Last modified on Sept 04, 2023 16:39

Contributors: L. Carrea (University of Reading), C.J. Merchant (University of Reading)

Issued by: University of Reading / L. Carrea, C.J. Merchant

Date: 14/04/2023

Ref: C3S2_312a_Lot4.WP2-FDDP-LK-v1_202212_LSWT_PQAR-v4_i1.1

Official reference number service contract: 2021/C3S2_312a_Lot4_EODC/SC1

History of modifications

List of datasets covered by this document

Related documents 

Acronyms

General definitions

Bias: Estimate of a systematic error.

Brokered Product: A brokered product is a pre-existing dataset to which the Copernicus Climate Change Service (C3S) acquires a license, for the purpose of including it in the Climate Data Store (CDS).

L2P – Geophysical variables derived from Level 1 source data on the same grid (typically the satellite swath projection). Ancillary data and metadata added following Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification.

L3U – L2 data granules remapped to a regular latitude/longitude grid without combining observations from multiple source files. L3U files will typically be "sparse" corresponding to a single satellite orbit.

L3C – Lake Surface Water Temperature (LSWT) observations from a single instrument combined into a space-time grid. A typical L3C file may contain all the observations from a single instrument in a 24-hour period.

L3S - Level 3 super-collated: this is a designation of satellite data processing level. "Level 3" indicates that the satellite data is a geophysical quantity (retrieval) that has been averaged where data are available to a regular grid in time and space. "Super-collated" indicates that (for those periods where more than one satellite data stream delivering the geophysical quantity has been available) the data from more than one satellite have been gridded together into a single grid-cell estimate, where relevant.

Lake Surface Water Temperature (LSWT) - is the measure of the water temperature at the surface of the lake (skin temperature).

Target requirement – ideal requirement which would result in a significant improvement for the target application.

Uncertainty: The dispersion of values that might reasonably be attributed to a measurand given a measured value, quantified by the standard deviation.

Scope of the document

This document is the Product Quality Assessment Report (PQAR) for the Copernicus Climate Change Service (C3S) Lake Surface Water Temperature product. This document summarises the results from the product assessment based on the Product Quality Assurance Document [D4].

Executive summary

The C3S Lake production system (C3S ECV LK) provides an operational service, generating lake surface water temperature (LSWT) and lake water level (LWL) Climate Dataset Records (CDRs) for a wide variety of users within the climate change community. The present document covers the LSWT system.

This document presents the results of the quality assessments undertaken for the product including (i) the generation of the matchups at L2 with an updated in-situ measurement database, (ii) the validation of the L2 temperatures, (iii) the validation of the European Space Agency (ESA) Climate Change Initiative (CCI) LAKES¹/C3S LSWT product, including the temperature and its uncertainty. An assessment of which lakes return more usefully complete data series is also made, since this is not a priori known before processing. The validation of the LSWT climate data record (CDR) v4.0 has been carried out with a larger in-situ database with respect to the validation of the previous versions of the C3S LSWT CDR.

This document concerns the fourth contractual version of LSWT products for C3S, corresponding to “LSWT v4.5” processing.

1. Product validation methodology

1.1. Validated products

The C3S Lakes product comprises a long-term climate data record (CDR), which includes a brokered dataset, the ESA CCI LAKES LSWT v2.0.2 dataset (from 1995 to 2020) and the C3S extension (from 2021 to 2022). The time series have been computed from sensors on multiple satellites. Lake-specific consistency adjustments between sensors have been applied using the MetOpA Advanced Very-High Resolution Radiometer (AVHRR) instrument as a reference as MetOpA AVHRR has the best combination of length of record and data density for this purpose. For Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra, only LSWTs of quality level (QL) 4 and 5 have been used and an ad-hoc adjustment has been applied. The same algorithm has been used to retrieve the LSWT from all sensors in order to obtain consistent time series for each of the ~2,000 target lakes. The target list was defined within the ESA CCI LAKES project and can be found online² and in the Product User Guide and Specifications (PUGS) [D5].

