Copernicus Global Land Operations Cryosphere and Water C-GLOPS2 Framework Service Contract N 199496 (JRC)

Copernicus Global Land Operations Lot 2 Date August 16, 2020

August 16, 2020

QUALITY ASSESSMENT REPORT

LAKE SURFACE WATER TEMPERATURE

1KM PRODUCTS

VERSION 1.0.1, 1.0.2 and 1.1.0

ISSUE I1.09

Organization name of lead contractor for this deliverable: Book Captain: Laura Carrea (University of Reading) Contributing Authors: Chris Merchant (University of Reading) Copernicus Global Land Operations Lot 2 Date August 16, 2020

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

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Document Release Sheet

Book Captain: Laura Carrea (University of Reading) Sign Date Approval: Sign Date Endorsement: Sign Date Distribution: Public

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Change Record

Issue/Rev Date Page(s) Description of Change Release I1.01: 26.06.2018 21 First Version for QAR Lake Surface Water Tem- perature products Version 1.1.0 I1.02: 09.11.2017 21 Internal review I1.03: 27.06.2018 21 Internal review I1.04: 26.06.2018 33 Internal review I1.05: 22.03.2019 36 Corrections according to Review 4 I1.06: 22.05.2019 34 Final minor corrections according to Review 4 I1.07: 30.11.2019 46 Inclusion of the validation of the LSWT product from Sentinel-3A SLSTR instrument. Update of the C-GLOPS website link at the University of Reading. Introduction of SLSTR-A LSWT v1.0.2 for reprocessed data. I1.08: 22.04.2020 46 Introduction of SLSTR-A/B LSWT v1.1.0. I1.09: 01.08.2020 67 Assessment of the reprocessed SLSTR-A NRT LSWT v1.0.1 product (v1.0.2) and assessment of the SLSTR-AB 10-day product (v1.1.0).

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Contents

1 Background of the document 14 1.1 Executivesummary...... 14 1.2 Scope and objectives ...... 14 1.3 Contentofthedocument...... 15 1.4 Relateddocuments...... 15 1.4.1 Applicable documents ...... 15 1.4.2 Input ...... 15 1.4.3 Output ...... 16 1.4.4 Externaldocuments ...... 16

2 Review of user requirements 16

3 Quality assessment method 16 3.1 Overallprocedure...... 16 3.1.1 Advanced Along-Track Scanning Radiometer (AATSR) LSWT ...... 17 3.1.2 SLSTR-A LSWT ...... 17 3.1.3 SLSTR-A-Sentinel3-B Sea and Land Surface Temperature Radiometer (SLSTR- B)LSWT...... 18 3.2 Satellitereferenceproduct...... 18 3.3 Insitureferenceproduct...... 18

4Results 20 4.1 Validation of L2P LSWT ...... 20 4.1.1 Validation of L2P AATSR LSWT ...... 20 4.1.2 Validation of L2P SLSTR-A LSWT ...... 23 4.1.3 L2P LSWT validation discussion ...... 25 4.2 Validation of the C-GLOPS 10-day LSWT ...... 27 4.2.1 Validation of C-GLOPS AATSR LSWT ...... 27 4.2.2 Validation of C-GLOPS SLSTR-A LSWT ...... 33 4.2.3 10-day LSWT validation discussion ...... 38 4.3 Validation of the C-GLOPS LSWT uncertainty ...... 39 4.3.1 Validation of the C-GLOPS AATSR LSWT uncertainty ...... 39 4.3.2 Validation of the C-GLOPS SLSTR-A LSWT uncertainty ...... 39 4.4 Validation of C-GLOPS SLSTR-A NRT LSWT ...... 41 4.5 Assessment of the SLSTR-A LSWT reprocessed product ...... 43 4.6 Assessment of the SLSTR-AB LSWT product ...... 51 4.7 Visual inspection ...... 58 4.7.1 Spatial AATSR LSWT ...... 58 4.7.2 Spatial SLSTR-A LSWT ...... 61 4.7.3 Spatial SLSTR-AB LSWT ...... 62

5 Summary 63

6 Limitations and known issues 63

7 Recommendation 64

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A In situ data sources 65

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List of Figures

1 Geographical distribution of the in-situ observation sites used for the validation of theC-GLOPSproductfromtheAATSRinstrument...... 19 2 Geographical distribution of the in-situ observation sites used for the validation of theC-GLOPSproductfromtheSLSTR-Ainstrument...... 19 3 Example of quality control applied to in-situ measurements for Lake Huron in Canada at lat=45.54 lon=-81.01 for year 2002. The red line represents the data provided and the blue line represents the results of the quality control applied within the GloboLakes project and used for the C-GLOPS validation...... 20 4 Location of the site of in-situ measurements for Lake Simcoe in Canada...... 21 5 L2P LSWT validation for Lake Simcoe in Canada for the year 2009 (left) and 2010 (right) where all the in-situ daily mean temperatures (black line), in-situ matches (white dots), satellite matches (coloured dots according to the quality levels), satellite (AATSR) minus in-situ matches (green line) and the climatology with on standard deviation are reported...... 22 6 Location of the site of in-situ measurements for Lake Itumbiara in Brazil and L2P validation for the year 2010 where all the in-situ daily mean temperatures (black line), in-situ matches (white dots), satellite matches (coloured dots according to the quality levels), satellite (AATSR) minus in-situ matches (green line) and the climatology with on standard deviation are reported...... 22 7 L2P LSWT validation for Lake Simcoe in Canada for the year 2017 (left) and 2018 (right) where all the in-situ daily mean temperatures (black line), in-situ matches (white dots), satellite matches (coloured dots according to the quality levels), satellite (SLSTR-A) minus in-situ matches (green line) and the climatology with on standard deviation are reported...... 24 8 Location of the site of in-situ measurements for Lake Tahoe in USA and L2P validation for the year 2018 where all the in-situ daily mean temperatures (black line), in-situ matches (white dots), satellite matches (coloured dots according to the quality levels), satellite (SLSTR-A) minus in-situ matches (green line) and the climatology with on standard deviation are reported...... 24 9 Time series (left hand side) of the 10-day in-situ measurements and the C-GLOPS AATSR product for the two sites shown on the right hand side on the lake Superior in Canada/USA. The left hand side plots show 10-day mean in-situ observations (black line), the C-GLOPS LSWT matches (coloured dots according to quality levels) and the di↵erencesatelliteminusin-situobservations(redline)...... 27 10 Time series (left hand side) of the 10-day in-situ measurements and the 10-day C- GLOPS AATSR product for the two sites shown on the right hand side on the lake Huron in Canada/USA. The left hand side plots show 10-day mean in-situ observations (black line), the C-GLOPS LSWT matches (coloured dots according to quality levels) and the di↵erence satellite minus in-situ observations (red line)...... 28 11 LSWT time series (right hand side) of the 10-day in-situ measurements and the 10-day C-GLOPS AATSR product for site on the Sea of Galilee in Israel shown on the left hand side of the ﬁgure. The left hand side plots show 10-day mean in-situ observations (black line), the C-GLOPS LSWT matches (coloured dots according to quality levels) and the di↵erence satellite minus in-situ observations (red line)...... 29

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12 Time series (left hand side) of the 10-day in-situ measurements and the 10-day AATSR C-GLOPS product for the site shown on the right hand side on the Sea of Galilee in Israel. The left hand side plots show 10-day mean in-situ observations (black line), the C-GLOPS LSWT matches (coloured dots according to quality levels) and the di↵erencesatelliteminusin-situobservations(redline)...... 31 13 Plot of the di↵erence satellite minus in-situ against the in-situ measurements and of the AATSR C-GLOPS product against the in-situ measurements for all the matches andallthequalitylevels...... 32 14 Time series (left hand side) of the 10-day in-situ measurements and the SLSTR-A C- GLOPS product for the two sites shown on the right hand side on the lake Superior in Canada/USA. The left hand side plots show 10-day mean in-situ observations (black line), the C-GLOPS LSWT matches (coloured dots according to quality levels) and the di↵erencesatelliteminusin-situobservations(redline)...... 33 15 Time series (left hand side) of the 10-day in-situ measurements and the 10-day SLSTR- A C-GLOPS product for the two sites shown on the right hand side on the lake Huron in Canada/USA. The left hand side plots show 10-day mean in-situ observations (black line), the C-GLOPS LSWT matches (coloured dots according to quality levels) and the di↵erencesatelliteminusin-situobservations(redline)...... 34 16 LSWT time series (right hand side) of the 10-day in-situ measurements and the 10- day SLSTR-AC-GLOPS product for site on the Sea of Galilee in Israel shown on the left hand side of the figure. The left hand side plots show 10-day mean in-situ observations (black line), the C-GLOPS LSWT matches (coloured dots according to quality levels) and the di↵erence satellite minus in-situ observations (red line). . . . 35 17 Time series (left hand side) of the 10-day in-situ measurements and the 10-day SLSTR- A C-GLOPS product for the site shown on the right hand side on the Lake Woods in Canada. The left hand side plots show 10-day mean in-situ observations (black line), the C-GLOPS LSWT matches (coloured dots according to quality levels) and the di↵erencesatelliteminusin-situobservations(redline)...... 37 18 Plot of the di↵erence satellite minus in-situ against the in-situ measurements and of the SLSTR-A C-GLOPS product against the in-situ measurements for all the matches andallthequalitylevels...... 38 19 Distribution of and Q-Q plot for the LSWT uncertainty of lake Erie in Canada for AATSR. In the Q-Q plot the blue line is the y = x line while the red is a fitting. . . 39 20 Distribution of and Q-Q plot for the LSWT uncertainty of lake Erie in Canada for SLSTR-A. In the Q-Q plot the blue line is the y = x line while the red is a fitting. . 40 21 C-GLOPS 10-day LSWT with SLSTR-A data for Represa de Furnas in Brazil for the lake centre highlighted on the left hand side of the figure...... 41 22 C-GLOPS 10-day LSWT with SLSTR-A data for lake Bin-El-Ouidane in Morocco for the lake centre highlighted on the left hand side of the figure...... 42 23 C-GLOPS 10-day LSWT with SLSTR-A data for lake Enriquillo in Dominica Republic for the lake centre highlighted on the left hand side of the figure...... 42 24 Number of SLSTR-A granules processed for the NRT product (operational) and for the reprocessed product together with their di↵erence namely the number of granules whichwerenotprocessedfortheNRTproduct...... 43 25 Number of SLSTR-A granules which have been either newly included or reprocessed as Non Time Critical (NTC). The granules shown in the figure have valid LSWTs. . 44 26 Number of cells for which the reprocessed minus NRT di↵erence in the dekadal number of observations was positive or negative...... 45

