sees a higher fraction of shadows given sun illumination angles from the East and GOES viewing geometry over most of the area from the north west; the fraction of shadows seen by MSG will be lower, given its viewing geometry from north East. This explains why GOES measures colder temperatures than MSG during the morning. In the afternoon the opposite illumination/viewing geometry occurs for

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Issue: I1.10 some time: when the Sun is towards west, GOES sees much less shadows and therefore will measure warmer temperatures. The differences are much larger in July, when the Sun position is further north, when compared to January. During the night-time those differences are mainly due to: emissivity differences (including angular effects on emissivity as described in Ermida et al. (2018); or other sensor characteristics; or minor algorithm differences. In January, in the night-time the expected reduction of the differences between GOES and MSG is observed when dynamic FCOVER is introduced, with mean differences ranging from -0.6 K to -0.4 K. In the afternoon, however, GOES becomes up to 0.2 K warmer than MSG when dynamic FCOVER is used. In July, GOES LST is always colder than MSG, even for the new version. The correction due to FCOVER actually increases the negative bias by about 0.1 K. Reasons for these systematic differences are still unclear. Differences in vegetation cover and acquisition times were minimized in CGLOPS LST v2.0, and therefore cannot explain the observed discrepancies. Other hypothesis include differences in calibration of the GSW algorithm, systematic biases due to viewing/illumination (e.g., Ermida et al., 2018b) or differences in the cloud mask. Further research is needed to fully explain these differences, to which comparisons to LST data from in situ stations within the overlapping area could provide key insight.

4.2.2 Himawari-8

In this section, the impact of introducing the CGLOPS FCOVER as input to the Himawari-8 processing chain is assessed. Maps of CGLOPS FCOVER for January and July exhibit major differences with the static map (Figure 12), namely in Central Australia and Central Asia. Most of these regions are classified as grasslands and open shrublands, which in the static FCOVER look- up table corresponded to an FCOVER value of 0.5. CGLOPS FCOVER values over these regions are actually close to 0, especially in January. In July, values over East and South East Asia become much larger, with values ranging from 0.6 to 1. CGLOPS FCOVER values over Malaysia, Indonesia, Philippines and Papua New Guinea are also larger than the prescribed static value, reaching values close to 1 all year round.

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Figure 12– (a) Static FCOVER values used in LST v1.2 (b) CGLOPS FCOVER for the second dekad of January (c) CGLOPS FCOVER for the second dekad of July.

In this section, a simplified discussion is presented, since the effect of the introduction of the new FCOVER was already thoroughly discussed in section 4.2.1 regarding GOES-16, and also because Himawari-8 did not overlap with estimates from other disks in the previous product version (in the current version it overlaps with a section of the MSG IODC disk). In Figure 13, the impact on emissivity is quantified for both channels (IR1 and IR2) and for the two months used above (January and July, 2019). The impact is rather large, especially in the mid latitudes and for IR1 emissivity, where new emissivities are up to 0.02 lower than the previously used values. Tropical forests show a modest increase, that reaches 0.01 in certain areas in Malaysia and the Philippines. Over Eastern Asia in July, both emissivities increase by less than 0.1 due to vegetation dynamics over that region. In Figure 14, the impact on LST is illustrated. In the left column, the median of the LSTs over the given timeslot over 11 days, calculated using static vegetation (product V1.2.1) are shown, and in the right are the differences to the latter caused by the introduction of the dynamic FCOVER. In January in the 03 UTC slot (close to noon over that area) LSTs are rather large over Australia, and since the change in emissivity was relatively large, that is the region where the largest impacts are seen. LSTs increase from 1 to 1.5 K over that area, and large areas with positive differences of around 0.5 -1.5 are also observed all over Eastern Asia for January, namely in the region north of the Korean Peninsula, where the largest decreases in emissivity were observed (see Figure 13). In the tropics, the impact is rather insignificant, which is not surprising since the algorithm loses sensitivity to emissivity for moister atmospheres as discussed before. In July, over East Asia the impact on LST follows the changes in emissivity, i.e. over those regions where increases in FCOVER resulted in increases in emissivity, LSTs are slightly decreased. There are a few patches of increases in LST over the Gobi desert, since the dynamic FCOVER is systematically lower than the static

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Issue: I1.10 values used for that area (Figure 12). For Australia, the impact is generally lower than 1 K in July, except for the region around Lake Eyre, with increases slightly above 1 K. The impact in LST uncertainty is presented in Figure 15. Uncertainty was already high over the Gobi Desert and in retrievals over moist regions, especially when performed in high viewing angles (such as in India in July). However, over Central Australia the uncertainty was too low, especially considering the amount of bare ground in this region. The new product accounts for this more realistically. Uncertainty is also increased in January over most of Eastern Asia due to the decrease in FCOVER over those regions, which in turn increases the uncertainty due to emissivity.

Figure 13 – Impact of the introduction of CGLOPS FCOVER on Himawari-8 emissivity. (a) IR1 emissivity using static vegetation; (b) Difference in IR1 emissivity for January, (c) Difference in IR1 emissivity for July, (d) IR2 emissivity using static vegetation (e) Difference in IR2 emissivity for January and (f) Difference in IR2 emissivity for July. All differences are calculated using dynamic- static emissivities for the second dekad of the given month.

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Figure 14 – Impact of the introduction of CGLOPS FCOVER on Himawari-8 LST. Individual time slots are the same as in Figure 9, 15 UTC corresponds to nighttime and 03 UTC corresponds to daytime.

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Figure 15 - Impact of the introduction of CGLOPS FCOVER on Himawari-8 LST uncertainty. Individual time slots are the same as in Figure 10, 15 UTC corresponds to nighttime and 03 UTC corresponds to daytime.

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4.3 IMPACT OF NEW CALIBRATION COEFFICIENTS

4.3.1 GOES-16

When changing model coefficients, the more relevant impacts are expected for moister atmospheres and for higher viewing angles, i.e. the regimes where the GSW algorithm coefficients become more uncertain, because in these regimes the linear hypotheses on which the GSW formulation is based are less valid. Different calibration databases change model coefficients in such a way that retrievals made with the same model with two different sets of coefficients may return rather different LSTs, especially in those conditions. It should be noted that, contrary to Himawari-8, the initial calibration of the GSW used for GOES-16 already used the methodology developed by Martins et al. (2016) only with a different selection of atmospheric clear-sky profiles that obeyed the criteria defined in that study. The change was made so that the same set of atmospheric profiles were used for both GOES-16 and Himawari-8, also minimizing algorithm error for both sensors. To quantify the impact of the new coefficients, the original processing chain was adapted to use the new set of coefficients, and the same set of retrievals was repeated (i.e. hourly retrievals for 11 days in January and in July). Static vegetation cover was used here in order to isolate the effect of the new coefficients. In Figure 16, the impact of changing algorithm coefficients for the GOES-16 processing chain is quantified. Over North America, when warm moist air from the Gulf of Mexico flows over the Southern Great Plains (in the case of July), mean TCWV values over that region increase, as well as the LST itself. In there, the change in coefficients causes localized increases between 1 and 1.5 K. Over South America in January, the same happens over Amazonia and over Cerrado, since in this time of year, it is the rainy season over these regions. In contrast, the change in coefficients is less relevant in the dry season (e.g. see the very low impact on the Cerrado in July). Regarding uncertainty, the change in coefficients does not have a significant impact on the GOES- 16 LST retrieval (Figure 17). Model coefficients contribute to the total uncertainty through the “model uncertainty” term, which may exceed 3 K in situations where the optical path (i.e. the combined effect of viewing angle and TCWV) is larger, i.e. for TCWV > 4.5 cm, and viewing angles above ~60º [CGLOPS1_ATBD_LST-V2.0]. The impact on LST uncertainty is generally between -0.5 and 0.5 K, and for some locations between 0.5 and 1 K, such as central Amazonia and in the north-western North America (close to the GOES-16 disk edge) in July.

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Figure 16 – Impact of the change in algorithm coefficients on LST, for the GOES-16 retrieval. Individual time slots are the same as in Figure 9.

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Figure 17 - Impact of the change in algorithm coefficients on LST uncertainty, for the GOES-16 retrieval. Individual time slots are the same as in Figure 9.

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4.3.2 Himawari-8

For the Himawari-8 processing chain the change in coefficients implied a change in the original calibration methodology. When the Himawari-8 GSW was first calibrated, the production team relied on a method that constrained the choice of atmospheric profiles which ensured a homogeneous distribution in TCWV classes. However, as demonstrated by Martins et al. (2016), the method could be further improved in order to simultaneously cover the dynamic range of possible skin temperature classes. Some more extreme surface conditions were poorly represented in the original calibration database, such as warmer and drier surfaces. Therefore, the change in coefficients is expected to impact more the Himawari-8 LST retrievals when compared to GOES-16, with impacts over moister atmospheres but also over drier conditions. The impact of the new coefficients on Himawari-8 retrievals is illustrated in Figure 18. As for GOES, the new coefficients tend to increase LSTs for moister atmospheres, as in the case of the tropical regions of the Himawari-8 disk. The largest impacts are for Australia daytime retrievals in January. Over drier regions LSTs are generally decreased by about -1.5 K, but there are areas with increases of the same magnitude, likely related to intrusions of moister air. Over East Asia there is a modestly positive impact, with increases smaller than 0.5 K. In July, impacts are larger in the Northern Hemisphere, especially over drier areas such as the Gobi Desert. Over Australia the impacts are greatly reduced in July, with a small decrease (less than -0.25 K) for daytime and an increase of the same magnitude for night time retrievals. The impact the new coefficients on uncertainty is rather modest in the case of Himawari-8 retrievals (Figure 19). There are however some notable impacts in July over the Northern Hemisphere. Close to the northern edge of the disk there is an increase of about 0.5 K and over India there is a decrease of about 2 K. The latter may be somewhat unrealistic, but with the original set of coefficients, retrievals were made with uncertainties of almost 4 K over this region. This means that the new set of coefficients is more indicated for these retrievals over more extreme conditions.

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Figure 18 – Impact of the change in algorithm coefficients on LST, for the Himawari-8 retrieval. Individual time slots are the same as in Figure 9.