The time periods used for each satellite/instrument are provided in Table 1. Not all lakes include LSWT from all sensors in the series because of differing density and geometry of observation. The products are observational, so gaps in time and space are common for all the lakes due to cloud cover and limited swath of the instruments.

Table 1. Time periods for the satellite/instrument used to generate the LSWT product.

A detailed description of the product generation is provided in the Algorithm Theoretical Basis Document (ATBD) [D3] with further information on the product given in the PUGS [D5].

1.2. Validating datasets

A match-up dataset was constructed from the in-situ temperature data collected through the ARCLake³ project, the GloboLakes project⁴ and the EU Surface Temperature for All Corners of Earth (EUSTACE) project⁵ and the ESA CCI LAKES project. The dataset was expanded every year for the validation of the C3S product.  For this version, the dataset consists of 215 observation locations covering 81 lakes. For some of the existing sites, low temporal frequency data have been replaced with high temporal frequency temperature data. This has improved the number of matches. Details of the in-situ observation locations with their sources are given in Table 2, which reports all locations for the target lakes where there are matches.

Table 2. List of the sources of the in-situ data.

Table 3 lists the 81 lakes (where matches have been found) together with their maximum distance from land (Carrea et al. 2015), which is an indication of each lake's size that is meaningful for LSWT remote sensing. Figure 1 shows the distance to land for Loch Lomond in Scotland. The highest resolution of the instruments used for the retrieval of the LSWT is 1 km. If, as here, the lake has a maximum distance to land of 1.5 km, the LSWT retrieval is likely to be available for that part of the lake from time to time, when a combination of factors occurs: i) the satellite image locations line up so that some pixels are nominally fully water pixels, which requires the satellite view zenith angle (which affects the on-the-ground resolution) to be such that the half-pixel size is smaller than the distance to coast; (ii) these pixels are cloud free; and (iii) image geolocation errors (which can be of order 1 pixel uncertainty) are small enough so that the nominally water-filled pixels are truly water-filled, meaning that the water detection tests are passed.

Figure 1. Distance to land in km for Loch Lomond in Scotland where each dot represents a 1/360° cell.

Table 3. Lakes in ESA CCI LAKES. List of lakes with in-situ data and their max distance to land.

A considerable number of the lakes that have been used for the validation are small (which means a max distance to land comparable with the sensor resolution of ~1km). This, given the previous discussion, is the most challenging situation for the LSWT retrieval.

Note also that some of the locations of in-situ measurements are situated close to the coast. The nearest water-filled pixels may not overlap with the in-situ measurement occasion in this circumstance, thus increasing the uncertainty in the comparison from spatial representativity.

The plot of the geographical distribution of the lakes with in-situ measurements is shown in Figure 2. The lakes sampled cover most of the latitudes and the continents. Most of the lakes are located in the Northern Hemisphere at relatively high latitudes.

Figure 2: Geographical distribution of the lakes with in-situ measurements.

As the in-situ data are from a variety of sources, with different formats, considerable effort has been put in consolidating each new source of data to a standard format for use in validation. A quality control procedure for checking the in-situ data is also necessary, since they are not always credible. This is partly automated and partly done by manual inspection. The quality control procedure was initiated within the ARCLake⁶ project, updated within GloboLakes⁷, ESA CCI LAKES and C3S. Moreover, the in-situ data have a range of characteristics:

• the measurements have been taken at different depths up to 1 m;
• the temporal sampling of the measurements ranges from 15 minutes to few times a year;
• the temporal availability of the in-situ measurements varies from few months up to covering all the satellite period;
• for some locations the measurements are averages while for others they have been taken at the reported time;
• none of the in-situ measurements which have been collected are accompanied by an uncertainty estimation.

While part of the data are available online, the majority has been collected through personal communications and in a proportion of cases we are not licensed to redistribute the data.