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27 reprocessed minus NRT number of observations di↵erence for the lake Tucurui in Brazil for the dekad starting on the 11-Jul-2018...... 46 28 SLSTR-A 10-day LSWT quality levels for lake Tucurui in Brazil (11-Jul-2018 dekad). On the left hand side the NRT and on the right hand side the reprocessed product areshown...... 46 29 SLSTR-A 10-day LSWT days of observation used to generate the dekad for lake Tucurui in Brazil (11-Jul-2018 dekad). On the left hand side the NRT and on the righthandsidethereprocessedproductareshown...... 47 30 SLSTR-A 10-day LSWT for lake Tucurui in Brazil (11-Jul-2018). On the left hand side the NRT and on the right hand side the reprocessed product are shown. . . . . 47 31 SLSTR-A 10-day LSWT uncertainty for lake Tucurui in Brazil (11-Jul-2018 dekad). On the left hand side the NRT and on the right hand side the reprocessed product areshown...... 48 32 Reprocessed minus NRT number of observations di↵erence for the lake Balqash in Kazakhstan for the dekad starting on the 11-Jul-2018...... 49 33 SLSTR-A 10-day LSWT quality levels for lake Balqash in Kazakhstan (11-Jun-2018 dekad). On the left hand side the NRT and on the right hand side the reprocessed productareshown...... 49 34 SLSTR-A 10-day LSWT uncertainty for lake Balqash in Kazakhstan (11-Jun-2018 dekad). On the left hand side the NRT and on the right hand side the reprocessed productareshown...... 50 35 SLSTR-A 10-day LSWT for lake Balqash in Kazakhstan (11-Jun-2018 dekad). On the left hand side the NRT and on the right hand side the reprocessed product are shown...... 50 36 SLSTR-AB minus SLSTR-A number of observations di↵erence for the dekad starting on 01-Apr-2020...... 51 37 Number of observations for the SLSTR-A 10-day LSWT for lake Upemba in Zaire (01-Apr-2020 dekad) on the left hand side and the for the SLSTR-AB 10-day LSWT ontherighthandside...... 52 38 Quality levels for the SLSTR-A 10-day LSWT for lake Upemba in Zaire (01-Apr-2020 dekad) on the left hand side and the for the SLSTR-AB 10-day LSWT on the right handside...... 52 39 SLSTR-AB vs SLSTR-A quality levels of the LSWTs for the dekad starting on the 01-Apr-2020...... 53 40 SLSTR-AB minus SLSTR-A LSWT uncertainty di↵erence histogram for the dekad starting on the 01-Apr-2020...... 54 41 SLSTR-AB minus SLSTR-A LSWT uncertainty di↵erence histogram for the dekad starting on the 01-Apr-2020 when the LSWT quality levels are the same for both the products...... 55 42 SLSTR-AB minus SLSTR-A LSWT uncertainty di↵erence histogram for the dekad starting on the 01-Apr-2020 when the LSWT quality levels are qlA =5qlAB = 4, qlA =5qlAB =3andviceversa...... 56 43 SLSTR-AB minus SLSTR-A LSWT uncertainty di↵erence histogram for the dekad starting on the 01-Apr-2020 when the LSWT quality levels are qlA =4qlAB = 3, qlA =4qlAB =2andviceversa...... 56 44 SLSTR-AB minus SLSTR-A LSWT uncertainty di↵erence histogram for the dekad starting on the 01-Apr-2020 when the LSWT quality levels are qlA =5qlAB = 2, qlA =3qlAB =2andviceversa...... 57

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45 LSWT, uncertainty and number of observations for lake Winnipeg in Canada for the 10-day period starting on the 01-Jul-2005 with data from the AATSR instrument. . 58 46 Standard deviation of the observations, quality levels and observation time for lake Winnipeg in Canada for the 10-day period starting on the 01-Jul-2005 with data from theAATSRinstrument...... 59 47 LSWT, number and time of observations for a small area of the lake Winnipeg in Canada for the 10-day period starting on the 01-Jul-2005...... 59 48 LSWT, uncertainty and number of observations for lake Winnipeg in Canada for the 10-day period starting on the 21-Nov-2005 with AATSR data...... 60 49 Standard deviation of the observations, quality levels and observation time for lake Winnipeg in Canada for the 10-day period starting on the 21-Nov-2005 with AATSR data...... 60 50 LSWT, uncertainty and number of observations for the Aral Sea in Kazakhstan for the 10-day period starting on the 01-Jul-2005 with AATSR data...... 61 51 Standard deviation of the observations, quality levels and observation time for the Aral Sea in Kazakhstan for the 10-day period starting on the 01-Jul-2005 with AATSR data. 61 52 LSWT, uncertainty and number of observations for the lake Enriquillo in Dominican Republic and lake Azuei in Haiti for the 10-day period starting on the 21-Jun-2018 withSLSTR-Adata...... 62 53 Standard deviation of the observations, quality levels and observation time for the lake Enriquillo and lake Azuei in Dominican Republic for the 10-day period starting on the 21-Jun-2018 with SLSTR-A data...... 62 54 LSWT, uncertainty and number of observations for the lake Hirfanli in Turkey for the 10-day period starting on the 21-Jul-2020 with SLSTR-A and SLSTR-B data. . 62 55 Standard deviation of the observations, quality levels and observation time for the lake lake Hirfanli in Turkey for the 10-day period starting on the 21-Jul-2020 with SLSTR-A and SLSTR-B data...... 63

List of Tables

1 C-GLOPSLSWTproducts...... 14 2 Number of matches per L2P quality level (AATSR)...... 21 3 Median, robust standard deviation, mean and standard deviation of the di↵erence satellite minus in-situ observations for L2P AATSR LSWT validation for each quality level...... 23 4 Median, robust standard deviation, mean and standard deviation of the di↵erence satellite minus in-situ observation for L2P AATSR LSWT validation for quality level intervals...... 23 5 Number of matches per L2P quality level (SLSTR-A)...... 23 6 Median, robust standard deviation, mean and standard deviation of the di↵erence satellite minus in-situ observations for L2P SLSTR-A LSWT validation for each qual- itylevel...... 25 7 Median, robust standard deviation, mean and standard deviation of the di↵erence satellite minus in-situ observation for L2P SLSTR-A LSWT validation for quality levelintervals...... 25 8 Median, robust standard deviation, mean and standard deviation of the di↵erence satellite minus in-situ 10-day average AATSR LSWT for each lake...... 30

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9 Median, robust standard deviation, mean and standard deviation of the di↵erence satellite minus in-situ 10-day average AATSR LSWT for all the lakes...... 31 10 Median, robust standard deviation, mean and standard deviation of the di↵erence satellite minus in-situ 10-day average SLSTR-A LSWT for each lake...... 36 11 Median, robust standard deviation, mean and standard deviation of the di↵erence satellite minus in-situ 10-day average SLSTR-A LSWT for all the lakes...... 37 12 Number of cells with positive or negative number of observations di↵erence (dekad 01-Apr-2020)...... 51 13 Number of cells across all the lakes for the same quality levels for the SLSTR-A product and for the combined SLSTR-AB one for the dekad starting on the 01-Apr- 2020...... 53 14 Number of cells across all the lakes for di↵erent quality levels for the SLSTR-A product and for the combined SLSTR-AB one for the dekad starting on the 01-Apr-2020. . . 53 15 Product characteristics against user requirements for the LSWT product...... 64 16 In-situsources ...... 65

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Acronyms

AATSR Advanced Along-Track Scanning Radiometer. ARC ATSR Reprocessing for Climate. ATBD Algorithm Theoretical Basis Document.

BLI Balaton Limnological Institute.

C-GLOPS Copernicus Global Land Operations.

ECV Essential Climate Variable. EUSTACE EU Surface Temperature for All Corners of Earth.

FOC Fisheries and Oceans Canada.

GHRSST Group for High Resolution SST. GLEON Global Lake Ecological Observatory Network. GLWD Global Lakes and Wetlands Database.

KDKVI Central Transdanubian Inspectorate for Environmental Protection.

LEGOS Laboratoire d0 Etudes´ en G´eophysique et Oc´eanographie Spatiales. LSWT Lake Surface Water Temperature.

NDBC National Data Buoy Center. NIWA National Institute of Water and Atmospheric Research. NRT Near Real Time. NTC Non Time Critical.

PUM Product User Manual.

QAR Quality Assessment Report.

RSD Robust Standard Deviation.

SD Standard Deviation. SLSTR Sea and Land Surface Temperature Radiometer. SLSTR-A Sentinel3-A Sea and Land Surface Temperature Radiometer. SLSTR-AB Sentinel3-A and Sentinel3-B Sea and Land Surface Temperature Radiometer.

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SLSTR-B Sentinel3-B Sea and Land Surface Temperature Radiometer. SLU Swedish University of Agricultural Sciences.

SYKE Finnish Environment Institute.

TERC Tahoe Environmental Research Center.

UMR-CARRTEL UnitéMixte de Recherche - Centre Alpin de Recherche sur les Réseaux Trophiques des Ecosystèmes Limniques.

General deﬁnitions

L2P Geophysical variables derived from Level 1 source data on the Level 1 grid (typically the • satellite swath projection). Ancillary data and metadata added following the Group for High Resolution SST (GHRSST) Data Specification. L3U L2 data granules remapped to a regular latitude/longitude grid without combining ob- • servations from multiple source files. L3U files will typically be sparse, corresponding to a single satellite orbit.

L3C LSWT observations from a single instrument combined into a space-time grid. In this • project, a typical L3C ﬁle may contain all the observations from a single instrument in a 24-hour period.

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1 Background of the document 1.1 Executive summary The C-GLOPS Lake Water provides an optical characterization of nominally 1000 inland water bodies (listed in the Algorithm Theoretical Basis Document (ATBD)) that belong to the world’s largest (according to the Global Lakes and Wetlands Database (GLWD)) or are otherwise of speciﬁc environmental monitoring interest. The products contain four sets of parameters: lake water surface temperature, lake water reﬂectance (all wavebands that are available after atmospheric correction), turbidity (derived from suspended solids concentration estimates) and a trophic state index (derived from phytoplankton biomass by proxy of chlorophyll-a). Production and delivery of the parameters are on a set grid over 10-day intervals (starting the 1st, 11th and 21st day of each month) and mapped to a common global grid at either nominally 300m ( 0.0026) or 1000m ( 0.01) resolution. This Quality Assessment Report (QAR) describes⇠ the validation performed⇠ in precursor activities (GloboLakes) through comparison with independent in-situ measurements as well as the quality control performed during and after the processing of the LSWT products from AATSR and SLSTR- A data. The routinely validation of the SLSTR-A NRT product is performed as a comparison with the climatology. The C-GLOPS NRT LSWT from SLSTR-A (v1.0.1) from the 10-Apr-2018 until 30-Apr-2020 have been reprocessed and it is the C-GLOPS LSWT product currently available. This report includes the analysis of the improvements of the C-GLOPS SLSTR-A NRT LSWT product (v1.0.1) after the reprocessing (v1.0.2). The NRT LSWT product has the advantage to be available in a short span of time but it is generated from satellite L1 data which were not entirely consolidated and not necessarily complete at the time of the NRT processing. This report also includes an assessment of the C-GLOPS LSWT product when both SLSTR-A and SLSTR-B are used to generate the dekad (v1.1.0). The LSWT product which includes both the sensor is available from the second dekad in August 2020. A summary of the C-GLOPS LSWT products is reported in Table 1.

Table 1: C-GLOPS LSWT products.

Sensor version type period covered

AATSR v1.0.2 historical 11-May-2002 to 01-Apr-2012 SLSTR-A v1.0.1 NRT 10-Apr-2018 to 20-Aug-2020 SLSTR-A v1.0.2 reprocessed 01-Jun-2016 to 30-Apr-2020 SLSTR-A + SLSTR-B v1.1.0 NRT 21-Aug-2020 to current

1.2 Scope and objectives The document presents the results of the quality assessment of LSWT C-GLOPS product version 1.0.1 and 1.0.2 for SLSTR-A and version 1.0.2 for AATSR. It shows single examples as well as it provides a wider overview through time series and match-up analyses of LSWT product from both

Document-No. CGLOPS2 QAR LSWT 1km v1.0.1 I1.09 c C-CGLOPS2 consortium Issue: I1.09 Page 14 of 67 Copernicus Global Land Operations Lot 2 Date August 16, 2020 the AATSR and SLSTR-A sensors. Regarding the SLSTR-A NRT LSWT product, plots of the product together with the climatology is provided. The C-GLOPS SLSTR-A LSWT v1.0.2 product (from Nov-2016 to Apr-2018), part of the C- GLOPS SLSTR-A LSWT v1.0.1 product (from Apr-2018 to Dec-2018) and the historical C-GLOPS AATSR LSWT v1.0.2 product (from May-2002 to Apr-2012) have been validated against fully independent in-situ measurements. The validation is described in Sec. 4 and it has been carried out ﬁrstly on the L2P data and then on the ﬁnal 10-day C-GLOPS product. Uncertainties in the C-GLOPS product have been validated assuming an uncertainty of 0.2 K (estimated calibration uncertainty) in the independent reference data. The v1.0.2 SLSTR-A LSWT reprocessed product (from Apr-2018 to Apr-2020) has been compared to the v1.0.1 SLSTR-A LSWT NRT product to show the the di↵erences between the NRT and the consolidated product. Finally, a similar comparison has been carried out for the SLSTR-A-only LSWT product and the SLSTR-A-SLSTR-B LSWT product to assess the enhancements in the product due to the inclusion of SLSTR-B.