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Figure 19 – Impact of the change in algorithm coefficients in LST uncertainty, for the Himawari-8 retrieval. Individual time slots are the same as in Figure 9.

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4.4 COMBINED EFFECT OF NEW FCOVER AND NEW COEFFICIENTS

4.4.1 GOES-16

In this section a third set of retrievals were performed for the same periods as before, including both the CGLOPS FCOVER and the new set of coefficients (i.e. what is used for V2.0). This translates the expected overall impact of the new product version with respect to V1.2. In Figure 20, the combined impact for GOES-16 is shown. There is an overall increase in GOES-16 LSTs, with mixed effects from both the use of the dynamic vegetation and from the new set of coefficients. New coefficients are responsible for the increase over moister areas, such as the Amazonia and over the Great Plains in July. The inclusion of FCOVER increases LSTs by up to 1 K over North America in January, over Cerrado, over the Andes and areas west of the Rocky Mountains. Over Venezuela and Southern Argentina in January, the increase is slightly higher than 1 K. Over East Coast of North America there is a decrease in LSTs in July (which locally exceeds - 0.5 K), mainly because of the new FCOVER but with a small contribution of the change in coefficients, especially over southern United States. Over Argentina LSTs decrease around 0.5 K in January, while for July there is an increase of the same magnitude. The overall impact on product uncertainty is quantified in Figure 21. All over the West Coast of North America there is an increase between 0.5 and 0.75 K, which is attributable to the introduction of CGLOPS FCOVER (see Figure 10). The same happens over Andes, however with a higher impact on daytime retrievals. Values around 0.5 K occur over northern Cerrado in July which are also caused by the inclusion of CGLOPS FCOVER. The change in coefficients impacts uncertainty mostly over moister regions (such as over Amazonia). Reductions of the uncertainty of about 0.5 K occur over the Atlantic Forest and northern regions of Cerrado.

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Figure 20 – Combined impact of the introduction of CGLOPS FCOVER and the new set of GSW coefficients in the GOES-16 LST retrieval. Individual time slots are the same as in Figure 9.

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Figure 21 – Combined impact of the introduction of CGLOPS FCOVER and the new set of GSW coefficients in the GOES-16 LST retrieval uncertainty. Individual time slots are the same as in Figure 9.

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4.4.2 Himawari-8

For Himawari-8 retrievals, the overall impact is more complex (Figure 22), since the change in coefficients and the use of CGLOPS FCOVER introduce changes with similar magnitudes, which in some cases add up and in others they partly cancel each other. Over East Asia in January, LST is generally increased, up to 1.5 K for both day and night-time. Over northern parts of Australia, there are increases exceeding 2 K, which are produced by the added effect of both changes, since both caused LST increases in this region. Towards the southeast, negative differences of up to -1 K dominate the spatial pattern, but some patches of positive impact may also be observed. In the night time positive differences of around 0.5 K are observed all over Australia, the exception being over some areas in the north and northwest, where there is an overall negative impact. In July, East is dominated by areas where the impact is negative, however the pattern is rather complex, with areas where the differences are of about -2 K and other areas where the differences reach +1 K. The area of mixed impact extends to the tropics. Australia on the other hand exhibits a much more homogeneous pattern of LST increase, reaching values around 1 K in the night-time, towards the western part of the continent. The impact on product uncertainty (Figure 23) is again rather complex. In January, over East Asia there are increases up to 0.5 K in the uncertainty estimate, except for some areas over the Gobi Desert and around the Yellow Sea, which show small decreases (below -0.5 K). In July there is a decrease over South East Asia and over India, for which the main contribution comes from the change in model coefficients. Over Australia there is a systematic increase in uncertainty, mainly caused by the introduction of the dynamic FCOVER, but with some added effect from the change in model coefficients. The uncertainty increase over Australia is realistic since most of the continent is sparsely vegetated or with barely any vegetation at all, and therefore land surface emissivity determination becomes much more uncertain.

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Figure 22 – Combined impact of the introduction of CGLOPS FCOVER and the new set of GSW coefficients in the Himawari-8 LST retrieval. Individual time slots are the same as in Figure 9.

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Figure 23 - Combined impact of the introduction of CGLOPS FCOVER and the new set of GSW coefficients in the Himawari-8 LST retrieval uncertainty. Individual time slots are the same as in Figure 9.

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4.5 SPATIAL COVERAGE

Up until product version v1.2, CGLOPS LST was derived from LSTs from GOES-16, MSGº and Himawari. The inclusion of the LST obtained by the MSG IODC mission (Figure 24) (and produced as a demonstrational product by the LSA-SAF) constitutes an important step forward to one of the major goals of the CGLOPS LST product, which is to provide global operational LST estimates in NRT. Figure 25 shows an example of the global product for the time slot corresponding to 15/01/2020 at 12UTC, for product version V1.2 and for the new version V2.0. In the new version, the region from western part of India to the Persian Gulf is now included in the product. Moreover, since there is a large overlap between the 0º and IODC MSG missions, spatial coverage is further increased because some of the regions classified as cloudy by the 0º mission are classified as clear by the IODC mission (see examples of this behaviour over South Africa and over Ethiopia).

Figure 24 – Areas covered by GEO satellites. From left to right: GOES, MSG 0°, MSG IODC and Himawari.

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Figure 25 – LST for the 15/01/2020 12UTC time slot, for product version V1.2 (top) and V2.0 (bottom).

The increase in product coverage was quantified by the determination of the fraction of land pixels over the period 15 to 30 January 2020, when both processing chains were running in parallel in the production environment. The coverage in this period was increased from 28.2% to 30.5% of the total of land pixels within the CGLOPS LST product domain (Figure 26). This was a particularly cloudy

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Issue: I1.10 period over the region now included in the product, and therefore the evaluation of the increase in spatial coverage with this criterion may be larger when a larger and more representative period is used. Another metric that is useful to demonstrate the coverage increase is the percentage of land pixels that is actually within at least one of the fields of view of the sensors used in CGLOPS LST. This metric indicates an increase from 53.2% in v1.2 to 60.0% in v2.0. The difference between the two metrics is attributable to the cloud mask and to quality control. The black bars indicate the standard deviation of the value, showing an increase towards v2.0. This can be explained by the wobbling motion of the MSG-1 satellite, i.e. oscillations around its nominal position over the Equator. These indicators follow these oscillations closely due to the fact that over the IODC mission field of view, the number of land pixels is strongly asymmetric between Northern and Southern Hemispheres.

Figure 26 – Comparison of the percentage of valid (clear-sky) land pixels for the period from 15 to 30 January 2020 (blue bars) and the percentage of land pixels that are covered by at least one sensor (orange). Black bars denote the +/- standard deviation of the percentage interval for the same period.

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4.6 CHANGE OF TIME SLOTS USED FOR MSG

The last change that was applied in v2.0 was the change in the timeslot used for MSG. This sensor performs full disk scans every 15 min. In the previous version (v1.2), the time slot for MSG was selected so that the acquisition time differences between sensors (GOES-12 and MTSAT-1) were minimized. That time slot corresponded to the T-15 min, where T is the reference time in the CGLOPS LST file. When Himawari-8 and GOES-16 went into operations, since the scans are faster and more frequent, a non-optimal selection of time slots was adopted. That option was chosen in order to avoid discontinuities in time series obtained over areas covered by other satellites, namely MSG. However, in the case of the overlapping area between the GOES-16 and MSG 0º disks, the merging algorithm was averaging values that were obtained up 21 min apart. With the launch of a new version (v2.0), the production team decided to update the configuration of the merging algorithm, taking into account the current swath geometry of all sensors and the fact that since MSG IODC is now incorporated into the global product, the overlapping areas are increased. The timeslot that minimizes those differences is T instead of T-15. With this new configuration, these time differences are reduced to less than 5 min in the overlapping areas.

Figure 27 – Time series of the LST differences resulting from choosing time slot T (v2.0) with respect to T-15 min (v1.2) in a 20x20 pixels area over the MSG disk (represented as the blue square in the map on the left). Colors represent the MSG 0º LST for the time slot 20200118 1200UTC (units ºC).

Although the mean acquisition time of the pixels used to produce the LST estimate is provided for each grid point of the CGLOPS LST product, a typical user will likely assume that the LST value corresponds to the reference time in each file. Therefore, an example of the expected LST differences between v2.0 and v1.2 is provided in Figure 27. Each point in the time series is an average (over a 20x20 window) of the difference between the MSG 0º LST estimates used in version v2.0 (i.e, from the time slot T = 0), and the LST estimate used in v1.2 (i.e, timeslot T-15 min). In a region over Central Sahara (selected due to the high dynamic range of LST and relatively lower

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5 IMPACT ON VALIDATION STATISTICS To study the impact of the changes on the overall quality, it is necessary to compare data from both product versions to in situ data, as well as to an alternative/”heritage” LST product, as recommended by the Committee on Earth Observation Satellites Working Group (CEOS) on Calibration and Validation - Land Product Validation (LPV) Subgroup for validation of Land Surface Temperature Products (Guillevic et al., 2018). The validation exercise presented below compares CGLOPS LST from v1.2 and from v2.0 against a set of stations over a wide range of land cover types and climate zones. A spatial and temporal consistency analysis is performed by comparing CGLOPS LST retrievals from v1.2. and v2.0 against LST from Sentinel-3 SLSTR operated by ESA.

5.1 IN SITU COMPARISONS

We use the available data from a set of stations within the product spatial domain, belonging to different measurement networks: • Baseline Surface Radiation Network (BSRN, Driemel et al., 2018); • Surface Radiation network (SURFRAD, Augustine et al., 2005); • Copernicus Ground-Based Observations for Validation of CGLS Products (GBOV, Bai et al., 2019); • Australian and New Zealand Flux Research and Monitoring (OZFlux, Beringer et al., 2016; Isaac et al., 2017); • European Flux Database Cluster (EFDC; available online: http://www.europe-fluxdata.eu/).