1.3. Description of product validation methodology

1.3.1. Overview

The quality assessment of the LSWT product consists of the comparison of the dataset with independent in-situ data. The satellite – in-situ matches are created at L2, i.e., in the original satellite coordinates. The output products are gridded (L3S), so the L3 grid cell corresponding to L2 match is identified and the L3 product is thus directly validateded. The validation of the L3S product is performed using conventional (mean and standard deviation of the satellite minus in-situ temperatures) and robust statistics (median and robust standard deviation of the satellite minus in-situ temperatures).

A separate dimension of product quality is the data density, which varies greatly between lakes and is also assessed. The metrics used in the density assessment are: i) the number of target lakes that yield no useful data; and (ii) a map of the number of days when LSWT with quality levels=(4,5) has been retrieved at the centre of each lake (defined as the position of the max distance to land, Carrea et al, 2015).

1.3.2. Generation of the L2 matchup database and L2 validation

The satellite – in-situ observation matches have been generated at L2, namely in satellite coordinates.
A per-sensor matchup is created and it contains space and time coincident satellite and in-situ data. It also provides the reference and time of the in-situ location, and the associated LSWTs quality level and uncertainty from the L2 LSWT product. The matchup is created for satellite observations based on the following criteria:

• Spatially: within 3 km from the location of the in-situ measurement and
• Temporally: within 3 hours for the in-situ measurements where the measurement time was available. For some of the lakes only daily mean or unknown time of the measurements was on the record, therefore the day was matched.

1.3.3. Validation of the L3S ESA CCI LAKES/C3S LSWT v4.5

The differences between the L3S LSWT and in-situ data are analysed using both standard and robust statistics. Robust statistics are less influenced by outliers in the distribution of differences. Time series of the absolute temperatures together with their difference are generated for each of the quality levels to verify the validity of the quality levels. Box plots are produced for each quality level, together with a scatter plot of the difference per quality level (where some jitter has been randomly added to reduce overlapping). Finally, the difference is plotted as a function of the in-situ temperature and a line fit is performed for each quality level. The robust statistics is also investigated per quality level for each year and for each lake.

1.3.4. Validation of the LSWT uncertainty

The validation of the L3S ESA CCI LAKES/C3S LSWT v4.5 product is carried out by comparing the satellite minus in-situ temperature difference with the combination of the satellite uncertainty (present in the products) and an estimate of the in-situ uncertainty (which is relatively poorly known). In an ideal case, the standard deviation of the differences between the satellite LSWT and a reference LSWT would equal the combined uncertainty measurement plus the uncertainty attributable to representativity effects.

1.3.5. Number of lakes in ESA CCI LAKES with LSWT

An assessment of the lakes with no retrieved LSWT is reported together with an estimation of the number of observations per lake, which is performed counting the number of days with observations at the lake centre.

2. Validation results

This section provides the results of the LSWT product validation for the ESA CCI LAKES/C3S CDR.

2.1. Validation of the ESA CCI LAKES/C3S L3S LSWT v4.5

The matchup is attempted per sensor over the 215 locations on 81 lakes. For 59 locations hourly (or sub-hourly) in-situ measurements were available. Subsequently, the correspondent L3 cell is found and stored in a L3 database.

The total number of matches is 97,792. The number of matches varies per year and, since the AVHRR sensors have a larger swath than the Along Track Scanning Radiometer (ATSR) sensors (ATSRs swath is 500 km and AVHRRs swath is ~2,900km), after year 2007, the number of matches clearly increases as it is shown in Figure 3. We can notice another clear increase in 2000 when MODIS started to be used and in 2017, when the AVHRR on MetOpA is used together with the AVHRR on MetOpB and Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel3A. In 2020 the number of matches is lower than the previous year because in 2019 AVHRR on MetOpA and on MetOpB are used until August and after that, only SLSTR on Sentinel3A and Sentinel3B are used. Moreover, due to the COVID-19 pandemic, a disruption in the in-situ data collection has occurred. The SLSTR swath (~14,00km at nadir view) is larger than the ATSRs swath, but is smaller than the AVHRR. The number of matches depends also on the availability of the in-situ data, since a different number of locations is available every year, as shown in Figure 4. The number of locations where in-situ measurements have been taken has more than doubled since 1995; however, a portion of the measurement frequency is daily.