1.3 Content of the document This document is structured as follows:

Chapter 2 recalls the users requirements, and the expected performance • Chapter 3 describes the quality assessment method • Chapter 4 presents the results for the LSWT and its uncertainty where each of the analysis • are presented for both the LSWT of SLSTR-A and AATSR.

Chapter 5 summarises limitations and known issues • Chapter 6 o↵ers some recommendations • 1.4 Related documents 1.4.1 Applicable documents AD1: Annex I Technical Speciﬁcations JRC/IPR/2015/H.5/0026/OC to Contract Notice 2015/S 151-277962 of 7th August 2015 AD2: Appendix 1 Copernicus Global Land Component Product and Service Detailed Technical requirements to Technical Annex to Contract Notice 2015/S 151-277962 of 7th August 2015

1.4.2 Input The inputs are: the Service Speciﬁcations of the Global Component of the Copernicus Land Service with • document ID CGLOPS2 SSD the Algorithm Theoretical Basis Document of the Lake Surface Water Temperature, 1km • the Service Validation Plan of the Global Land Service •

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1.4.3 Output The output is the Product User Manual (PUM) summarizing all information about the Lake Surface Water • Temperature product, 1km, historic (AATSR) and NRT data (SLSTR-A).

1.4.4 External documents NA

2 Review of user requirements

According to the applicable document [AD2], the user’s requirements relevant for LSWT considered in this document are:

Definition The Lake Surface Water Temperature LSWT is defined as the temperature of the water at the surface of the water body (surface skin temperature). Geometric properties The baseline dataset pixel size shall be provided at resolution of 1km. The target baseline location accuracy shall be 1/3 of the at-nadir instantaneous field of view. Pixel co-ordinates shall be given for the centre of the pixel. Geographical coverage The initial window definition is aligned to the global datasets produced during the GIO phase for the most widely used output data: geographic projection: lat lon • geodetical datum: WGS84 • pixel size: 1/120 • accuracy: min 10 digits • coordinate position: pixel centre • global window coordinates: UL: 180W 90N, LR: 180E 90S • Accuracy requirements LSWT is an Essential Climate Variable (ECV) (GCOS-200, 2016) with an associated uncertainty requirement of 1 K. Temporal Definition As a baseline the physical parameter is computed by and representative of decades, i.e. for ten-day periods a decade is defined as follows: days 1 to 10, days 11 to 20 and days 21 to end of month for each month of the year.

3 Quality assessment method 3.1 Overall procedure A visual inspection of the C-GLOPS archived AATSR and SLSTR-A, the NRT SLSTR-A and the SLSTR-AB products has been carried out for few lakes and the spatial distribution of the LSWT for lake Winnipeg in Canada, for the Aral Sea in Kazakhstan, for lake Enriquillo in Dominican Republic and lake Azuei in Haiti and for lake Hirfanli in Turkey are reported in this document. Moreover, once a sucient amount of SLSTR-A LSWT data have become available the same procedure has been

Document-No. CGLOPS2 QAR LSWT 1km v1.0.1 I1.09 c C-CGLOPS2 consortium Issue: I1.09 Page 16 of 67 Copernicus Global Land Operations Lot 2 Date August 16, 2020 employed for AATSR and SLSTR-A LSWT validation. However, the validation of the two datasets has happened at di↵erent times and recently in-situ measurements from new sites have been collected. Therefore, the SLSTR-A LSWT validation has been carried out on a slightly di↵erent set of lakes. An important fact to highlight is that the retrievals of LSWT (consistently for all the instruments AATSR, SLSTR-A and SLSTR-B) are based on physics. This retrieval method has been speciﬁcally selected because this gives good reason to expect stable performance across domains in time and space that cannot be always directly validated (see the ATBD for details). For this reason, and because of the success of the SLSTR-A LSWT validation of the Jun-2016 to Dec-2018 period and of the successful comparison of the LSWT from SLSTR-A and SLSTR-B during the tandem phase, the NRT LSWT product can be only routinely checked through a comparison with the climatology.

3.1.1 AATSR LSWT Firstly, the L2P LSWT output is validated against in-situ measurements spatially selecting the closest pixel centre to the in-situ measurement within 3km allowing for imprecision in the location, and temporally within 3 hours when hourly in-situ measurements were available, otherwise the daily in-situ measurements have been used. In total, 7336 matches have been found for the L2P LSWT AATSR data from May-2002 to Apr-2012. Subsequently, the C-GLOPS 10-day LSWT product is matched against in-situ observations spatially selecting the closest pixel centre to the in-situ location (within 1km) and aggregating them in time. In total there are 5018 matches for the C- GLOPS AATSR 10-day product. This total is over two orders (taking into account that it is a 10-day product) of magnitude less than the number of buoy-satellite match-ups available over the oceans because of the scarcity of in-situ measurement system for water temperature. However, the number is sucient to indicate some aspects of quality, albeit for limited geographical coverage. The locations of the matches for the AATSR are shown in Figure 1. LSWTs are compared to the in-situ observations. Time series of both the 10-day average of the in-situ measurements were extracted and plotted in order to assess the reliability of seasonal trends, outliers, unexpected patterns of both the in-situ measurements and the C-GLOPS product. This validation of product consistency is shown in Sec. 4.2.1 and a visual inspection is carried out in Sec. 4.7. The validation of the LSWT uncertainties has been carried out comparing the C-GLOPS product with the in-situ measurements as described in Sec. 4.3.1 assuming a ﬁxed uncertainty for the in-situ measurements.

3.1.2 SLSTR-A LSWT The SLSTR-A LSWT product validation has been carried out with the same procedure described in Sec. 3.1.1. In total, 9255 matches have been found for the L2P SLSTR-A LSWT data from Jun-2016 to Dec-2018. For the 10-day SLSTR-A LSWT product 3113 matches have been found. The NRT SLSTR-A LSWT product quality is routinely monitored through a comparison with the climatology described in the ATBD. The comparison is performed for the centre point for each lake which is deﬁned as the most distant from land [Carrea et al., 2015]. The assessment of the improvements brought by reprocessing the NRT LSWT has been carried out evaluating the number of NTC L1b Sea and Land Surface Temperature Radiometer (SLSTR) granules • used in the reprocessing which were NRT in the NRT LSWT product the number of NTC L1b SLSTR granules used in the reprocessing which have not been pro- • cessed at all for the NRT LSWT product

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3.1.3 SLSTR-A-SLSTR-B LSWT The assessment of the improvements brought by including both the SLSTR-A and SLSTR-B LSWT in the C-GLOPS product has been carried out comparing the number of observations, the quality level and the uncertainty for the dekad starting on the 01-Apr-2020 and the 21-Jul-2020 although only the results for the ﬁrst 10-day period have been reported here. Regarding the SLSTR-A and SLSTR-B LSWT, an assessment of the temperatures, uncertainties and quality levels has been carried out during the tandem phase when the two instruments where in a conﬁguration such that they were observing the same scene 30 seconds apart. This unique data has o↵ered the opportunity to investigate any potential bias between the instruments which may lead to incorrect conclusion on the thermal behaviour of the lakes. The assessment is reported in the ATBD.

3.2 Satellite reference product Analyses are performed on the C-GLOPS user products,deﬁned as a 10-day aggregation of L3C LSWT products. The 10-day aggregation has been carried out on retrievals at nadir for day-time only. For lakes where in-situ measurements can be found the C-GLOPS product (from AATSR and SLSTR-A) has been extracted. The C-GLOPS AATSR LSWT product covers the period from May- 2002 to Apr-2012, while the C-GLOPS SLSTR-A LSWT product validated in this documents covers the period from Jun-2016 to Dec-2018. The rest of the SLSTR-A or SLSTR-AB LSWT product from Jan-2019 to present have not been directed validated with in-situ measurements and it is referred to as the NRT/reprocessed product. However, since the retrieval algorithm is based on physics and a careful assessment of the SLSTR-AB product has been carried out during the tandem phase of the two Sentinel satellites (reported in the ATBD) all the C-GLOPS products can be considered equally solid. Moreover, the SLSTR-AB product is here compared the SLSTR-A-only LSWT product.

3.3 In situ reference product A match-up dataset was constructed from the in-situ temperature data collected through the ATSR Reprocessing for Climate (ARC)-Lake, the GloboLakes and the EU Surface Temperature for All Corners of Earth (EUSTACE) projects. The in-situ database is updated every year also with new measurement suorces. The locations of the in-situ observation points for the validation of the AATSR LSWT product are shown in Figure 1 and they are spread in all of the continents, although the majority of the in-situ data are from North America and Europe. This dataset consists of 80 observation locations covering 32 of the C-GLOPS lakes.

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Figure 1: Geographical distribution of the in-situ observation sites used for the validation of the C-GLOPS product from the AATSR instrument.

The locations of the in-situ observation points for the validation of the SLSTR-A LSWT product are shown in Figure 2 and the dataset consists of 113 observation locations covering 43 of the C-GLOPS lakes.

Figure 2: Geographical distribution of the in-situ observation sites used for the validation of the C-GLOPS product from the SLSTR-A instrument.

Details of all the in-situ data with their sources are given in the Table 16 in the Appendix A which reports all locations for C-GLOPS where matches have been found either for AATSR or the SLSTR-A LSWT product or both. As the in-situ data are from a variety of sources, with di↵erent formats, considerable e↵ort has been put in to consolidate this data to a standard format for use in C-GLOPS, and to apply quality control measures. The quality control has been applied in both automatic and manual way to remove the most suspicious data. An example of the output of the quality control procedures applied is shown in Figure 3 showing the in-situ measurements for year 2002 of one site on the Lake Huron in Canada. The climatology with one standard deviation is reported for reference.

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Figure 3: Example of quality control applied to in-situ measurements for Lake Huron in Canada at lat=45.54 lon=-81.01 for year 2002. The red line represents the data provided and the blue line represents the results of the quality control applied within the GloboLakes project and used for the C-GLOPS validation.

Temporally, some of the in-situ measurements are hourly or sub-hourly, while others are daily or less frequent to up to 3 or 4 times a year. For some lakes, the daily measurements are daily means and for others it is not clear the time of the day when the measurement was taken. Spatially, for some of the lakes the location is not exact and may be di↵erent from year to year.

4 Results 4.1 Validation of L2P LSWT The validation of the L2P LSWT for each lake site includes a consistency check of the time series for both the in-situ measurements and the C-GLOPS product. The time series of the L2P LSWT product are shown together with the uncertainty and the quality levels deﬁned in the ATBD. Basic statistics from the comparisons with in-situ observations are presented in Table 4 showing Mean di↵erence: the average di↵erence between the satellite observations and the in-situ • measurements SD of the di↵erence: the standard deviation of the di↵erence between the satellite observations • and the in-situ measurements Median di↵erence: the median of di↵erence between the satellite observations and the in-situ • measurements RSD of the di↵erence: the robust standard deviation of the di↵erence between the satellite • observations and the in-situ measurements computed as the median of the di↵erence between the di↵erence and the median of the di↵erence corrected by a factor.