Since these environmental monitoring networks are not always specifically designed for satellite LST validation, not all in situ stations are adequate to assess the quality of the CGLOPS LST product. Namely, measurements made by the station are often not representative of the pixel at the satellite scale. Careful examination of station surroundings is performed using high resolution information on orography, land cover and LST retrievals performed with Landsat, all of which using Google Earth Engine (Gorelick et al., 2017) and the package to derive LST from Landsat retrievals, developed by Ermida et al. (2020). Stations in each network are equipped with a similar set of instruments. However, not all of them provide downwelling longwave radiation fluxes (e.g. the stations obtained through GBOV, see below). Each network provides data in a different format / sampling, which are detailed in the next subsections. GBOV already provides LST estimates, which were estimated using the procedures detailed in the product ATBD (GBOV-ATBD-RM2-RM8-RM9, available at: https://gbov.acri.fr/public/docs/products/GBOV-ATBD-RM2-RM8-RM9_v1.3-LST.pdf). All the other networks provide radiative fluxes, which need to be converted into LST. The downward pointing radiometer measurements, 퐿↑, are the sum of the radiance emitted by the surface with the reflected downward longwave radiance originating in the lower atmosphere, 퐿↓:

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퐿↑ = 휖(휆)퐵(휆, 푇) + (1 − 휖(휆))퐿↓(휆), (1) where 퐵 represents the Planck function describing the radiance emitted by a black body at temperature 푇 and wavelength 휆, and 휖(휆) stands for surface spectral emissivity. In the case of broadband radiometers, 퐵(휆, 푇) is given by the Stephan-Boltzmann’s law (i.e., 퐵(푇) = 휎푇4), where 휎 = 5.6704 × 10−8 W m-2 K-4 is the Stephan-Boltzmann constant, and 휖 in this case is an emissivity value characterizing the whole longwave band, 휖푏 (3.5-50 µm). Rearranging for 푇 yields:

↑ ↓ 4 퐿 − (1 − 휖푏)퐿 푇 = √ , (2) 휎휖푏

Since all the stations used here had broadband sensors, emissivity was estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Data Set version 3 (GEDv3; Hulley et al., 2015) following the procedures described in Malakar et al. (2018). Google Earth Engine was used to estimate timeseries of 휖푏 for each station (Ermida et al., 2020), and a short description of the methodology is presented here. Narrowband emissivities for each ASTER GEDv3 channel are estimated as a function of the fraction of vegetation cover:

휖푖 = 푓푣휖푣푒푔,푖 + (1 − 푓푣)휖푏푎푟푒,푖,, (3)

Where the bare ground emissivities (which are static values) are corrected by the fraction of vegetation cover for each scene:

휖퐴푆푇퐸푅,푖 − 휖푣푒푔,푖푓푣퐿푎푛푑푠푎푡8 휖푏푎푟푒,푖 = (4) 1 − 푓푣퐿푎푛푑푠푎푡8

In this procedure, 휖퐴푆푇퐸푅 is internally collocated with Landsat8 by Google Earth Engine. Fraction of vegetation cover is estimated for each Landsat8 pixel using:

푁퐷푉퐼푚푎푥 − 푁퐷푉퐼 푓푣퐿푎푛푑푠푎푡8 = 1 − (5) 푁퐷푉퐼푚푎푥 − 푁퐷푉퐼푚푖푛

NDVI is estimated for each pixel using the conventional definition, i.e. computing the normalized difference between NIR (Band 5) and Red bands (Band 4). 푁퐷푉퐼푚푎푥 is set to 0.999 and 푁퐷푉퐼푚푖푛 is set to 0. Vegetation emissivities for each ASTER band are listed in Table 6. Finally, broadband emissivity is estimated using (Malakar et al., 2018; Ogawa et al., 2008):

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휖푏 = 0.128 + 0.014휖10 + 0.145휖11 + 0.241휖12 + 0.467휖13 + 0.004휖14 (6)

Station emissivity is estimated using a spatial buffer of 30 m around the upwelling sensor location, for each valid Landsat8 measurement in the validation period, thus accounting for the effect of vegetation phenology.

Table 6 – ASTER bands used to estimate surface broadband emissivity

Wavelength (μm) 흐풗풆품

Band 10 8.125 - 8.475 0.987 Band 11 8.475 - 8.825 0.989 Band 12 8.925 - 9.275 0.985 Band 13 10.25 - 10.95 0.989 Band 14 10.95 - 11.65 0.986

5.1.1 Station descriptions

5.1.1.1 BSRN

BSRN stations provide upward and downward longwave radiation flux measurements made by pyrgeometers covering the 4 – 40 µm spectral range (Driemel et al., 2018). In some cases, they are located over areas that are representative of their surroundings at the satellite pixel scale. Data files are downloaded quality-controlled using the BSRN toolbox (Schmithusen et al., 2019) and provide measurements every min as ASCII tables. The BSRN stations used in this report are listed in Table 7.

Table 7 – List of BSRN stations used in this report. Land cover information was retrieved from http://maps.elie.ucl.ac.be/CCI/viewer/index.php, and Köppen-Geiger climate classification data was obtained from http://koeppen-geiger.vu-wien.ac.at/.

Height Name Code Latitude Longitude CCI Land Cover Köppen climate classification (m)

40 (mosaic nat. Budapest (Hungary) BUD 47.4291 N 19.1822 E 137 Temperate, humid (Cfb) veg. / cropland)

Cabauw CAB 51.971° N 4.927° E 0 130 (Grassland) Temperate, humid (Cfb) (The Netherlands)

Gobabeb (Namibia) GOB 23.519° S 15.083° E 409 200 (Bare) Arid, desert (BWh)

Payerne PAY 46.815° N 6.944° E 495 10 (Cropland) Temperate, humid (Cfb) (Switzerland)

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140.126° Warm, humid, warm summer Tateno (Japan) TAT 36.058° N 9 10 (Cropland) E (Cfa)

Toravere (Estonia) TOR 58.254° N 26.462° E 46 100 (Cropland) Snow, humid, hot summer (Dfb)

5.1.1.2 SURFRAD

SURFRAD stations are part of the BSRN network and therefore share most of the basic characteristics with those stations. Data from each station are available in a quasi-daily basis through ftp://ftp.srrb.noaa.gov/pub/data/surfrad/. The SURFRAD stations used in this report are listed in Table 8.

Table 8 - List of SURFRAD stations used in this report. Land cover information was retrieved from http://maps.elie.ucl.ac.be/CCI/viewer/index.php, and Köppen-Geiger climate classification data was obtained from http://koeppen-geiger.vu-wien.ac.at/

Height Köppen climate Name Code Latitude Longitude CCI Land Cover (m) classification

Bondville Temperate, humid, hot BON 40.051° N 88.373° W 213 11 (Cropland) (USA) summer (Cfa)

Desert Rock (USA) DRA 36.623° N 116.020° W 1003 120 (Shrubland) Cold arid (BWk)

Fort Peck FPK 48.308° N 105.102° W 634 130 (Grassland) Arid, steppe, cold (BSk) (USA)

Goodwin Creek 40 (Mosaic natural Temperate, humid, hot GWN 34.255° N 89.873° W 98 (USA) veg. / cropland) summer (Cfa)

Penn State Temperate, humid, hot PSU 40.720° N 77.931° W 376 11 (Cropland) University (USA) summer (Cfa)

Sioux Falls SXF 43.734° N 96.623° W 473 11 (Cropland) Cold, humid (Dfa) (USA)

Table Mountain TBL 40.126° N 105.238° W 1689 130 (Grassland) Arid, steppe, cold (BSk) (USA)

5.1.1.3 GBOV

The Copernicus GBOV initiative aims to aggregate and harmonize a comprehensive set of stations providing in situ data – the so-called Reference Measurements – to ease the regular validation exercises that all the products provided by CGLOPS are subject to. The GBOV service is still at an early stage, and will be further expanded in terms of the number of available stations, namely adding some of those used in this report, which are maintained within various networks. The deployment and maintenance of dedicated GBOV stations is also foreseen in the near-future. All data may be downloaded from https://gbov.acri.fr/. Reference measurements are provided until the end of the previous year (i.e. by the time this report was produced, data was only available until

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Issue: I1.10 the end of 2019). It should be noted that GBOV stations consider a simplified correction for the down- welling radiation reflected by the surface, as described in the GBOV Energy products ATBD (GBOV- ATBD-RM1-LP1-LP2_v1.3-Energy). The list of GBOV stations used in this report is provided in Table 9. Stations “Duke Forest” and “Yosemite Village” which are provided by GBOV were excluded from this analysis as they were providing LST estimates that are not representative of CGLOPS LST pixel scale. Station “Santa Barbara” is frequently cloud covered in the nighttime due to the presence of coastal clouds (which are difficult to detect from satellite); its results should be therefore regarded with caution. All files provide half-hourly LST estimates in CSV files, except for Southern Great Plains station, whose sampling is 1 min. The GBOV stations used in this report are listed in Table 9.

Table 9 – List of GBOV stations used in this report. Land cover information was retrieved from http://maps.elie.ucl.ac.be/CCI/viewer/index.php, and Köppen-Geiger climate classification data was obtained from http://koeppen-geiger.vu-wien.ac.at/.

Height Köppen climate Name Code Latitude Longitude CCI Land Cover (m) classification

Des Moines Snow, humid, hot summer DMOS 41.5563° N 93.2854° W 281 130 (Grassland) (USA) (Dfa)

Temperate, humid, hot Manhattan (USA) MANH 39.1027° N 96.6097° W 347 130 (Grassland) summer (Cfa)

Southern Great Temperate, humid, hot SGP 36.6058° N 97.4888° W 318 11 (Cropland) Plains (USA) summer (Cfa)

Williams (USA) WILL 35.7550° N 112.3374° W 1826 120 (Shrubland) Arid, steppe, cold (BSk)

5.1.1.4 OZFlux

OZFlux provides flux tower data from a series of sites across Australia and New Zealand (Beringer et al., 2016; Isaac et al., 2017) and are in line with other global networks such as FLUXNET and CarboEurope, among others. Some of the OZFlux stations will soon be re-distributed from the GBOV portal. All data were downloaded from the server http://www.ozflux.org.au/. Each station provides yearly netCF4 files with data every 30 min. The OZFlux stations used in this report are listed in Table 10.

Table 10 - List of OZFlux stations used in this report. Land cover information was retrieved from http://maps.elie.ucl.ac.be/CCI/viewer/index.php, and Köppen-Geiger climate classification data was obtained from http://koeppen-geiger.vu-wien.ac.at/.

Height CCI Land Köppen climate Name Code Latitude Longitude (m) Cover classification

120 Alice Springs Mulga OZFX_ASM 22.2828° S 133.2493° E 606 Desert, cold, arid (BWk) (Shrublands)

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120 Calperum Chowilla OZFX_CAL 34.0027° S 140.5877° E 64 Arid, steppe, cold (BSk) (Shrublands)

50 (Evergreen Cow Bay OZFX_COW 16.2382° S 145.4272° E 68 Equatorial monsoon (Am) Forest)

50 (Evergreen Temperate, humid, hot Cumberland Plain OZFX_CBL 33.6153° S 150.7236° E 37 Forest) summer (Cfa)

122 Equatorial savannah with Daly River Savanna OZFX_DAS 14.1592° S 131.3881° E 53 (Shrubland) dry winter (Aw)

122 Equatorial savannah with Dry River OZFX_DRY 15.2588° S 132.3706° E 180 (Shrubland) dry winter (Aw)

62 (Deciduous Warm temperate climate Gingin OZFX_GIN 31.3764° S 115.7138° E 53 Forest) with hot dry summer (Csa)

Warm temperate climate Ridgefield OZFX_RGF 318 11 (Crops) 32.5061° S 116.9668° E with hot dry summer (Csa)

60 (Deciduous Temperate, humid, hot Robson Creek OZFX_ROB 17.1175° S 145.6301° E 710 Forest) summer (Cfa)

60 (Deciduous Sturt Plains OZFX_STP 17.1507° S 133.3502° E 150 Arid, steppe, hot (BSh) Forest)

50 (Evergreen Temperate, humid, warm Warra OZFX_WRR 43.09502° S 146.6545° E 121 Forest) summer (Cfb)

50 (Evergreen Temperate, humid, hot Whroo OZFX_WHR 36.6732° S 145.0294° E 121 Forest) summer (Cfa)

Wombat State 50 (Evergreen Temperate, humid, warm OZFX_WOM 37.4222° S 144.0944° E 715 Forest Forest) summer (Cfb)

Yanco (JAXA) OZFX_YAN 34.9878° S 146.2908° E 123 10 (Crops) Arid, steppe, cold (BSk)

5.1.1.5 EFDC

The EFDC portal provides data for a number of stations mostly concentrated over Europe. However, very few stations were available for the recent months. Here we only use one station from this network, whose characteristics are detailed in Table 11. Broadband measurements of upwelling and downwelling longwave fluxes are provided half-hourly in ASCII files.

Table 11 - List of EFDC stations used in this report. Land cover information was retrieved from http://maps.elie.ucl.ac.be/CCI/viewer/index.php, and Köppen-Geiger climate classification data was obtained from http://koeppen-geiger.vu-wien.ac.at/.

Height CCI Land Köppen climate Name Code Latitude Longitude (m) Cover classification

Guaya Flux (French 50 (Evergreen GFGuy 5.2788° N 52.9249° W 46 Equatorial monsoon (Am) Guiana) Forest)

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5.1.2 Evaluation metrics

To evaluate the level of agreement between different LST estimates, we use robust statistics. The median error (also denoted as bias in this report, 휇), which is a measure of the accuracy is given by:

휇 = 푚푒푑푖푎푛(퐿푆푇푠푎푡 − 퐿푆푇푖푛푠푖푡푢), (7) where 퐿푆푇푠푎푡 stands for individual satellite LST measurements and 퐿푆푇푖푛푠푖푡푢 represents in situ LST, collocated in space and time. The median of the absolute residuals, 휎, reports on precision and is given by:

휎 = 푚푒푑푖푎푛( |(퐿푆푇푠푎푡 − 퐿푆푇푖푛푠푖푡푢) − 휇|) (8)

Finally, an estimate of total uncertainty is given by root-mean-square-difference, RMSD:

2 ∑(퐿푆푇푠푎푡 − 퐿푆푇푖푛푠푖푡푢) 푅푀푆퐷 = √ , (9) 푁 where 푁 represents the sample size.

5.1.3 Results

The comparisons were performed for the period January – December 2019, for which both satellite and in situ data were available. Each satellite – in situ matchup is valid only if the absolute difference in acquisition time is less than 7.5 min (Trigo et al., 2008a). In the comparison between versions, it is ensured that the sampling is the same; we also restrict the comparison to cases where the flag PERCENT_PROC_PIXELS equals 100 %. This procedure ensures that 1) all the pixels in the original satellite grids used to produce the final LST estimate are classified as clear-sky and 2) the pixel is not cosmetically filled (i.e. its value is interpolated from neighboring pixels). In general, the validation statistics described in section 5.1.2 are calculated for the CGLOPS LST - in situ LST matchups, for v1.2 and v2.0 respectively, using the same sampling (i.e. only when there are valid estimates both in v1.2 and in v2.0). An example of the individual comparisons for each station is provided below, for station OZFX_CBL. Figure 28 shows a snapshot of the LST timeseries for that station, where in situ values are compared to the corresponding LSTs derived with v2.0 and v1.2, using both the nearest pixel and the pixel actually used in the colocation. The closest pixel shows “overshooting” values both during daytime (with differences reaching about 5 K) and during most nighttime situations. For the case of the pixel actually selected to perform the validation (see below), individual values mostly follow the in-situ curve, with small differences between v1.2 and v2.0, indicating that the selected pixel is more representative of the measurements made at the station.

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Figure 28 – Time series of LST obtained between 15 April 2019 and 25 April 2019, for nearest pixel of station OZFX_CBL, both with v1.2 (light blue) and v2.0 (orange). Also shown are the corresponding estimates for the pixel actually used for the collocation (“coloc” in red for v2.0 and in dark blue for v1.2). In situ values are represented in black. Figure 29 shows the satellite LST metrics, when compared with in situ measurements, over a 5x5 pixel window around the station. The central pixel in each diagram contains the station. In the case of the OZFX_CBL station, the nearest pixel shows a pronounced negative 휇 (-1.2 K for v2.0 and - 1.3 K for v1.2), a relatively high 휎 (1.7 K for v2.0 and 1.8 K for v1.2) and a relatively high RMSD (3.5 K for v2.0 and 3.7 K for v1.2). Looking at the values of the different metrics, it is possible to discern a spatial variability pattern within the considered region. In general, the pixel showing the best statistics in this kind of diagram is chosen, as better statistics frequently correspond to better representativity. This choice is backed by visual inspection of ancillary high resolution datasets. Figure 30 shows the high resolution Google Earth Engine “satellite view” of the area surrounding OZFX_CBL, revealing that it is located within a relatively small forest patch, surrounded by man- made structures and farms. Therefore, the Himawari (CLOGPS1) pixel (shown by the semi- transparent grid) is far from homogeneous and not well represented by the station measurements. Himawari pixels located to the left (i.e. outside the represented grid) are fully covered by forest. The diagrams in Figure 29 confirm that the closest of those forest pixels is more representative of the in- situ measurements. After the application of the representativeness criteria described above, this station shows a 휇 = −0.1 K for v2.0 (휇 = −0.3 K for v1.2), and RMSDs of 2.6 K for both versions. The choice of the matchup pixel for all the stations is detailed in ANNEX 1, where all the diagrams similar to the one shown in Figure 29 are presented together with visible imagery of the surrounding area obtained through Google Earth Engine.

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Figure 29 – Validation statistics obtained using the neighboring 5x5 pixels around the station (which lies within the central pixel in the diagrams). The top row shows the statistics obtained with v2.0 estimates, while those obtained with v1.2 are shown in the lower row.

Figure 30 – High resolution visible imagery from Google Earth Engine around station OZFX_CBL. The station is located over the red dot. Gray transparent squares represent a 3x3 pixel window around the station.

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Finally, the scatterplots for the matchups obtained over the selected OZFX_CBL pixel are shown in Figure 31, together with the values of the validation metrics. Although most matchups follow the 1:1 line (dashed black line), this station exhibits some dispersion for lower values, which possibly corresponds to cloud contamination at night.

Figure 31 – Scatterplots of the matchups between CGLOPS LST (v1.2 in the left, v2.0 in the right) and in situ values from station OZFX_CBL. A summary of the statistics for each station is presented in Table 12. Most stations comply with the stricter accuracy requirements (|휇| < 1 K). All stations that did not meet this requirement with v1.2 improved or maintained their accuracies in v2.0 except for TOR, OZFX_COW and OZFX_ASM: the median difference between satellite and in situ changed from -1.6 K to -1.7 K for TOR, from -1.0 K to -1.3 K for OZFX_COW and from 0.6 K to 1.3 K for OZFX_ASM. Most stations exhibit RMSD < 4 K, except OZFX_CAL. Assuming RMSD to be a realistic measure of the product’s total uncertainty, then v2.0 LST meets the target value almost everywhere. Over the most homogeneous sites, LST RMSDs are within or nearly within target uncertainty (i.e. RMSD < 2 K), as is the case for GOB, USA_SGP, OZFX_RGF, PSU, GFGuy, CAB, OZFX_WOM, USA_DMOS and FPK. Most shrubland stations of OZFX are just below the uncertainty threshold (i.e. RMSD < 4 K). It is worth noticing that the validation period was characterized by unprecedented bush/wildfires all over Australia, which introduce dramatic changes in the landscape and produce dense smoke / aerosol clouds that difficult satellite LST retrievals. This issue was thoroughly discussed in the showcases section of the annual 2019 Scientific Quality Assessment Report [CGLOPS1_SQE2019_LST-V1.2_I1.00]. Stations GOB, PAY, CAB, BUD and TOR lie in the area of intersection of the MSG 0º and IODC missions; their differences between v1.2 and v2.0 are almost exclusively attributable to the introduction of IODC in v2.0 (they are also due to the change in timeslot, as discussed in section 4.6, as well as due to geometric effects, such as mixing different pixel sizes). The effect of the merge between estimates of both missions translates into a small increase in negative median differences

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(between 0.1 and 0.3 K). The merge also translates into a decrease of between 0.0 to 0.2 K in RMSD. In general, the explicit representation of seasonal variations of vegetation seems to benefit the grassland and mixed vegetation sites, while for other land cover types the impact is mostly station- dependent, due to different representativity of the in-situ measurements at the pixel level (see Annex 1 for a thorough discussion on this issue).