4.1.1 Validation of L2P AATSR LSWT The validation of the L2P LSWT from the AATSR instrument has been carried out with 7336 matches where all the quality levels (deﬁned in the ATBD) of the L2P LSWT have been included.

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The matches per quality level are reported in Table 2 showing that the majority of data have quality level 4 or 5. Quality level 1 indicates bad data.

Table 2: Number of matches per L2P quality level (AATSR).

QL=5 QL=4 QL=3 QL=2 QL=1 N 2749 1827 900 308 1552

The left hand side of Figure 5 shows an example of time series plot for the year 2009 and the right hand side for the year 2010 for the site on Lake Simcoe in Canada shown in Figure 4. The figures show all the in-situ daily mean temperatures, the in-situ matches, the L2P matches, the di↵erence satellite minus in-situ temperature and the climatology with one standard deviation as a reference. For the year 2009, the greatest di↵erence is for a L2P quality level = 2 (the lowest quality that might be usable), while for the year 2010 the greatest di↵erence is for a L2P quality level = 1, which is a flag for suspected bad data and therefore excluded from the temporal aggregation to generate the C-GLOPS product. Figure 6 shows the same plot for the site of Lake Itumbiara in Brazil shown. Also in this case, better quality levels o↵er a smaller di↵erence with the in-situ measurements. The quality levels indicate the confidence in the retrieval and good quality observation may have higher uncertainties than lower quality level observations as it can be noticed for the figures described in the current section.

Figure 4: Location of the site of in-situ measurements for Lake Simcoe in Canada.

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Figure 5: L2P LSWT validation for Lake Simcoe in Canada for the year 2009 (left) and 2010 (right) where all the in-situ daily mean temperatures (black line), in-situ matches (white dots), satellite matches (coloured dots according to the quality levels), satellite (AATSR) minus in-situ matches (green line) and the climatology with on standard deviation are reported.

Figure 6: Location of the site of in-situ measurements for Lake Itumbiara in Brazil and L2P validation for the year 2010 where all the in-situ daily mean temperatures (black line), in-situ matches (white dots), satellite matches (coloured dots according to the quality levels), satellite (AATSR) minus in-situ matches (green line) and the climatology with on standard deviation are reported.

The statistics of the validation are reported in Table 3 for each quality level and in Table 4 for all quality levels greater than as indicated in the first column. Generally, the median and the Robust Standard Deviation (RSD) are less influenced by large di↵erences arising occasionally from cloud detection or large diurnal stratification events, and is more indicative of the capabilities of the retrieval algorithm than is the conventional mean and Standard Deviation (SD).

Clearly the best results are for the most acceptable quality levels, namely ql=4,5.

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Table 3: Median, robust standard deviation, mean and standard deviation of the di↵erence satellite minus in-situ observations for L2P AATSR LSWT validation for each quality level.

Median RSD Mean SD N ql=5 -0.300 0.430 -0.389 0.935 2749 ql=4 -0.440 0.652 -0.590 1.242 1827 ql=3 -0.745 1.082 -0.946 1.620 900 ql=2 -1.445 1.749 -1.636 1.945 308 ql=1 -3.310 4.018 -4.110 4.342 1552

Table 4: Median, robust standard deviation, mean and standard deviation of the di↵erence satellite minus in-situ observation for L2P AATSR LSWT validation for quality level intervals.

Median RSD Mean SD N ql>3 -0.340 0.504 -0.469 1.073 4576 ql>2 -0.370 0.578 -0.548 1.193 5476 ql>1 -0.390 0.608 -0.606 1.268 5784 ql>0 -0.510 0.860 -1.347 2.703 7336

4.1.2 Validation of L2P SLSTR-A LSWT The validation of the L2P LSWT from the SLSTR-A instrument has been carried out with 9255 matches where all the quality levels (deﬁned in the ATBD) of the L2P LSWT have been included. The matches per quality level are reported in Table 5 showing that the majority of data have quality level 4 or 5. Quality level 1 indicates bad data and they have not been used for the 10-day LSWT product.

Table 5: Number of matches per L2P quality level (SLSTR-A).

QL=5 QL=4 QL=3 QL=2 QL=1 N 2376 1978 1892 1015 1994

The left hand side of Figure 7 shows an example of time series plot for the year 2017 and the right hand side for the year 2018 for the site on Lake Simcoe in Canada shown in Figure 4. The ﬁgures show all the in-situ daily mean temperatures, the in-situ matches, the L2P matches, the di↵erence satellite minus in-situ temperature and the climatology with one standard deviation as a reference. For the year 2009, the greatest di↵erence is for a L2P quality level = 2 (the lowest quality that might be usable), while for the year 2010 the greatest di↵erence is for a L2P quality level = 1, which is a ﬂag for suspected bad data and therefore excluded from the temporal aggregation to generate the C-GLOPS product.

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Figure 8 shows the same plot for the site of Lake Tahoe in USA shown. Also in this case, better quality levels o↵er a smaller di↵erence with the in-situ measurements. For some of the lakes, such as lake Tahoe, the measurements are sparser, often taken daily and not necessarily every day. The quality levels indicate the conﬁdence in the retrieval and good quality observation may have higher uncertainties than lower quality level observations as it can be noticed for the ﬁgures described in the current section.

Figure 7: L2P LSWT validation for Lake Simcoe in Canada for the year 2017 (left) and 2018 (right) where all the in-situ daily mean temperatures (black line), in-situ matches (white dots), satellite matches (coloured dots according to the quality levels), satellite (SLSTR-A) minus in-situ matches (green line) and the climatology with on standard deviation are reported.

Figure 8: Location of the site of in-situ measurements for Lake Tahoe in USA and L2P validation for the year 2018 where all the in-situ daily mean temperatures (black line), in-situ matches (white dots), satellite matches (coloured dots according to the quality levels), satellite (SLSTR-A) minus in-situ matches (green line) and the climatology with on standard deviation are reported.

The statistics of the validation are reported in Table 6 for each quality level and in Table 7 for all quality levels greater than as indicated in the ﬁrst column. Generally, the median and the RSD are less inﬂuenced by large di↵erences arising occasionally from cloud detection or large diurnal

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Table 6: Median, robust standard deviation, mean and standard deviation of the di↵erence satellite minus in-situ observations for L2P SLSTR-A LSWT validation for each quality level.

Median RSD Mean SD N ql=5 -0.280 0.385 -0.268 0.780 2376 ql=4 -0.460 0.608 -0.503 1.163 1978 ql=3 -0.600 0.964 -0.781 1.367 1892 ql=2 -0.890 1.379 -1.204 1.887 1015 ql=1 -4.475 4.952 -4.949 6.316 1994

Table 7: Median, robust standard deviation, mean and standard deviation of the di↵erence satellite minus in-situ observation for L2P SLSTR-A LSWT validation for quality level intervals.

Median RSD Mean SD N ql>3 -0.330 0.489 -0.375 0.980 4354 ql>2 -0.380 0.593 -0.498 1.127 6246 ql>1 -0.410 0.667 -0.596 1.285 7261 ql>0 -0.550 0.978 -1.534 3.618 9255

The best results are for the most acceptable quality levels, namely ql=4,5.

4.1.3 L2P LSWT validation discussion For SLSTR-A more matches than for AATSR can be found despite the shorter timespan of the LSWT (about 10 years for AATSR and 2.5 years for SLSTR-A). This is surely due to a larger in situ dataset but especially it is due to the larger swath of SLSTR-A (about 1400km for SLSTR-A and 500km for AATSR). The results for the LSWTs from both the instruments show a similar pattern at di↵erent quality levels, although both the mean and the median for the LSWT from SLSTR-A are closer to 0 for all the quality levels (except quality level 4) and the SD and RSD are smaller for LSWT from SLSTR-A. One of the reasons could be that more than double of the matches for the AATSR LSWT are from daily in-situ data (AATSR LSWT 13%, SLSTR-A LSWT 5% of the matches). Unquestionably, a contribution to the di↵erence on average is the expected skin e↵ect of about -0.2 K (the satellite LSWT is expected to be cooler than the subsurface). Satellite LSWT biases surely contribute to the remaining residual, as do in-situ errors and unquantiﬁed geophysical e↵ects (near-surface stratiﬁcation other than the skin e↵ect, plus horizontal variability). During the day,

Document-No. CGLOPS2 QAR LSWT 1km v1.0.1 I1.09 c C-CGLOPS2 consortium Issue: I1.09 Page 25 of 67 Copernicus Global Land Operations Lot 2 Date August 16, 2020 there is a combination of the cool skin e↵ect and the average stratification between measurement depth (of order 1 m, typically) and the radiometric lake surface represented in the mean di↵erence. At present, we have not assessed the degree of near-surface stratification to be expected in di↵erent lakes, which depends on fetch, weather conditions, in-situ depth of measurement, and any local vertical mixing perturbations introduced by the presence of the in-situ measurement system. In summary, a contribution to the di↵erence of -0.340 K and -0.330 K (for LSWT for quality levels greater than 3) for AATSR and SLSTR-A respectively on average is the expected skin e↵ect of 0.2 K, but it is dicult to infer a precise contribution of satellite LSWT biases to the remaining residual, in the face of in-situ errors and unquantified geophysical e↵ects. The match-up has been performed spatially within 3km and temporally, where hourly measurements were available, within 3 hours. Otherwise, merely daily measurements have been used. Therefore, another contribution to the di↵erence could also be attributed to the fact that part of the in-situ measurements are available as a daily value, which we have included in the match-up database to create more robust statistics. On the other hand, we have limited the spatial match-up to 3km. The spatial position of the in-situ measurements is often not precisely known and this can also contribute to the di↵erence. Morever, the in-situ measurements also present imperfections and the uncertainty on the in-situ measurements is not known.

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4.2 Validation of the C-GLOPS 10-day LSWT 4.2.1 Validation of C-GLOPS AATSR LSWT The validation for the 10-day AATSR LSWT for each lake site includes a consistency check of the time series for both the in-situ measurements and the C-GLOPS product. The time series and other results for some of the lake sites are reported in this documents and the full set can be accessed through the link http://www.laketemp.net/home_CGLOPS/aatsr_validation/. The time series of the 10-day C-GLOPS LSWT product are shown per quality level. The in-situ measurements have been averaged within the 10 days. Figures 9, 10 and 11 show on the left hand side the location of the in-situ observation site on the lake and on the right hand side the time series. Figure 9 and Figure 10 show the location of the sites and the LSWT time series for two sites on the lake Superior and on lake Huron in Canada/USA respectively.

Figure 9: Time series (left hand side) of the 10-day in-situ measurements and the C-GLOPS AATSR product for the two sites shown on the right hand side on the lake Superior in Canada/USA. The left hand side plots show 10-day mean in-situ observations (black line), the C-GLOPS LSWT matches (coloured dots according to quality levels) and the di↵erence satellite minus in-situ observations (red line).

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Figure 10: Time series (left hand side) of the 10-day in-situ measurements and the 10-day C- GLOPS AATSR product for the two sites shown on the right hand side on the lake Huron in Canada/USA. The left hand side plots show 10-day mean in-situ observations (black line), the C- GLOPS LSWT matches (coloured dots according to quality levels) and the di↵erence satellite minus in-situ observations (red line).