Table 12 - Summary statistics for the 32 stations used in this report, for CGLOPS LST v1.2 and for v2.0 (differences are CGLOPS LST – in situ LST). Stations are grouped by land cover type. Global summary statistics, using all matchups from all stations, are provided in the last row. Accuracy (μ) compliance with requirements in Table 1 is colored as: “within required accuracy” as green, “within achievable accuracy” as yellow, “above achievable accuracy” as red. Uncertainty (RMSD) compliance with Table 2 is colored as: “within target uncertainty” in green; “within threshold uncertainty” in yellow, “above threshold uncertainty” in red.

V1.2 V2.0 RMSD RMSD Code Land Cover 흁 [K] 흈 [K] #points 흁 [K] 흈 [K] #points [K] [K] GOB Bare 0.0 1.0 2.4 6890 -0.2 1.0 2.3 6890 PAY Crops 0.6 1.3 2.4 2588 0.1 1.3 2.3 2588 TAT Crops 0.4 2.6 3.5 2909 0.9 2.6 3.6 2909 USA_SGP Crops -0.2 1.1 2.1 3949 0.2 1.0 2.0 3949 OZFX_RGF Crops -0.4 0.9 1.8 4517 -0.1 0.8 1.7 4517 OZFX_YAN Crops 0.2 1.3 3.6 4029 0.5 1.3 3.6 4029 BON Crops -1.3 1.5 3.1 3217 -1.1 1.6 3.0 3217 PSU Crops -0.3 1.3 2.4 2604 -0.0 1.4 2.3 2604 SXF Crops -0.7 1.3 2.5 3341 -0.4 1.4 2.5 3341 Deciduous OZFX_GIN 0.3 1.1 2.6 5536 0.5 1.0 2.5 5536 Forest Deciduous OZFX_ROB -0.2 1.6 2.8 4168 0.0 1.6 2.7 4168 Forest Deciduous OZFX_STP -0.0 1.3 3.1 4833 0.5 1.2 3.3 4833 Forest Evergreen GFGuy 0.7 0.9 2.8 55 0.7 0.9 2.3 55 Forest Evergreen OZFX_COW -1.0 1.4 3.4 3152 -1.3 1.3 3.4 3152 Forest Evergreen OZFX_CBL -0.2 1.4 2.6 4297 -0.1 1.3 2.6 4297 Forest Evergreen OZFX_WRR -1.9 1.9 3.6 1782 -1.5 1.8 3.4 1782 Forest Evergreen OZFX_WHR -0.7 1.1 3.0 2950 -0.5 1.0 2.9 2950 Forest Evergreen OZFX_WOM -1.0 1.3 2.2 2377 -0.8 1.2 2.1 2377 Forest CAB Grasslands -0.1 0.9 2.0 1938 -0.2 0.9 1.9 1938 USA_MANH Grasslands -0.0 1.2 2.5 3657 0.4 1.2 2.5 3657

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USA_DMOS Grasslands 0.1 1.1 2.1 3230 0.3 1.1 2.1 3230 TBL Grasslands -1.0 1.3 2.7 3697 -0.6 1.3 2.5 3697 FPK Grasslands -1.4 1.2 2.5 3012 -0.9 1.2 2.2 3012 Mixed BUD -0.1 1.4 2.7 1969 0.0 1.3 2.5 1969 Vegetation Mixed TOR -1.6 1.7 3.3 840 -1.7 1.6 3.3 840 Vegetation Mixed GWN -0.6 1.5 2.6 3610 -0.4 1.4 2.6 3610 Vegetation USA_WILL Shrublands -0.2 1.3 2.4 4506 0.6 1.3 2.4 4506 OZFX_ASM Shrublands 0.6 1.2 3.5 6017 1.3 1.1 3.8 6017 OZFX_CAL Shrublands -1.3 2.0 4.7 5058 -0.6 2.0 4.7 5058 OZFX_DAS Shrublands -0.4 1.6 3.1 3448 -0.1 1.6 3.0 3448 OZFX_DRY Shrublands -1.9 1.9 3.4 3016 -1.7 1.7 3.3 3016 DRA Shrublands -0.5 2.0 3.1 4658 -0.4 2.0 3.1 4658

Total -0.3 1.4 2.9 111850 0.0 1.4 2.9 111850

In these comparisons, maintenance of the in situ sensors may also affect satellite to in-situ comparisons: for instance, if the sensors are moved, there can be changes in their field of view, with different surface end-members (canopy, bare ground, grass,…) being sensed in different stages of the time series. There have been many reports of changes/malfunctions of station sensors, which may also degrade the in-situ time series homogeneity. Despite these limitations, the representation of a wide range of different land covers and climate regions provides evidence that changes in CGLOPS LST v2.0 are generally beneficial, with an overall higher accuracy (i.e. with an absolute value of 휇 that is 0.3 K lower when compared to v1.2) and a similar overall uncertainty. However, in general individual stations show small decreases in RMSD from v1.2 to v2.0.

5.2 COMPARISON WITH SENTINEL-3 SLSTR LST

Comparisons with other satellite LST products are useful, not as validation per se (since none of the satellite products represents an absolute reference), but as a tool to assess the spatial and temporal consistency of the proposed product. In the case of CGLOPS LST, this exercise is particularly useful as the four missions cover different parts of the globe, so it is important to evaluate if there are quality differences among the different areas. Several versions of MODIS LST have been used for this purpose in past CGLOPS LST evaluation exercises. However, since this sensor is approaching the end of its life cycle and there are indications of radiometric degradation of some MODIS channels (particularly for the MODIS onboard Terra), other options are considered here. In particular, Copernicus now provides its own LST product based on a fully operational polar-orbiting service, which makes the LST product derived from the Sea and Land Surface Temperature Radiometer (SLSTR) on-board the Sentinel-3A platform a good candidate for consistency assessments against CGLOPS geostationary-based LST. The quality of

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Issue: I1.10 the SLSTR LST product is discussed in the respective validation report (Sentinel-3-SLSTR-Level-2- Land-Validation-Report, available online at sentinel.esa.int). In general, comparison with in situ values shows that the SLSTR product exhibits a daytime accuracy of 0.81 K and 1.07 K for night- time, while its precision is 0.72 K for the daytime retrievals and 1.21 K in the case of night–time values. Comparisons with other LST products (namely LST derived from SEVIRI, GOES and MODIS) revealed that SLSTR LST is mostly consistent with those products, with mean differences within ~1 K. However, higher differences may occur in 1) heterogeneous areas; 2) higher solar angles and 3) scenes observed by the left side in the along-track direction, where viewing angles are larger. Here we extend those comparisons as follows. Data from September 2019 to August 2020 were downloaded from scihub.copernicus.eu1. Since Sentinel-3A and Sentinel-3B fly in tandem, separated by 180º, the added value of using data from both platforms is small; therefore, the analysis focused on Sentinel-3A data only. Each SLSTR LST field is re-projected into the CGLOPS LST grid, by averaging all valid SLSTR LST estimates within each CGLOPS grid cell. Reprojected LST values are only considered if all SLSTR swath pixels falling into a given CGLOPS grid cell contain valid values, thus avoiding cloud contaminated retrievals. Colocation in time is performed by linear interpolation of two consecutive CGLOPS LSTs to the SLSTR acquisition time for a given grid cell, in the case both are valid. In case one of them is not valid (e.g. if it is cloud contaminated), the estimate that is closest in time is used if the observation time difference is less than 7.5 min apart. 5.2.1 Spatial comparisons

In Figure 32, the mean CGLOPS – SLSTR LST for December-January-February (DJF) differences are shown, together with the number of matchups corresponding to each comparison. During daytime, large areas over North America, Europe, Asia and Australia show positive differences. Discrepancies over Africa are close to neutral (particularly v2.0), despite oscillating between positive and negative differences. South America shows mostly negative differences, both in the case of v1.2 and v2.0. Daytime differences are often related or enhanced by LST directional effects: in cases where the observation geometry is biased towards sunlit scenes (e.g., geostationary satellites, or polar-orbiting platforms scanning from east/west in local morning/afternoon), the observed surface temperature tends to be warmer than when viewing/illumination geometries favour shaded surfaces to be seen. It should be noted that GOES-16 (located over 75ºW) favours particularly the observation of sunlit surfaces over North America in the morning (i.e., during Sentinel-3 overpasses), since the region is then observed from south-east; this effect is attenuated over south America, observed mostly from north or north-west. Warmer/cooler CGLOPS LST when compared with SLSTR values prevail over Northern/Southern America. Over part of South America, CGLOPS LST has a small contribution from MSG 0º observations which contributes to increase LST for this time of day (see

1 SLSTR LST was downloaded using the “dhusget.sh” script (https://scihub.copernicus.eu/userguide/BatchScripting#dhusget_script); SLSTR LST are available in full-orbit files up to mid-May and in 3-minute orbit files from then onwards. For the sake of time, only SLSTR LST data available through at ESA’s rolling archive has been used in this report Document-No. CGLOPS1_QAR_LST-V2.0 © C-GLOPS Lot1 consortium Issue: I1.10 Date: 05.11.2020 Page: 70 of 125

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Issue: I1.10 section 6.1 below). Part of the overall negative differences could also be attributable to the reported SLSTR LST positive bias (when compared to ground truth) in the Validation Report (Sentinel-3- SLSTR-Level-2-Land-Validation-Report); see section 5.2.2 below. The large negative differences over the Arabian Peninsula were observed in v1.2 both in the daytime and in the night-time, which are mitigated in v2.0 with the introduction of the IODC mission. A few areas over the globe present sign switches from negative to positive mean differences in v2.0, namely the southern United States, southern tip of the Andes and Australia. The Sahel region was already exhibiting positive differences in DJF during both daytime and night time. The areas over the Gobi desert show the highest negative differences between both datasets, specially pronounce at night time. Moreover, these differences are common to both CGLOPS product versions. Regarding the June-July-August (JJA) period (Figure 33), the mean differences are generally between -1 and -2 K with some exceptions. Mountain areas show positive differences in general, such as the Rockies, the Andes, and the Himalayas. Southern Africa and Australia also exhibit positive differences. As in the DJF case, the high negative differences Arabian Peninsula in v1.2, were partly mitigated in v2.0. However, a negative difference pattern arises in v2.0 over western Sahel, for both daytime and night-time, clearly associated to the introduction of IODC in v2.0; a similar negative pattern occurs in the northwest part of the GOES disk: both are related with the decrease in surface emissivity under high view zenith angles, particularly over bare or sparsely vegetated areas (e.g., Ermida et al., 2020) . Overall, daytime differences could be explained by differences in viewing and illumination geometry, as explained above. Other causes of discrepancies between LST datasets are differences in cloud mask, ancillary datasets and on the retrieval algorithm itself.

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Figure 32 – Comparison of CGLOPS LST with SLSTR LST for December, January, February (DJF) 2019-2020. The left column shows the mean CGLOPS – SLSTR differences (in K) for daytime v2.0, daytime v1.2, night time v2.0 and night time v1.2. In the right column, the corresponding number of matchups are shown.

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Figure 33 – Comparison of CGLOPS LST with SLSTR LST for June, July, August (JJA) 2020. The left column shows the mean CGLOPS – SLSTR differences (in K) for daytime v2.0, daytime v1.2, night time v2.0 and night time v1.2. In the right column, the corresponding number of matchups are shown.

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5.2.2 In situ comparisons

Alongside the spatial consistency assessment made in the previous section, it is also necessary to assess the temporal consistency of the datasets. In the period September 2019 to December 2019, all LST datasets (in situ, CGLOPS and SLSTR) were available. All the datasets were collocated as described in the previous section: consecutive CGLOPS measurements were linearly interpolated to the SLSTR acquisition time and then compared to the in-situ estimate. A given matchup is only considered if all the datasets are available, thus ensuring the same sampling for all the comparisons. Comparing the different datasets in triple collocation-like is useful to diagnose problems with one of the datasets in case the other two show some degree of consistency with each other. For instance, in Figure 34, a time series of the matchups between all the datasets for station OZFX_CAL is shown. This station was selected as an example here since it was the only non-complying one with respect to the threshold uncertainty requirement (Table 12). In the case of night time retrievals, there is an overall good agreement, with an underestimation of in situ LSTs by all satellite retrievals, which is slightly more severe in the case of SLSTR. During daytime, there is better agreement among satellite retrievals than between satellite and in situ estimates. This simple exercise shows that this station is not representative at the satellite pixel scale, which explains why CGLOPS LST is not compliant with the requirements in this particular station.

Figure 34 – Time series of the matchups between SLSTR LST, CGLOPS LSTs (v1.2 and v2.0) and in situ LST for the case of station OZFX_CAL, for the period September to December 2019. Since the number of matchups is relatively reduced using this collocation methodology, matchups from all stations are analysed together, only separating daytime and night time cases. Both SLSTR and CGLOPS v1.2 and v2.0 are compared to situ, and the results are shown in Figure 35. All datasets tend to overestimate in situ LSTs by more than 2 K during daytime. CGLOPS v2.0 shows higher median differences, but better precision, when compared to both SLSTR and CGLOPS v1.2. During night time, results are comparable for the 3 datasets, with CGLOPS v2.0 again showing the best precision, and an accuracy similar to that of SLSTR.

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Figure 35 – Scatterplots comparing SLSTR LST (left), CGLOPS v1.2 (center) and CGLOPS v2.0 (right) with in situ matchups, using all stations in section 5.1.1, for the period September-December 2019 (for which all datasets were available). Results are separated into day (top) and night (bottom). Colors represent datapoint density.

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6 OVERLAPPING REGIONS The combination of LST estimates from different sensors poses several challenges, for instance the best way to combine estimates from different sensors when a certain area is covered by more than one sensor. Up until v2.0, the only overlapping region was located over South America (and some pixels of West Africa), in the area where the GOES and MSG 0° intersect. With the introduction of IODC, all the disks have overlapping areas. In Figure 36, the areas covered by each satellite mission are represented by different colors, and the overlapping areas are classified as “Multimission”. In this chapter, the differences between the estimates of the pair of sensors now covering the different overlapping areas are explored in the next sections, over the areas marked down in Figure 36 in magenta. While these comparisons are relevant to infer the spatial consistency of the LSTs from the individual sensors, it should be noted that several factors may introduce discrepancies. Examples are the viewing and illumination geometry, the slightly different calibration strategy of each retrieval algorithm, the pixel size, different sensor characteristics, the channels used in the GSW algorithm, the cloud mask and the surface emissivity estimate.

Figure 36 – Areas covered by the different satellite missions, taken from the LST V2.0 file corresponding to the timeslot 2020-8-15 15:00 UTC. In magenta, the limits of the overlapping areas under analysis are shown.

6.1 INTERSECTION BETWEEN GOES AND MSG (A1)

In Figure 37, the mean differences between the MSG 0º LST and the GOES-16 LST are shown, for January and July 2020, every 3 hours, for area A1 (cf. Figure 36). The night-time timeslots (0 to 9 UTC) present a relatively constant pattern of mostly positive differences (below 2 K) although patches of equally weak negative differences (also below -2 K) may also be seen. Morning and afternoon slots (i.e., 12 UTC and 18 UTC, respectively) present LST differences of same magnitude but opposite signs, a clear effect of viewing and illumination geometries, since MSG will tend to

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Issue: I1.10 observe a wider fraction of sunlit surfaces during local morning and shaded areas after mid-day. Differences are roughly consistent between January and July, with January exhibiting larger areas that were masked out due to persistent cloudiness. Larger localized differences occur in the vicinity of these masked areas, suggesting these differences could be caused by undetected clouds/cloud edges from both sensors. In Figure 38, the scatterplots comparing the mean LSTs from both sensors are shown for January and July, and reveal that MSG LST is generally warmer than GOES-16 over area A1, with 휇 = 0.4 K in January and 휇 = 0.3 K in July. In each row, the same scatterplot is coloured according to the VZA of MSG 0º (left side) and GOES-16 (right side). Over the considered area, GOES-16 VZAs are much lower ranging between around 30º in the northwest to around 50º in the southeast, while MSG 0º VZAs range between 50º to around 70º. Although some discontinuities due do the use of a different class of VZA in the GSW algorithm are clearly discernible in the maps of Figure 38 (especially in the bottom row of the July maps, over the tropical area), the scatterplots do not clearly show that the higher differences depend on the VZA alone.

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Figure 37 – Mean differences between LST from MSG 0º and GOES-16, for January 2020 (top) and July 2020 (bottom). Differences are shown every 3h (UTC).

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Figure 38 – Scatterplots comparing mean LST from MSG with mean LST from GOES-16 in the overlapping region A1. Mean LSTs correspond to the values used in Figure 37 (i.e. every 3h). Scatterplots are colored according to VZA: on the left using the MSG 0º VZA, on the right using the GOES-16 VZA. In the top row are shown the results for January and on the bottom row, results for July.

6.2 INTERSECTION BETWEEN MSG 0° AND IODC (A2)

In Figure 39, the mean differences between the MSG 0º LST and the MSG IODC LST are shown, for January and July 2020, every 3 hours, for area A2 (cf. Figure 34). Overall, a pattern of negative differences in the eastern areas and positive differences in the western areas may be observed. This contrast is more pronounced during the early morning, while in the afternoon MSG 0º LST is only colder than IODC over the Arabian Peninsula and in the Horn of Africa areas. A pattern of relatively strong positive differences (reaching up to 10 K) over West Sahara may be observed in the comparisons, being more pronounced in daytime/July. There are several reasons for such high differences: on one hand the decrease of surface emissivity over bare/sparsely vegetated areas

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Issue: I1.10 under high view angles may account for apparent cooler LST values derived from IODC (a similar effect is seen in MSG 0º LST values over the Arabian Peninsula); on the other hand, the atmospheric correction may be underestimated under high satellite viewing angle, especially under very high water vapour and possibly aerosol loads, which are common in July in West Africa.

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Figure 39 – Mean differences between LST from MSG 0º and IODC, for January 2020 (top) and July 2020 (bottom). Differences are shown every 3h (UTC).

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In Figure 40, the scatterplots corresponding to the spatial distribution shown in the panels of Figure 39 are shown, for January and July. The overall agreement between the estimates of both sensors is generally good, with a 휇 = 0.2 K in January and 휇 = 0.7 K in July, indicating that IODC LST is generally colder than MSG 0º, a result that is clearly influenced by the abovementioned area. Since the optical path is higher in the case of IODC retrievals over that area, and since no specific correction for that effect is included in the GSW retrieval, it is somehow expected that higher attenuation would lead to lower brightness temperatures, and consequently lower LSTs.

Figure 40 – Scatterplots comparing mean LST from MSG 0º with mean LST from IODC in the overlapping region A2. Mean LSTs correspond to the values used in Figure 39 (i.e. every 3h). Scatterplots are colored according to VZA: on the left using the MSG 0º VZA, on the right using the IODC VZA. In the top row are shown the results for January and on the bottom row, results for July.