For both lake Superior and lake Huron we can observe a consistent seasonal trend among the sites for the both the in-situ and the C-GLOPS product. The di↵erence satellite minus in situ measurements is shown for quality levels 3, 4 and 5. A similar behaviour can be detected also for the other in-situ observation locations as shown at http://www.laketemp.net/home_CGLOPS/ aatsr_validation/. The time series for a smaller lake at a lower latitude is shown in Figure 11 where the seasonal trend can be observed. The C-GLOPS LSWTs seem in good agreements with the in-situ measurements especially for quality levels 3,4,5. the di↵erence satellite minus in-situ observation is consistently less than 2K for most of the matches. The validation has been carried out per-lake and for all the matches. The results presented in the following are grouped per-lake namely all the sites for each lake have been used. The correspondent plots are located at http://www.laketemp.net/home_CGLOPS/aatsr_validation/ together with

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Figure 11: LSWT time series (right hand side) of the 10-day in-situ measurements and the 10-day C-GLOPS AATSR product for site on the Sea of Galilee in Israel shown on the left hand side of the ﬁgure. The left hand side plots show 10-day mean in-situ observations (black line), the C-GLOPS LSWT matches (coloured dots according to quality levels) and the di↵erence satellite minus in-situ observations (red line). the per-site plots. Median, robust standard deviation, mean and standard deviation of the di↵erence satellite minus in-situ observation from the comparisons with in-situ observations are presented in Table 8 together with N, the number of matches. For example, over lake Baikal the in-situ measurements have been taken in one site only and they were daily. For these reasons, the number of matches is relatively low. These facts are summarised in a high RSD. The high variability over lake Superior very likely originates from the fact that the in-situ data comes from eight di↵erent sites over a very large lake.

Figure 12 shows the plot of the di↵erence satellite minus in-situ against the in-situ measurements for the Sea of Galilee per quality levels. The red line is the ﬁtted line of slope m =0.023 and intercept c = 0.718 showing some trend in di↵erence against in-situ temperature.

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Table 8: Median, robust standard deviation, mean and standard deviation of the di↵erence satellite minus in-situ 10-day average AATSR LSWT for each lake.

Lake N Median RSD Mean SD Superior 531 -0.150 1.107 -0.128 0.623 Huron 701 -0.340 0.786 -0.321 1.120 Michigan 267 -0.170 0.623 -0.089 1.323 Baikal 51 0.200 1.438 0.319 1.758 Great Slave 90 -0.290 0.689 -0.425 1.00 Erie 390 -0.270 0.749 -0.274 1.210 Winnipeg 225 -0.280 0.801 -0.226 1.313 Ontario 433 -0.340 1.305 -0.272 1.905 Issykkul 10 -0.720 0.704 -0.787 0.920 Vänern 225 -0.250 0.949 -0.139 1.623 Woods 62 -0.050 0.927 0.067 1.238 Tucurui 35 0.080 0.326 0.056 0.679 Kivu 51 -0.930 0.682 -1.241 1.072 Vättern 310 -1.365 1.668 -1.252 2.010 Saint Clair 138 -0.280 1.060 -0.220 1.282 Mälaren 156 0.075 1.342 0.060 1.465 Nipissing 110 -0.215 0.860 -0.241 1.049 Simcoe 92 -0.165 0.786 0.059 1.537 Itumbiara 17 -0.460 0.341 -0.419 0.489 Taupo 120 -0.420 0.526 -0.439 0.858 Balaton 378 0.155 1.786 0.186 2.269 Geneva 116 -0.090 1.305 -0.045 1.574 Tahoe 210 -0.195 0.830 0.049 1.231 Siljan 3 -1.230 1.394 -0.997 1.066 Võrstjärv 45 0.349 1.216 0.421 1.671 Bolmen 9 0.300 1.512 0.175 1.348 Neusiedl 63 0.500 1.898 0.466 2.051 Sea of Galilee 93 -0.180 0.623 -0.164 0.791 Atilián 16 -0.195 0.704 0.009 0.622 Trasimeno 13 -1.640 0.682 -1.509 0.609 Ekoln 26 0.705 1.408 70 1.510 Roxen 32 0.090 1.127 0.061 1.912

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Figure 12: Time series (left hand side) of the 10-day in-situ measurements and the 10-day AATSR C-GLOPS product for the site shown on the right hand side on the Sea of Galilee in Israel. The left hand side plots show 10-day mean in-situ observations (black line), the C-GLOPS LSWT matches (coloured dots according to quality levels) and the di↵erence satellite minus in-situ observations (red line).

The lakes have di↵erent statistics where the di↵erence depends on the number of sites, the size of the lake, the number of matches, the quality of the in-situ measurements and the latitude, since at lower latitudes it may be less cloudy so that the water detection may work better. The global statistics for all the matches is shown in Table 9.

Table 9: Median, robust standard deviation, mean and standard deviation of the di↵erence satellite minus in-situ 10-day average AATSR LSWT for all the lakes.

QL N Median RSD Mean SD 5 2956 -0.240 0.786 -0.193 1.244 4 1421 -0.230 1.156 -0.183 1.668 3 508 -0.350 1.586 -0.300 2.107 2 133 -1.110 1.942 -0.753 2.495 >3 4377 -0.240 0.875 -0.190 1.396

The in-situ measurements present imperfections and also the 10-day aggregation may impair the validation since the aggregation is performed on a di↵erent number of measurements for satellite and in-situ.

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Figure 13: Plot of the di↵erence satellite minus in-situ against the in-situ measurements and of the AATSR C-GLOPS product against the in-situ measurements for all the matches and all the quality levels.

The retrieval shows some trend in di↵erence against in-situ temperature shown in Figure 13, 1 quantiﬁed by the slope, m, shown on the plots. The LSWT matches have m =-0.002 K K , meaning that over the 25 K range of lake temperatures in the data, the satellite is warmer relative to in-situ observations by 0.05 K for the lowest temperatures compared with the warmest temperatures.

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4.2.2 Validation of C-GLOPS SLSTR-A LSWT The validation for the 10-day LSWT for each lake site includes a consistency check of the time series for both the in-situ measurements and the C-GLOPS product. The time series and other results for some of the lake sites are reported in this documents and the full set can be accessed through the link http://www.laketemp.net/home_CGLOPS/slstr_validation/. The time series of the 10-day C-GLOPS LSWT product are shown per quality level. The in-situ measurements have been averaged within the 10 days. Figures 14, 15 and 16 show on the left hand side the location of the in-situ observation site on the lake and on the right hand side the time series. Figure 14 and Figure 15 show the location of the sites and the LSWT time series for two sites on the lake Superior and on lake Huron in Canada/USA respectively.

Figure 14: Time series (left hand side) of the 10-day in-situ measurements and the SLSTR-A C- GLOPS product for the two sites shown on the right hand side on the lake Superior in Canada/USA. The left hand side plots show 10-day mean in-situ observations (black line), the C-GLOPS LSWT matches (coloured dots according to quality levels) and the di↵erence satellite minus in-situ observations (red line).

For both lake Superior and lake Huron we can observe a consistent seasonal trend among the sites for the both the in-situ and the C-GLOPS product. The di↵erence satellite minus in situ

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Figure 15: Time series (left hand side) of the 10-day in-situ measurements and the 10-day SLSTR-A C-GLOPS product for the two sites shown on the right hand side on the lake Huron in Canada/USA. The left hand side plots show 10-day mean in-situ observations (black line), the C-GLOPS LSWT matches (coloured dots according to quality levels) and the di↵erence satellite minus in-situ observations (red line). measurements is shown for quality levels 3, 4 and 5. A similar behaviour can be detected also for the other in-situ observation locations as shown at http://www.laketemp.net/home_CGLOPS/ slstr_validation/. The time series for a smaller lake at a lower latitude is shown in Figure 16 where the seasonal trend can be observed. The C-GLOPS SLSTR-A LSWTs seem in good agreements with the in-situ measurements especially for quality levels 3,4,5. the di↵erence satellite minus in-situ observation is consistently less than 2K for most of the matches. The validation has been carried out per-lake and for all the matches. The results presented in the following are grouped per-lake namely all the sites for each lake have been used. The correspondent plots are located at http://www.laketemp.net/home_CGLOPS/slstr_validation/ like the per-site validation. Median, robust standard deviation, mean and standard deviation of the di↵erence satellite minus

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Figure 16: LSWT time series (right hand side) of the 10-day in-situ measurements and the 10-day SLSTR-AC-GLOPS product for site on the Sea of Galilee in Israel shown on the left hand side of the ﬁgure. The left hand side plots show 10-day mean in-situ observations (black line), the C-GLOPS LSWT matches (coloured dots according to quality levels) and the di↵erence satellite minus in-situ observations (red line). in-situ observation from the comparisons with in-situ observations are presented in Table 10 together with N, the number of matches and the number of sites on the lake used. For example, over lake Baikal the in-situ measurements have been taken in one site only and they were daily. For these reasons, the number of matches is relatively low. These facts are summarised in a high RSD as in the case of AATSR. The high variability over lake Superior very likely originates from the fact that the in-situ data comes from thirteen di↵erent sites over a very large lake.

Figure 17 shows the plot of the di↵erence satellite minus in-situ against the in-situ measurements for the Lake Woods per quality levels. The red line is the ﬁtted line of slope m =0.021 and intercept c = 0.473 showing some trend in di↵erence against in-situ temperature.

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Table 10: Median, robust standard deviation, mean and standard deviation of the di↵erence satellite minus in-situ 10-day average SLSTR-A LSWT for each lake.

Lake N Nsites Median RSD Mean SD Superior 457 13 -0.200 0.815 -0.219 1.302 Huron 362 8 -0.440 0.652 -0.349 1.114 Michigan 586 14 -0.460 0.845 -0.475 1.350 Baikal 15 1 -0.370 1.438 0.356 1.438 Great Slave 27 2 -0.280 0.534 -0.503 1.055 Erie 403 10 -0.520 0.756 -0.528 1.148 Winnipeg 87 3 -0.400 0.756 -0.293 1.042 Ontario 216 4 -0.340 1.275 -0.268 1.960 Vänern 60 6 -0.445 0.800 -0.104 1.309 Woods 30 1 -0.225 0.652 -0.087 0.937 Vättern 14 2 -0.035 0.245 -0.162 0.317 Saimaa 7 2 -0.720 1.349 -0.648 1.206 Saint Clair 44 1 -0.355 0.801 -0.322 1.060 Mälaren 78 9 -0.120 1.082 -0.223 1.713 Champlain 59 2 -0.450 0.534 -0.473 1.046 Orivesi and Pyhäselkä 8 2 -0.265 1.127 1.292 3.583 Pielinen 5 4 -0.640 2.446 -0.144 2.495 Nipissing 78 3 -0.395 0.964 -0.254 1.307 Oulujärvi 7 1 -1.160 2.313 -0.321 1.971 Haukivesi 4 1 -1.025 1.008 -0.832 1.140 Simcoe 43 1 -0.730 0.578 -0.563 0.881 Mead 165 3 -0.380 0.652 -0.161 1.043 Taupo 10 1 -0.400 0.245 -0.872 0.905 Nasijärvi 8 1 -0.085 0.697 -0.425 1.030 Lokka 6 1 0.390 0.674 0.402 0.663 Tahoe 54 1 -0.245 0.986 -0.020 1.340 Hjalmaren 3 1 -0.350 1.008 -0.317 0.596 Onkivesi 11 2 -0.620 3.070 0.305 2.640 Garda 45 2 -0.050 1.275 0.138 1.519 St. John River 61 3 -0.010 1.260 0.070 1.421 Puulavesi 3 1 0.070 0.148 0.023 0.143 Siljan 2 1 -0.455 0.230 -0.455 0.155 Maggiore 16 1 -0.190 0.815 -0.042 0.901 Bolmen 3 2 -0.020 1.364 0.507 1.445 Neusiedl 24 1 0.685 1.275 0.502 1.847

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Lake N Nsites Median RSD Mean SD Sea of Galilee 18 1 -0.215 0.689 -0.342 0.965 Vana javesi 13 1 -0.220 1.230 -0.860 1.534 Pyhäajäarvi 14 1 -0.875 0.460 -0.796 0.759 Lappajäarvi 15 1 0.480 1.824 -0.222 1.815 Trasimeno 9 2 -0.890 1.023 -0.625 0.950 Ekoln 10 1 -0.360 1.305 -0.368 1.199 Roxen 7 1 -0.320 0.593 0.344 0.333

Figure 17: Time series (left hand side) of the 10-day in-situ measurements and the 10-day SLSTR-A C-GLOPS product for the site shown on the right hand side on the Lake Woods in Canada. The left hand side plots show 10-day mean in-situ observations (black line), the C-GLOPS LSWT matches (coloured dots according to quality levels) and the di↵erence satellite minus in-situ observations (red line).