6.3 INTERSECTION BETWEEN IODC AND HIMAWARI (A3)

Figure 41 shows the mean differences between the MSG IODC LST and the Himawari LST, for January and July 2020, every 3 hours, for area A3 (cf. Figure 36). This is a relatively smaller

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Issue: I1.10 overlapping area, since the southern part of the intersection occurs mostly over the Indian Ocean. It is also rather complex in terms of climatic conditions, with the SE Asia areas mostly dominated by tropical conditions (frequent cloud cover, precipitation) and the Tibetan Plateau in the northern part (complex orography, snow-covered). Since July roughly corresponds to the onset of the Asian Monsoon, sampling is rather limited in that month. Focusing the analysis in January, it is possible to infer that Himawari LST is frequently cooler that IODC values over India, although thus region is observed under extreme high Himawari view angles. The illumination/viewing geometry arguments mentioned in previous sections also apply for this region: during morning (e.g. 03 UTC) Himawari faces mostly illuminated surfaces, while IODC observes a higher fraction of shadowed areas; the opposite occurs in the afternoon (e.g. 09 UTC), when the Sun moves towards west.

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Figure 41 – Mean differences between LST from IODC and Himawari, for January 2020 (top) and July 2020 (bottom). Differences are shown every 3h (UTC).

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Figure 42 – Scatterplots comparing mean LST from MSG IODC with mean LST from Himawari in the overlapping region A3. Mean LSTs correspond to the values used in Figure 36 (i.e. every 3h). Scatterplots are colored according to VZA: on the left using the MSG 0º VZA, on the right using the IODC VZA. In the top row are shown the results for January and on the bottom row, results for July.

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7 CONCLUSIONS

The CGLOPS LST product main goal is to supply its users with long term global time series of Land Surface Temperature, and its most distinct feature compared to other global LST products is the fine sampling of the diurnal cycle. So far, the product relies on a series of measurements made by imagers onboard geostationary satellites operated by different agencies (NOAA, EUMETSAT and JMA). Each agency has its own schedule, and improved satellites have been progressively launched by each agency, namely Himawari-8 by JMA and GOES-16 by NOAA. New sensors onboard these platforms allow the use of more accurate retrieval algorithms, particularly because they allow higher spectral, temporal and spatial resolutions. This poses challenges to operational products such as CGLOPS LST, because retrievals from all satellites used to produce LST estimates should be as harmonized as possible. It is therefore of paramount importance that the algorithms are continuously developed to improve retrievals from each individual sensor, but also to improve the harmonization between estimates from different sensors, especially over areas where measurements from different sensors overlap. It is also desirable that this harmonization extends to the different land surface products provided by CGLOPS, which use a common set of inputs for their retrievals, whenever required and/or possible. With all this in mind, a new version of the CGLOPS LST product has been prepared. The changes in this version include the introduction of the CGLOPS FCOVER as an additional input to estimate surface emissivity for the sensors that are processed specifically for the product. This improves compatibility with MSG LST, since the LSA-SAF takes into account a dynamic vegetation and therefore a dynamic emissivity in their LST processing chain. All sensors now used by CGLOPS LST consider the Generalized Split Windows (GSW) algorithm, which combines brightness temperatures from adjacent thermal infrared channels to improve atmospheric correction needed to estimate LST. A harmonized calibration methodology is now used for the sensors specifically processed for CGLOPS LST (i.e. GOES-16 and Himawari-8). These changes on spatial harmonization of the estimates from different sensors are especially relevant for this version since the MSG Indian Ocean Data Coverage (IODC) mission is included in CGLOPS LST from this product version onwards. IODC increases the area of overlap by different sensors (e.g., since IODC has a large overlapping area with the MSG 0º mission, and another one with part of the Himawari-8 disk). Also with this in mind, the choice of timeslots used to obtain the hourly LST estimates was slightly changed in this product version, in order to accommodate the changes in satellite swath configuration for the most recent sensors such as the ABI on GOES-16 and AHI on Himawari-8. The changes minimize the acquisition time differences in overlapping regions in the current configuration, but they introduce discontinuities in time series obtained in regions covered by the MSG 0º mission. Users are therefore discouraged to combine data from different product versions to analyse long time series. In this report, each change was analysed individually and then a final assessment of the upgrade was performed, namely through comparisons with external datasets (in situ and SLSTR LST), but also through a comparison of the retrievals made by each satellite mission within the overlapping areas.

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The introduction of CGLOPS FCOVER in the processing chain of GOES-16 and Himawari-8 allows for a seasonally varying emissivity, according to the vegetation cover of each surface type. Vegetated areas have higher emissivities, which in turn yield lower LSTs. The analysis showed that some biomes were not well characterized by the static FCOVER value used in version 1.2, not only because their seasonal variability was not represented at all (such as in the case of Cerrado in Brazil) but also due to systematic differences lasting the whole year (such as over the centre of Australia). Although these misrepresentations affect the estimation of surface emissivity, the impact on LST retrieval is larger for drier atmospheres, which are generally associated to less vegetated biomes. This means that there is a disproportionate impact between those cases where the static FCOVER was positively biased and those where the bias was negative. In general, when the CGLOPS FCOVER is lower than the prescribed static value in v1.2 (such as in the case of the centre of Australia – see Figure 13 and Figure 14), the retrieved LST will be significantly increased in v2.0. If the bias had the opposite sign, the decrease in LST may be smaller (e.g. Eastern China in July, in the same figures). The change in the algorithm coefficients was needed in order to mitigate systematic biases between estimates from the different sensors used in CGLOPS LST, attributable to differences in calibration methodology. This change had more impact on Himawari-8 retrievals, since the initial GOES-16 calibration was already performed obeying to the current criteria (but still with a different set of atmospheric profiles). Himawari-8, on the other hand, was calibrated with a database on which more extreme situations (such as very warm and dry surfaces) were not so well represented. This change impacted mostly retrievals over Australia in the southern hemisphere summer. The increase in spatial coverage was quantified by assessing the mean amount of land pixels providing valid LST values, for a 15-day period in January 2020. The introduction of the estimates provided by the MSG IODC mission increases the spatial coverage of the CGLOPS LST product from 28.2% to 30.5%. It should be noted that there was a large cloud cover in the period under analysis, and therefore the real increase should be larger, since most of the area that is now included corresponds to the desert areas over Iran, Pakistan, India and Arabia (and therefore with much fewer clouds throughout the year). The inclusion of IODC also increased the area of overlap between the field of view of the different sensors used in CGLOPS LST, which reduces uncertainty over these areas. Moreover, the time slots used to extract the LSTs for the MSG disk were changed in order to minimize the acquisition time differences in overlapping areas, taking into account the swath geometry of the sensors currently used in CGLOPS LST. This change introduces differences in the LSTs estimated over the MSG 0º area. Since in the new product version the timeslot is 15 min later, the differences are positive when temperatures are rising in the morning, and negative when they cool down in the afternoon and night time. The comparison of LST estimates from both product versions v1.2 and v2.0 against independent measurements is essential to demonstrate the added value of the proposed changes. This Quality Assessment Report extended the procedure normally adopted in routine validation exercises of CGLOPS LST operational product. Comparisons to in situ LST observations included more stations:

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Issue: I1.10 a total of 32 stations from the SURFRAD, BSRN, EFDC, GBOV and OZFlux networks were used. CGLOPS v1.2 and v2.0 retrievals made during the full year of 2019 were compared to the corresponding in situ estimates. We carefully assessed the most appropriate neighboring pixel for in situ comparisons, as extensively detailed in ANNEX 1. Comparisons revealed an overall improvement of statistics: the median difference between satellite and in-situ estimates decreased from -0.3 K to -0.0 K; while both the median of the absolute residuals and the RMSD remained constant (1.4 K and 2.9 K, respectively). However, these overall results are station-dependent, with many of the stations located in, e. g., regions with seasonally varying vegetation registering an accuracy improvement. A comparison with LSTs derived from the SLSTR sensor onboard Sentinel- 3 was performed to further assess the spatial and temporal consistencies of the product. The spatial comparisons showed that including IODC mitigated some of the strongest negative differences observed over the Arabian Peninsula in the daytime, as IODC is yielding higher LSTs (cf. Figure 39), likely due to the fact that it observes that region with lower VZAs. Thus, since the merging algorithm gives more weight to observations made with lower VZAs, the bias towards SLSTR is mitigated. The same kind of differences are still present in the July daytime cases, over drier areas observed with high viewing angles (North America, Tibetan Plateau). A negative bias is also observed over West Africa in JJA, for both daytime and nighttime retrievals. This biases still require further investigation to be done. The detailed study of overlapping areas showed that, even with a common strategy to determine surface emissivity and with a similar calibration strategy, significant differences among sensors still remain. The differences are mostly dominated by directional effects, i.e. from the fact that each scene in the overlapping area is observed by a pair of sensors with different viewing and illumination geometries. In general, when the Sun is behind the satellite, a given scene will appear to be warmer than when the satellite observes from the opposite side, since in the latter case it observes a higher fraction of shaded areas. Indeed, in the example provided in Figure 11, during the morning, MSG yields temperatures up to 3 K warmer than the GOES-16 counterpart, while in the afternoon the differences change in sign. These differences are not well accounted for in the merging algorithm and research is still ongoing to reduce these differences in fully harmonized LST products. These effects are noticeable in the marginal quality decreases over stations within the MSGs’ overlapping regions, such as Toravere, Cabauw and Gobabeb. PAY and BUD on the other hand, showed an improvement of the statistics, and they are also observed by both MSG missions in v2.0. It was also noted here that there are differences in the cloud masking algorithms used for each sensor that can cause differences in cloud classification. The cloud mask for GOES-16 and Himawari use a simplified version of the NWC-SAF algorithm, detailed in ANNEX3 of the ATBD [CGLOPS1_ATBD_LST-V2.0]. It is acknowledged that this is a caveat of the current product and that extending the NWC SAF processing to GOES-R and Himawari (as foreseen in future releases of that software package) will benefit CGLOPS products. Generally, cloud contaminated pixels appear as negative outliers in comparisons of satellite LST with in situ estimates. One of the reasons that robust statistics are preferred (i.e. median instead of the mean) in the validation of these products is to avoid the effect of clouds in the final accuracy estimate.

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In situ observations are often not representative of LST at pixel scales. The deployment of further stations will be most welcome, although these must take into account representativity issues at the pixel level, or they risk being unusable in exercises such as this report. Furthermore, the impacts reported in this document are significant enough to justify a reprocessing of the CGLOPS LST product, back to at least to the date when Himawari-8 went into operations, in order to provide a more reliable and complete global LST dataset.

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8 RECOMMENDATIONS

The quality of the CGLOPS LST product is strongly influenced by how effective the cloud masking procedure is. To obtain better validation statistics when comparing CGLOPS LSTs against in situ estimates, it proved beneficial to constrain individual measurements regarding its cloud classification as much as possible. In other words, the stricter we are, the better quality we will get, at the cost of sampling (as more suspicious retrievals being excluded). Therefore, users are advised to weight the levels of accuracy and uncertainty with the sampling level that fit their applications, using the information provided in each data file to perform the necessary constraints. The PERCENT_PROC_PIXELS gives a quantitative estimate of the percentage of pixels from all the missions falling into the final CGLOPS gridbox that are considered as “clear”. Users looking for maximum quality should only use a given LST estimate if this value is 100%. Moreover, three pre- selected thresholds were coded into the quality flag that users can rely on (see the Annex 2 of the PUM [CGLOPS1_PUM_LST-V2.0] for more details on the quality flag). In this respect, the production team has received some user feedback stating that the quality flag is too complicated to decode. We will take this feedback into account by providing an easier-to-use quality flag in a future release, so that more users can access this information. Future product improvements will likely focus on mitigation of the different biases found over the course of this report. Although this validation exercise increased the number of in situ stations, not all of them were really useful to diagnose possible problems with the algorithm; their relatively poor results mostly reflect their lack of representativity at the pixel level. The inter-comparisons between LST retrievals from different sensors / missions revealed that most discrepancies can be explained by different viewing / illumination geometries. Research on methods to overcome these constrains is ongoing, aiming the development of new methodologies to yield fully harmonized multi-mission LST datasets.

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9 REFERENCES Augustine, J.A., Hodges, G.B., Cornwall, C.R., Michalsky, J.J., Medina, C.I., Augustine, J.A., Hodges, G.B., Cornwall, C.R., Michalsky, J.J., Medina, C.I., 2005. An Update on SURFRAD— The GCOS Surface Radiation Budget Network for the Continental United States. J. Atmos. Ocean. Technol. 22, 1460–1472. https://doi.org/10.1175/JTECH1806.1 Bai, G., Dash, J., Brown, L., Meier, C., Lerebourg, C., Ronco, E., Lamquin, N., Bruniquel, V., Clerici, M., Gobron, N., 2019. GBOV (Ground-Based Observation for Validation): A Copernicus Service for Validation of Vegetation Land Products, in: International Geoscience and Remote Sensing Symposium (IGARSS). https://doi.org/10.1109/IGARSS.2019.8898634 Beck, H.E., Zimmermann, N.E., McVicar, T.R., Vergopolan, N., Berg, A., Wood, E.F., 2018. Present and future köppen-geiger climate classification maps at 1-km resolution. Sci. Data. https://doi.org/10.1038/sdata.2018.214 Beringer, J., Hutley, L.B., McHugh, I., Arndt, S.K., Campbell, D., Cleugh, H.A., Cleverly, J., De Dios, V.R., Eamus, D., Evans, B., Ewenz, C., Grace, P., Griebel, A., Haverd, V., Hinko-Najera, N., Huete, A., Isaac, P., Kanniah, K., Leuning, R., Liddell, M.J., MacFarlane, C., Meyer, W., Moore, C., Pendall, E., Phillips, A., Phillips, R.L., Prober, S.M., Restrepo-Coupe, N., Rutledge, S., Schroder, I., Silberstein, R., Southall, P., Sun Yee, M., Tapper, N.J., Van Gorsel, E., Vote, C., Walker, J., Wardlaw, T., 2016. An introduction to the Australian and New Zealand flux tower network - OzFlux. Biogeosciences. https://doi.org/10.5194/bg-13-5895-2016 Driemel, A., Augustine, J., Behrens, K., Colle, S., Cox, C., Cuevas-Agulló, E., Denn, F.M., Duprat, T., Fukuda, M., Grobe, H., Haeffelin, M., Hodges, G., Hyett, N., Ijima, O., Kallis, A., Knap, W., Kustov, V., Long, C.N., Longenecker, D., Lupi, A., Maturilli, M., Mimouni, M., Ntsangwane, L., Ogihara, H., Olano, X., Olefs, M., Omori, M., Passamani, L., Bueno Pereira, E., Schmithüsen, H., Schumacher, S., Sieger, R., Tamlyn, J., Vogt, R., Vuilleumier, L., Xia, X., Ohmura, A., König- Langlo, G., 2018. Baseline Surface Radiation Network (BSRN): Structure and data description (1992-2017). Earth Syst. Sci. Data. https://doi.org/10.5194/essd-10-1491-2018 Ermida, S.L., Soares, P., Mantas, V., Göttsche, F.-M., Trigo, I.F., 2020. Google Earth Engine Open- Source Code for Land Surface Temperature Estimation from the Landsat Series. Remote Sens. 12, 1471. https://doi.org/10.3390/rs12091471 Ermida, S.L., Trigo, I.F., DaCamara, C.C., Pires, A.C., 2018a. A methodology to simulate LST directional effects based on parametric models and landscape properties. Remote Sens. https://doi.org/10.3390/rs10071114 Ermida, S.L., Trigo, I.F., DaCamara, C.C., Roujean, J.L., 2018b. Assessing the potential of parametric models to correct directional effects on local to global remotely sensed LST. Remote Sens. Environ. https://doi.org/10.1016/j.rse.2018.02.066 Freitas, S.C., Trigo, I.F., Macedo, J., Barroso, C., Silva, R., Perdigão, R., 2013. Land surface temperature from multiple geostationary satellites. Int. J. Remote Sens. 34, 3051–3068. https://doi.org/10.1080/01431161.2012.716925 García-Haro, F.J., Sommer, S., Kemper, T., García-Haro, F.J., Sommer, S., Kemper, T., 2005. A new tool for variable multiple endmember spectral mixture analysis (VMESMA). Int. J. Remote Sens. 26, 2135–2162. https://doi.org/10.1080/01431160512331337817 Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27. https://doi.org/10.1016/J.RSE.2017.06.031

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Guillevic, P., Göttsche, F., Nickeson, J., Hulley, G., Ghent, D., Yu, Y., Trigo, I., Hook, S., Sobrino, J.A., Remedios, J., Román, M. & Camacho, F., 2018. Land Surface Temperature Product Validation Best Practice Protocol. Version 1.1. In P. Guillevic, F. Göttsche, J. Nickeson & M. Román (Eds.), p. 58, Land Product Validation Subgroup (WGCV/CEOS), doi:10.5067/doc/ceoswgcv/lpv/lst.001 Hulley, G.C., Hook, S.J., Abbott, E., Malakar, N., Islam, T., Abrams, M., 2015. The ASTER Global Emissivity Dataset (ASTER GED): Mapping Earth’s emissivity at 100 meter spatial scale. Geophys. Res. Lett. https://doi.org/10.1002/2015GL065564 Hulley, G.C., Hook, S.J., Manning, E., Lee, S.-Y., Fetzer, E., 2009. Validation of the Atmospheric Infrared Sounder (AIRS) version 5 land surface emissivity product over the Namib and Kalahari deserts. J. Geophys. Res. 114, D19104. https://doi.org/10.1029/2009JD012351 Isaac, P., Cleverly, J., McHugh, I., Van Gorsel, E., Ewenz, C., Beringer, J., 2017. OzFlux data: Network integration from collection to curation. Biogeosciences. https://doi.org/10.5194/bg-14- 2903-2017 Malakar, N.K., Hulley, G.C., Hook, S.J., Laraby, K., Cook, M., Schott, J.R., 2018. An Operational Land Surface Temperature Product for Landsat Thermal Data: Methodology and Validation. IEEE Trans. Geosci. Remote Sens. 56, 5717–5735. https://doi.org/10.1109/TGRS.2018.2824828 Martins, J.P.A., Trigo, I.F., Bento, V.A., da Camara, C., 2016. A physically constrained calibration database for land surface temperature using infrared retrieval algorithms. Remote Sens. 8. https://doi.org/10.3390/rs8100808 Ogawa, K., Schmugge, T., Rokugawa, S., 2008. Estimating broadband emissivity of arid regions and its seasonal variations using thermal infrared remote sensing, in: IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2007.913213 Roujean, J.-L., Leroy, M., Deschamps, P.-Y., 1992. A bidirectional reflectance model of the Earth’s surface for the correction of remote sensing data. J. Geophys. Res. https://doi.org/10.1029/92jd01411 Schmithusen, H., Koppe, R., Sieger, R., Konig-Langlo, G., 2019. BSRN Toolbox V2.5 - a tool to create quality checked output files from BSRN datasets and station-to-archive files. Alfred Wegener Institute, Helmholtz Cent. Polar Mar. Res. Bremerhaven. https://doi.org/10.1594/PANGAEA.901332 Trigo, I.F., Monteiro, I.T., Olesen, F., Kabsch, E., 2008a. An assessment of remotely sensed land surface temperature. J. Geophys. Res. 113, 1–12. https://doi.org/10.1029/2008JD010035 Trigo, I.F., Peres, L.F., DaCamara, C.C., Freitas, S.C., 2008b. Thermal land surface emissivity retrieved from SEVIRI/Meteosat. IEEE Trans. Geosci. Remote Sens. 46, 307–315. https://doi.org/10.1109/TGRS.2007.905197 Verger, A., Baret, F., Weiss, M., 2014. Near real-time vegetation monitoring at global scale. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. https://doi.org/10.1109/JSTARS.2014.2328632

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ANNEX 1 - IN SITU STATION REPRESENTATIVITY

The representativeness of station sensors’ field-of-view-scale measurements at the pixel level is one of the major challenges when validating LST products (in fact, this is an issue for mostly all relevant meteorological variables). In this section, some attention is given to this issue, giving some context to the comparisons presented in section 5.1. The following pages show how the validation statistics vary when the pixels over a 5x5 pixel window around the station are used to perform the matchups. The nearest pixel is chosen by default, unless pixels in the vicinity exhibit more favourable statistics. Although subjective, this choice is always backed up by information on the landscape, thermal heterogeneity (not shown) and orography (not shown).

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