Table 11: Median, robust standard deviation, mean and standard deviation of the di↵erence satellite minus in-situ 10-day average SLSTR-A LSWT for all the lakes.

QL N Median RSD Mean SD 5 1677 -0.330 0.712 -0.288 1.060 4 922 -0.430 0.941 -0.291 1.491 3 390 -0.430 1.134 -0.370 1.701 2 124 -0.700 1.497 -0.807 2.239 >3 2599 -0.370 0.771 -0.289 1.230

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Figure 18: Plot of the di↵erence satellite minus in-situ against the in-situ measurements and of the SLSTR-A C-GLOPS product against the in-situ measurements for all the matches and all the quality levels.

The retrieval shows some trend in di↵erence against in-situ temperature shown in Figure 18, 1 quantiﬁed by the slope, m, shown on the plots. The LSWT matches have m =-0.009 K K , meaning that over the 25 K range of lake temperatures in the data, the satellite is warmer relative to in-situ observations by about 0.2 K for the lowest temperatures compared with the warmest temperatures.

4.2.3 10-day LSWT validation discussion The number of matches for the 10-day C-GLOPS LSWT product di↵ers for AATSR and SLSTR-A because of the shorter coverage period of SLSTR-A. Tables 9 and 11 show slightly higher median and mean for LSWT from SLSTR-A than from AATSR but lower variability.

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4.3 Validation of the C-GLOPS LSWT uncertainty 4.3.1 Validation of the C-GLOPS AATSR LSWT uncertainty A validation of the C-GLOPS LSWT uncertainties (see ATBD for deﬁnition) has been carried out comparing the di↵erence C-GLOPS 10-day LSWT product minus the in-situ observations and the LSWT uncertainty: LSW T T = insitu . (1) 2 2 LSWT + insitu

Assuming insitu =0.2, should have ap Gaussian distribution with a mean µ = 0 and a standard deviation = 1 for acceptable uncertainties. The value for insitu has been assigned as a typical instrument calibration uncertainty. Since currently we do not have any uncertainty value for the in-situ measurements. The distribution of for lake Erie together with the Q-Q plot are reported in Figure 19. In the plot of the distribution of the Gaussian curve (with µ = 0 and = 1) is shown. A Gaussian fitting has been also performed giving a µ = 0.4332 and =1.7147. The Gaussian fit width of 1.71 shows that observed di↵erences are more di↵erent than expected from the quoted uncertainties. The Q-Q plot is the quantile-quantile plot which allows to compare the probability distribution without the need of binning using the quantiles. If the two distributions are similar the Q-Q plot points will lie on the y = x line which is reported on the plot. The plot shows the outliers and it is slightly flatter for positive values showing that is slightly less dispersed that the theoretical Gaussian. The blue line represents the y = x line while the red is a fitting.

Figure 19: Distribution of and Q-Q plot for the LSWT uncertainty of lake Erie in Canada for AATSR. In the Q-Q plot the blue line is the y = x line while the red is a ﬁtting.

For the rest of the lakes the histogram and the Q-Q plot can be found at http://www.laketemp. net/home_CGLOPS/aatsr_validation/. However, a robust uncertainty validation is not currently possible due to the limited number of accurate in-situ measurements.

4.3.2 Validation of the C-GLOPS SLSTR-A LSWT uncertainty A validation of the C-GLOPS SLSTR-A LSWT uncertainties (see ATBD for deﬁnition) has been carried out comparing the di↵erence C-GLOPS 10-day LSWT product minus the in-situ observations and the LSWT uncertainty deﬁned in (1).

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Assuming insitu =0.2, should have a Gaussian distribution with a mean µ = 0 and a standard deviation = 1 for acceptable uncertainties. The value for insitu has been assigned as a typical instrument calibration uncertainty. Since currently we do not have any uncertainty value for the in-situ measurements. The distribution of for lake Erie together with the Q-Q plot are reported in Figure 20. In the plot of the distribution of the Gaussian curve (with µ = 0 and = 1) is shown. A Gaussian fitting has been also performed giving a µ = 0.815 and =1.609. The Gaussian fit width of 1.609 shows that observed di↵erences are more di↵erent than expected from the quoted uncertainties. The Q-Q plot is the quantile-quantile plot which allows to compare the probability distribution without the need of binning using the quantiles. If the two distributions are similar the Q-Q plot points will lie on the y = x line which is reported on the plot. The plot shows the outliers and it is slightly flatter for positive values showing that is slightly less dispersed that the theoretical Gaussian. The blue line represents the y = x line while the red is a fitting.

Figure 20: Distribution of and Q-Q plot for the LSWT uncertainty of lake Erie in Canada for SLSTR-A. In the Q-Q plot the blue line is the y = x line while the red is a ﬁtting.

For the rest of the lakes the histogram and the Q-Q plot can be found at http://www.laketemp. net/home_CGLOPS/slstr_validation/. However, a robust uncertainty validation is not currently possible due to the limited number of accurate in-situ measurements.

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4.4 Validation of C-GLOPS SLSTR-A NRT LSWT The validation of the C-GLOPS SLSTR-A NRT LSWT has been performed through comparison with the climatology only for the lake centre defined in [Carrea et al., 2015] and reported in the Appendix of the ATBD for each lake. In case of measurements exceeding two standard deviation of the climatology, a flag has been set. The plots are reported for all the C-GLOPS lakes at http://www.laketemp.net/home_CGLOPS/dataNRT/. We have examined especially narrow lakes since in this case the retrieval using 1 km (at best) pixels is challenging because of the risk of undetected land contamination from geolocation uncertainty. The water detection algorithm, described in the ATBD is applied to every pixel which is labelled as water on the mask in order to minimise the risks of geolocation uncertainty and of the lake extent uncertainty on the static mask. Because water detection is applied, retrieved pixels are ensured to be water filled. In case of narrow/small lakes, the water detection will often imply there is some land in a pixel, and the retrieval will not be made as often or will have a low quality level (as captured by the quality level flag). Figure 21 shows the SLSTR-A LSWT for Represa de Furnas in Brazil. The maximum distance to land for this lake is 2 km, namely a very narrow lake where the retrieval using 1 km (at best) pixels is challenging because of the risk of undetected land contamination from geolocation uncertainty. In this case the measurements are well within 2 standard deviation of the climatology. The plot reported is for the lake centre which has been defined as the point most distant from land [Carrea et al., 2015]. Figure 22 shows the SLSTR-A LSWT for lake Bin-El-Ouidane in Morocco. The maximum distance to land for this lake is 1.9 km which is again a narrow lake. Also, in this case the measurements are well within 2 standard deviation of the climatology.

Figure 21: C-GLOPS 10-day LSWT with SLSTR-A data for Represa de Furnas in Brazil for the lake centre highlighted on the left hand side of the ﬁgure.

Figure 22 shows the SLSTR-A LSWT for lake Bin-El-Ouidane in Morocco. The maximum distance to land for this lake is 1.9km. The lake is again very narrow. Also, in this case the measurements are well within 2 standard deviation of the climatology.

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Figure 22: C-GLOPS 10-day LSWT with SLSTR-A data for lake Bin-El-Ouidane in Morocco for the lake centre highlighted on the left hand side of the ﬁgure.

Figure 23 shows the SLSTR-A LSWT for lake Enriquillo in Dominica Republic. The maximum distance to land for this lake is 5.1 km, a larger lake. In this case one of the measurements is exceeding 2 standard deviations of the climatology. This point has a quality level 3 and overall the neighbours seems to all consistently have lower temperature than the climatology. Moreover, variations of 4 or more degrees can be considered reasonable within periods of one month.

Figure 23: C-GLOPS 10-day LSWT with SLSTR-A data for lake Enriquillo in Dominica Republic for the lake centre highlighted on the left hand side of the ﬁgure.

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4.5 Assessment of the SLSTR-A LSWT reprocessed product The assessment of the SLSTR-A LSWT reprocessed product is carried out firstly assessing the number of L1b granules where the mask was indicating the presence of lakes. The SLSTR L1b granules are of two types depending on delivery time to users and the available consolidated auxiliary or ancillary data: NRT: the granule is delivered in less than 3 hours after data acquisition • NTC: the granule is delivered within one month after data acquisition. This additional delay • allows consolidation of some auxiliary or ancillary data (e.g. precise orbit data) At the time of the NRT C-GLOPS processing not all the granules are available and moreover not all the granules are in consolidated form (NTC). Figure 24 shows in the upper plot the number of L1b granules that have been processed for the NRT C-GLOPS separated as NRT and NTC. Not only many of the granules were not consolidated at the time of the NRT processing but also many files have not been processed at all. This is highlighted in the lower plot of the figure which shows that at the time of the reprocessing few of the files were still NRT. The reprocessed product contains though mainly NTC consolidated L1b granules.

Figure 24: Number of SLSTR-A granules processed for the NRT product (operational) and for the reprocessed product together with their di↵erence namely the number of granules which were not processed for the NRT product.

However, although all the granules containing lakes (according to the L1b in-ﬁle SLSTR mask) have been processed only a portion of them contains validly processed temperatures to be used to generated the 10-day product. Figure 25 includes only granules that contains validly processed LSWT and shows the number of granules which were NRT and were reprocessed as NTC • were not processed for the NRT product but only in the reprocessing as NTC • NRT granules which could not be reprocessed or did not contain equally valid temperatures • in the reprocessing

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In particular, a big portion of the existent granules on the 13-Jan-2019 and a small portion of them on the 18-Apr-2018, 08-Jan-2019 and on the 28-Apr-2020 could not be reprocessed due to technical problems with the accessibility of the ﬁles. The ﬁles which could not be reprocessed are reported in green in Figure 25. In the middle of 2018 and 2019 many granules have newly contributed to the LSWT dekad and especially in 2018 many granules have contributed to the dekad as consolidated granules rather than NRT.

Figure 25: Number of SLSTR-A granules which have been either newly included or reprocessed as NTC. The granules shown in the ﬁgure have valid LSWTs.

Therefore, there are cases where the number of validly processed temperatures in the reprocessing product are less than in the NRT product. Figure 26 shows the number of cells with valid LSWT R for which the di↵erence between Nobs, the number of observations in the reprocessed product and N Nobs, the number of observations in the NRT product is positive or negative. High number of cells across all the lakes with positive di↵erences throughout the dekads especially for dekads where new granules have been processed can be found. But also we can ﬁnd cells with negative di↵erences especially for dekads where the number of granules which were NRT and have been reprocessed as NTC is higher then the number of new reprocessed granules.

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Figure 26: Number of cells for which the reprocessed minus NRT di↵erence in the dekadal number of observations was positive or negative.

We present the case of lake Tucurui in Brazil for the 11-Jul-2018 dekad for which this happens. The reprocessed minus NRT number of observations di↵erence for the lake in Figure 27 shows that the number of observations is lower after the reprocessing for most of the lake. However, the quality levels in the NRT product were 2 for most of the lake and became higher in the reprocessed product as shown in Figure 28. Also Figure 29 shows that observations from two days (the 2nd and the 6th) were used for the NRT dekad while for the reprocessed dekad only the day 9th was used. This is because the dekad is created with aggregated LSWT of quality level 3,4,5 and quality level 2 exclusively. If no observations of quality level 3, 4 or 5 is available then the dekad is generated with quality level 2 only data. Dekadal LSWT with quality level 2 implies that no observation of quality level > 2 was available for any of the day. On the contrary, dekadal LSWT with quality level 3,4,5 implies that no observation of quality level = 2 has been used (but it may have been available). Figure 30 and 31 shows the LSWT and its uncertainty for the NRT and the reprocessed product. The LSWT is slightly cooler after the reprocessing but the uncertainty is about the double due to both di↵erent satellite measurements and lower number of observations. However, the uncertainty in the reprocessed product is more realistic than in the NRT because the quality levels have an higher value. Quality levels are a measure of the conﬁdence level of the uncertainties.

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Figure 27: reprocessed minus NRT number of observations di↵erence for the lake Tucurui in Brazil for the dekad starting on the 11-Jul-2018.

Figure 28: SLSTR-A 10-day LSWT quality levels for lake Tucurui in Brazil (11-Jul-2018 dekad). On the left hand side the NRT and on the right hand side the reprocessed product are shown.

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Figure 29: SLSTR-A 10-day LSWT days of observation used to generate the dekad for lake Tucurui in Brazil (11-Jul-2018 dekad). On the left hand side the NRT and on the right hand side the reprocessed product are shown.

Figure 30: SLSTR-A 10-day LSWT for lake Tucurui in Brazil (11-Jul-2018). On the left hand side the NRT and on the right hand side the reprocessed product are shown.

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Figure 31: SLSTR-A 10-day LSWT uncertainty for lake Tucurui in Brazil (11-Jul-2018 dekad). On the left hand side the NRT and on the right hand side the reprocessed product are shown.

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In general, reprocessing the NRT product has improved the coverage and also the quality. We present lake Balqash in Kazakhstan during the 10-day period stating on the 11-Jun-2018 where we found a higher coverage, improved quality levels and uncertainty as shown in Figure 32, 33, 34. The 10-day LSWTs are shown in Figure 35 where we can clearly see an improvement of the lake coverage. The improvements are partly due to the usage of NTC instead of NRT L1b granules (which is reﬂected more likely in higher quality levels) and also due to the fact that a later reprocessing allow to process all the NTC L1b granules (which is reﬂected more likely in lower uncertainties and better coverage).

Figure 32: Reprocessed minus NRT number of observations di↵erence for the lake Balqash in Kaza- khstan for the dekad starting on the 11-Jul-2018.

Figure 33: SLSTR-A 10-day LSWT quality levels for lake Balqash in Kazakhstan (11-Jun-2018 dekad). On the left hand side the NRT and on the right hand side the reprocessed product are shown.

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Figure 34: SLSTR-A 10-day LSWT uncertainty for lake Balqash in Kazakhstan (11-Jun-2018 dekad). On the left hand side the NRT and on the right hand side the reprocessed product are shown.

Figure 35: SLSTR-A 10-day LSWT for lake Balqash in Kazakhstan (11-Jun-2018 dekad). On the left hand side the NRT and on the right hand side the reprocessed product are shown.

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4.6 Assessment of the SLSTR-AB LSWT product The new product generated with observations from both SLSTR-A and SLSTR-B instruments is assessed for two dekads inspecting the number of observations contributing to the dekad and the improvements in quality levels and in the uncertainty of the LSWT. The two dekads are from 01- Apr-2020 to 10-Apr-2020 and from 21-Jul-2020 to 31-Jul-2020. The assessment reported here is for the ﬁrst dekad but a similar behaviour can be found for the second dekad. The number of observations are generally higher for the SLSTR-AB LSWT product but there are cases where the number of observations is lower. Table 12 shows the number of cells with positive or negative D = N AB N A (2) obs obs and Figure 36 shows the histogram of the di↵erence D.

Table 12: Number of cells with positive or negative number of observations di↵erence (dekad 01- Apr-2020).

D>0 D<0 D=0 823370 (83.5%) 4893 (0.5%) 156754 (16%)

Figure 36: SLSTR-AB minus SLSTR-A number of observations di↵erence for the dekad starting on 01-Apr-2020.

The inspection of some of the cases shows that the number of observations decreases for cells where qlA = 2 and increased for introducing SLSTR-B. An example is shown in Figure 37 for lake Upemba in Zaire (ID314) for the dekad starting on 21-Jul-2020, where the SLSTR-AB product is generated with 1 or 2 observations less than in the SLSTR-A product. However, the quality levels of the SLSTR-A were 2, which indicates that the product has been generated with only LSWT of qlA = 2. Since SLSTR-B has introduced LSWTs of higher quality, the SLSTR-A quality level 2 temperatures have been ignored and overall the product has been generated only LSWTs from SLSTR-B of qlB > 2 as shown in Figure 38.

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Figure 37: Number of observations for the SLSTR-A 10-day LSWT for lake Upemba in Zaire (01- Apr-2020 dekad) on the left hand side and the for the SLSTR-AB 10-day LSWT on the right hand side.

Figure 38: Quality levels for the SLSTR-A 10-day LSWT for lake Upemba in Zaire (01-Apr-2020 dekad) on the left hand side and the for the SLSTR-AB 10-day LSWT on the right hand side.

As expected, for the majority of the cells across the lakes the quality levels remain the same since the two sensors have quite similar characteristics as shown in the SLSTR-A SLSTR-B comparison in the ATBD v9. However, a variation in the quality levels is encountered which is an improvement for a big portion of the cells. This can be seen in Figure 39 and on Tables 13 and 14 showing a scatter plot of the quality levels where the size of the symbols is proportional to the number of cells of such quality levels. For a small portion of cells where the quality levels reduced of 1 (from qlA =5to qlA = 4 and from qlA =4toqlA = 3). These variations can certainly be due to a slight di↵erence in the instruments (see the SLSTR-A SLSTR-B comparison in the ATBD v9) but also to the di↵erence in the scene since the two satellite are 140 apart in their operational orbits.

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Table 13: Number of cells across all the lakes for the same quality levels for the SLSTR-A product and for the combined SLSTR-AB one for the dekad starting on the 01-Apr-2020.

AABAABAABAAB

QL 55443322 N 625276 85869 150131 26665

Table 14: Number of cells across all the lakes for di↵erent quality levels for the SLSTR-A product and for the combined SLSTR-AB one for the dekad starting on the 01-Apr-2020.

AABAABAABAABAABAAB

QL 544332534252 N 26005 4565 0 0 0 0

QL 453423352425 N 22774 9484 19347 6899 5793 2209

Figure 39 shows a scatter plot of the quality levels where the size of the symbols is proportional to the number of cells of such quality levels.

Figure 39: SLSTR-AB vs SLSTR-A quality levels of the LSWTs for the dekad starting on the 01-Apr-2020.

The LSWT uncertainty has been assessed simply through the di↵erence

= . (3) AB A Figure 40 shows that the di↵erence in the uncertainty indicates both a lower but also an higher LSWT uncertainty when temperatures from both the sensors are used for the temporal 10-day aggregation.

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Figure 40: SLSTR-AB minus SLSTR-A LSWT uncertainty di↵erence histogram for the dekad starting on the 01-Apr-2020.

Since the LSWTs are aggregated exclusively for ql=2 and ql>2, we plot the histograms of grouping quality levels. Figure 41 shows the histograms of for quality levels that are the same for the SLSTR-A-only LSWT and for the combined SLSTR-AB LSWT. The histograms show that the uncertainty has decreased for all the cells across the lakes when comparing LSWT uncertainties of the same quality level for the SLSTR-A LSWT and for the SLSTR-AB LSWT.

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Figure 41: SLSTR-AB minus SLSTR-A LSWT uncertainty di↵erence histogram for the dekad starting on the 01-Apr-2020 when the LSWT quality levels are the same for both the products.

Comparing histograms of LSWT uncertainty of di↵erent quality levels shows that the uncertainty is consistently lower for the combined product than for the SLSTR-A-only product except when qlA = 2 as shown in Figures 42, 43, 44. For some quality level combinations (degrading from SLSTR-A to SLSTR-AB) no LSWT was found as also shown for example in Figure 39. An important fact to stress is that the higher quality levels the more reliable are the uncertainties since the quality levels are a measure of the conﬁdence level. As for the other LSWT C-GLOPS products we recommend to use LSWT with quality levels 4 and 5 which are more reliable than the lower quality levels. These can be only used with extreme care. To conclude, the C-GLOPS 10-day LSWT product from the combination of SLSTR-A and SLSTR-B o↵ers more coverage and better quality both in terms of uncertainty and conﬁdence levels.

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Figure 42: SLSTR-AB minus SLSTR-A LSWT uncertainty di↵erence histogram for the dekad starting on the 01-Apr-2020 when the LSWT quality levels are qlA =5qlAB = 4, qlA =5qlAB = 3 and viceversa.

Figure 43: SLSTR-AB minus SLSTR-A LSWT uncertainty di↵erence histogram for the dekad starting on the 01-Apr-2020 when the LSWT quality levels are qlA =4qlAB = 3, qlA =4qlAB = 2 and viceversa.

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Figure 44: SLSTR-AB minus SLSTR-A LSWT uncertainty di↵erence histogram for the dekad starting on the 01-Apr-2020 when the LSWT quality levels are qlA =5qlAB = 2, qlA =3qlAB = 2 and viceversa.

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4.7 Visual inspection A visual validation has been carried out on the spatial distribution of the 10-day LSWT product with its associated uncertainty, quality level and standard deviation for the archive AATSR, the NRT SLSTR-A LSWT and the NRT SLSTR-AB LSWT products.

4.7.1 Spatial AATSR LSWT Figure 45 shows an example of spatial distribution of the AATSR LSWT, of the uncertainty and the number of observations for lake Winnipeg in Canada for the 10-day period starting on the 01- Jul-2005. We can notice lower uncertainties for areas where the number of observations is higher. Also, we can observe higher LSWT close to the coast of the lake as expected. Figure 46 shows the standard deviation of the observations used to construct the 10-day product which illustrates the variability of the observations. Standard deviation equal to 0 corresponds to cases where only one observation was present. In these areas the uncertainty is relatively high accounting for the lack of observations. The quality levels of the C-GLOPS product is generally greater than 2 since we have selected only observations with quality levels greater than 2 to generate the 10-day aggregation. In case of observations only of quality level 2 during the 10-day period, we have still generated the product but it has to be used with care. In Figure 46 the time of observation is presented. Clearly, areas with the same number of observations may have di↵erent observation time. Figure 47 shows a zoom over an area of the lake Winnipeg where observations on di↵erent days (on day 5 and day 8 of the 10-day period) gives raise to a gradient of about 2 K in the LSWT.

Figure 45: LSWT, uncertainty and number of observations for lake Winnipeg in Canada for the 10-day period starting on the 01-Jul-2005 with data from the AATSR instrument.

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Figure 46: Standard deviation of the observations, quality levels and observation time for lake Winnipeg in Canada for the 10-day period starting on the 01-Jul-2005 with data from the AATSR instrument.

Figure 47: LSWT, number and time of observations for a small area of the lake Winnipeg in Canada for the 10-day period starting on the 01-Jul-2005.

Figure 48 and 49 show the lake Winnipeg C-GLOPS product during the 10-day period starting on the 21-Nov-2005 again from AATSR. The LSWTs are close to the freezing point and the number of observations over the whole lake is reduced. The uncertainties are lower for higher number of observations. Quality levels are at the highest indicating high conﬁdence in the overall estimation.

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Figure 48: LSWT, uncertainty and number of observations for lake Winnipeg in Canada for the 10-day period starting on the 21-Nov-2005 with AATSR data.

Figure 49: Standard deviation of the observations, quality levels and observation time for lake Winnipeg in Canada for the 10-day period starting on the 21-Nov-2005 with AATSR data.

Figures 50 and 51 show the Aral Sea C-GLOPS product during the 10-day period starting on the 01-Jul-2005 using AATSR data. A temperature jump is noticeable in the eastern southern part of the lake due to the 10-day aggregation. In case of one observation, data come from di↵erent days (day 3 and day 10 of the 10-day period) with temperatures of about 2 K apart. Also, having a low number of observation is due to the fact that the AATSR instrument has a narrow swath.

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Figure 50: LSWT, uncertainty and number of observations for the Aral Sea in Kazakhstan for the 10-day period starting on the 01-Jul-2005 with AATSR data.

Figure 51: Standard deviation of the observations, quality levels and observation time for the Aral Sea in Kazakhstan for the 10-day period starting on the 01-Jul-2005 with AATSR data.

4.7.2 Spatial SLSTR-A LSWT Figure 52 shows an example of spatial distribution of the SLSTR-A LSWT, of the uncertainty and the number of observations for lake Enriquillo in Domenican Republic and lake Azuei in Haiti for the 10-day period starting on the 21-Jun-2018. We can notice that mainly only one observation was available in the 10-day period. In case of narrow/small lakes, the water detection will often imply there is some land in a cell, and the retrieval will not be made as often or will have a low quality level (as captured by the quality level ﬂag). Lake Azuei seems cooler than lake Enriquillo at the same time of observation but also the quality levels of the retrieval are lower.

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Figure 52: LSWT, uncertainty and number of observations for the lake Enriquillo in Dominican Republic and lake Azuei in Haiti for the 10-day period starting on the 21-Jun-2018 with SLSTR-A data.

Figure 53: Standard deviation of the observations, quality levels and observation time for the lake Enriquillo and lake Azuei in Dominican Republic for the 10-day period starting on the 21-Jun-2018 with SLSTR-A data.

4.7.3 Spatial SLSTR-AB LSWT Figure 54 shows an example of spatial distribution of the combined SLSTR-A and SLSTR-B LSWT, of the uncertainty and the number of observations for lake Hirfanli in Turkey for the 10-day period starting on the 21-Jul-2020. Despite the lake is quite small (the maximum distance from land on the lake is 4km), up to 12 observations were available to generate the 10-days aggregation. The uncertainty is quite low and the LSWT quite consistent throughout the lake. Figure 55 shows the quality levels which are mainly 5 except along the shores where the quality levels are lower since there may be land/water mixed cells. High quality levels indicate high reliability of the uncertainty.

Figure 54: LSWT, uncertainty and number of observations for the lake Hirfanli in Turkey for the 10-day period starting on the 21-Jul-2020 with SLSTR-A and SLSTR-B data.

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Figure 55: Standard deviation of the observations, quality levels and observation time for the lake lake Hirfanli in Turkey for the 10-day period starting on the 21-Jul-2020 with SLSTR-A and SLSTR- B data.

5 Summary

The assessment of the AATSR and SLSTR-A LSWT time series for the sample lakes shows that the product is consistent with the seasonal pattern in time. Regarding the SLSTR-A NRT LSWT time behaviour, the comparison with the climatology shows a good agreement with the seasonal pattern. The distribution in space of the LSWT for AATSR, SLSTR-A and SLSTR-AB seems consistent with the distance to land and the inclusion of SLSTR-B data improves the product in both coverage and quality. Some pattern of the LSWT distribution in space are due to temporal averaging. These patterns can be detected using the variables containing the number of observations and the observation times. The validation of the AATSR and SLSTR-A LSWT with in-situ measurements shows a consistently satisfactory agreement especially for high quality levels, although for SLSTR-A the number of matches were significantly lower. In-situ measurements are seldom accompanied by an uncertainty and they cannot be considered as an absolute truth. Quality control measures have applied to the in-situ measurements both automatically and manually but, as already stated, the measurements lack of the uncertainty. The assessment of the reprocessed SLSTR-A 10-day LSWT data highlights the benefits of the reprocessing which are due to the inclusion of granules which were not available at the time of the NRT processing but also due to the fact that NTC L1b granules o↵er a better quality than the NRT L1b granules. Therefore, we conclude that periodic reprocessing is beneficial for the usability of the data. The assessment of the C-GLOPS SLSTR-AB 10-day LSWT product shows an improved coverage and more importantly a higher quality and more reliable product. Table 15 shows an overview on the performance of the products with respect to the user requirements. Regarding the location accuracy, an issue regarding the flagging of degraded geolocation in NTC L1b SLSTR-A files seem not to be fully resolved yet at EUMETSAT.

6 Limitations and known issues

Limitations and known issues are reported in the ATBD. Crucial is the water detection algorithm which requires thresholds on visible channels and combinations. Consequently, these thresholds are dependent principally on the color of the lake but also on wind and satellite zenith angle. Another

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Table 15: Product characteristics against user requirements for the LSWT product.

Requirements AATSR LSWT product SLSTR-A LSWT product characteristics characteristics

Spatial resolution: 1km 1km product has been created 1km product has been created and assessed and assessed Location accuracy: 1/3 of the according to [ESA and according to [ESA and EUMET- at nadir instantaneous field of Telespazio, 2012] less than 1 SAT, 2018] and [ESA, 2011] the view. pixel geolocation accuracy meets the mission requirements (less than 0.5 pixel) Coverage: global windows fulfilled fulfilled LSWT accuracy: fulfilled fulfilled Decades: days 1 to 10, days 11 to fulfilled fulfilled 20 and days 21 to end of month for each month of the year important limitation related to the validation is the lack of accurate in-situ measurements and of a NRT in-situ lake temperature measurement database. Also, the knowledge of the uncertainty on the in-situ measurements would be helpful.

7 Recommendation

Manual inspection of all products for more than 1000 water bodies is impossible and in most cases requires local knowledge. The validation of the products is, and always will be, based on a small sample of well-studied areas. Users of these products are therefore advised to inspect the results for their area of interest before generating derivative products. This inspection could include, for example, histograms to identify outliers. Users are also advised to take into account the number of observations underlying the results. Where observations are sparse, having a small number of satellite passes to cover a large water body can lead to visual inconsistencies that do not reﬂect the state of the water body at any particular time this is merely the nature of creating aggregate products. Expert users are encouraged to take part in the validation of these products that is increasingly taking place at the global scale. We recommend to use the LSWT with quality levels higher than 2, although 4 and 5 are mostly recommended where the retrieval has been performed with higher conﬁdence.

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A In situ data sources

Table 16: In-situ sources

Source Lake name (number of locations) NDBC Superior (3), Huron (2), Michigan (2), Erie (1), Ontario (1) FOC Superior (1), Huron (4), Great Slave (2), Erie (2), Winnipeg (3), Ontario (4), Woods (1), Saint Claire (1), Nipissing (1), Simcoe (1) Michigan Technological University (USA) Superior (2) University of Minnesota (USA) Superior (2) Northern Michigan University (USA) Superior (3) Superior Watershed Partnership (USA) Superior (1) U.S. Army Corps of Engineers (USA) Superior (1) NOAA GLERL (USA) Huron (2), Michigan (1) University of Wisconsin-Milwaukee (USA) Michigan (2) Northwestern Michigan College (USA) Michigan (1) University of Michigan CIGLR (USA) Michigan (2) Limno Tech (USA) Michigan (3), Erie (4) Illinois-Indiana Sea Grant (USA) Michigan (2) Michigan Technological University (USA) Michigan (1) Irkutsk State University (Russia) Baikal (1) Regional Science Consortium (USA) Erie (1) UGLOS (USA) Erie (2) LEGOS (France) Issykkul (1) SLU (Sweden) Vänern (6), Vättern (2), Mälaren (8), Hjal- maren (1) Siljan (1), Bolmen (2), Ekoln (1), Roxen (1) SYKE (Finland) Inarinjärvi (1), Orivesi and Pyhäselkä(2), Pieli- nen (2), Oulujärvi (1), Haukivesi (1), Nasijärvi (1), Onkivesi (1), Puulavesi (1), Vanajavesi (1), Pyhäjärvi (1), Lappajärvi (1) Vermont EPSCOR (USA) Champlain (1) Eric Leibensperger and SUNY Plattsburgh (USA) Champlain (1) Nipissing University (Canada) Nipissing (2) Enner Alcantara, São Paulo State University (Brazil) Tucurui (1), Itumbiara (1)

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National Park Service (USA) Mead (3) Utrecht University (The Nederlands) Garda(1) CNR (Italy) Garda (1) NOAA NWLON (USA) St. John River (3) CNR Institute for Water Research (Italy) Maggiore (1) KU Leven (Belgium) Kivu (1) Uppsala University (Sweden) Vättern (1) NIWA (New Zealand) Taupo (1) GLEON Balaton (1) BLI (Hungary) Balaton (6) KDKVI (Hungary) Balaton (3) UMR-CARRTEL (Switzerland) Geneva (1) TERC (USA) Tahoe (1) Estonian University of Life Sciences Võrstjärv (1) Martin Dokulil (Austria) Neusiedl (1) Israel Oceanographic and Limnological Research Sea of Galilee (1) Kinneret Limnological Laboratory Sea of Galilee (1) Universidad del Valle de Guatemala Atilian (1) Universita degli Studi di Perugia (Italy) Trasimeno (1)

Acknowledgment

The authors are thankful to Iestyn R. Woolway who has established the contacts to set up the in situ database and to all the institutions listed in Table A that have provided the in-situ data and in particular to Enner Alcantara (SaõPaulo State University, Brazil), Jean-Fran¸cois Crétaux (LEGOS, France), Margaret Dix (Universidad del Valle de Guatemala, Guatemala), Martin Dokulil (Mondsee, Austria), Claudia Dresti (CNR Institute for Water Research, Italy), Gideon Gal (Israel Oceanographic and Limnological Research Institute, Israel), Claudia Giardino (CNR Institute for Electromagnetic Sensing of the Environment, Italy), April James (Nipissing University, Canada), Alo Laas (Estonian University of Life Sciences, Estonia), Eric Leibensperger (State University of New York at Plattsburgh, USA), Alessandro Ludovisi (University of Perugia, Italy), Ghislaine Monet (UMR CARRTEL, France), Sebastiano Piccolroaz (University of Trento, Italy), Tiina Nõges (Esto- nian University of Life Sciences, Estonia), Peeter Nõges (Estonian University of Life Sciences, Esto- nia), Antti Raike (SYKE, Finland), Alon Rimmer (Israel Oceanographic and Limnological Research Institute, Israel), Michela Rogora (CNR Institute for Water Research, Italy), Geo↵rey Schladow (UC-Davis Tahoe Environmental Research Center, USA), Eugene Silow (Irkutsk State University,

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Russia), Evangelos Spyrakos (University of Stirling, UK), Wim Thiery (KU Leuven, Belgium), Piet Verburg (NIWA, New Zealand).

References

L. Carrea, O. Embury, and C.J. Merchant. Datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identiﬁer and lake- centre co-ordinates. Geoscience Data Journal, 2:83–97, 2015.

ESA. Sentinel-3A Mission Requirements Traceability Document (MRTD), 2011. ESA and EUMETSAT. Sentinel-3A Product Notice - SLSTR Level-1B NRT and NTC (S3A.PN- SLSTR-L1.05), 2018. ESA and Telespazio. Envisat AATSR performance report, 2012.

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