Figure 3: Quality assessment of the LSWT product consisting on the comparison of the satellite dataset with independent in-situ data from locations on lakes. The number of matches between LSWT satellite and in-situ data at L3 are shown (y-axis), per year (x-axis).


Figure 4: Quality assessment of the LSWT product consisting on the comparison of the satellite dataset with independent in-situ data from locations on lakes. The number of locations with matches between LSWT satellite and in-situ data at L3 (y-axis), per year (x-axis) is shown.

Table 4 reports the robust statistics and the traditional statistics per quality level for the matches across all the locations where in-situ measurements were available as reported in Table 3.

The agreement between satellite and in-situ measurements varies according to the quality levels in a way that is expected. In Table 4, the number of matches per quality levels are listed together with the median and the robust standard deviation (RSD) of the satellite minus in-situ temperature difference and the traditional metrics, the mean and the standard deviation. The difference between the median and the mean is almost neglectable for quality level 5, and it increases as the quality levels get lower, showing a symmetry in the distributions for high quality levels.

Table 4: Robust and traditional statistics of the satellite minus in-situ temperature difference per quality level.

The best agreement is for quality levels 4 and 5, which reflect a higher degree of confidence in the validity of the satellite estimate. As noted in the PUGS [D5], our recommendation to users is to use the highest quality level in preference (4 and 5), unless they have specifically verified for a given lake that lower quality levels are fit for their application. Quality level 3 data comparison with the in-situ data shows an agreement that may be acceptable to some users; however, they have to be used with care. Quality level 1 data should never be used, and they are classified as “bad data”, and quality level 2 data should be thoroughly inspected if the use is strictly necessary.

A contribution to the difference on average is the expected skin effect. Infrared radiometers are sensitive to radiation emitted between the air-surface interface and 20 mm below the interface, while the in-situ measurements considered here are taken at a distance up to 1m from the air-surface interface. During the night, the surface of the water is generally cooler than the subsurface by 0.2 K (Saunders, 1967; Embury et al., 2012). However, during the day, thermal stratification due to solar heating contributes to the difference in temperature between the radiometric lake surface and the in-situ measurement depth (up to 1 m). At present, we have not assessed the degree of near-surface stratification to be expected in different lakes, which depends on fetch, weather conditions, depth of in-situ measurement, and any local vertical mixing perturbations introduced by the presence of the in-situ measurement system. In summary, a contribution to the satellite minus in-situ temperature difference is the expected skin effect of 0.2 K, but it is difficult to infer a precise contribution of satellite LSWT biases to the remaining residual, in the face of in-situ errors and unquantified geophysical effects (near-surface stratification other than the skin effect, plus horizontal variability) (Hondzo et al., 2022).

The distributions of the satellite minus in-situ temperature differences per quality level are reported in Figure 5. The difference deteriorates as the quality level decreases, becoming a larger (in absolute terms) negative value with larger spreads, indicating less accurate and precise measurements. The distributions become more stretched as the quality levels decreases. The satellite minus in-situ temperature differences have been reported also for quality level 1 to compare how the distribution of the difference is for bad data. However, quality level 1 data should never be used.

Figure 5: Distribution of the satellite minus in-situ temperature difference per quality level.

The median and the robust standard deviation per quality level (except for quality level 1, which are bad data) per year for all the lakes is shown in Figure 6 and Figure 7 together with the number of matches. For high quality levels the median and the robust standard deviation are consistent throughout the years when different instruments have been adopted and a different number of matches is available. They deteriorate in the lower quality levels, especially before 2007, when the AVHRR sensors have been introduced. The number of matches for quality level 5 is consistently the highest. From September 2019 until October 2020, the LSWTs are derived solely from SLSTRs and MODIS. The median difference for 2019, 2020, 2021 and 2022 is slightly more negative than during the previous years, amounting to -0.225 K, -0.340 K, -0.320 K and -0.220 K respectively, whereas the robust standard deviation is comparable with values observed for the previous years.

Figure 6: Annual variations in (upper plot) median temperature difference between satellite-derived LSWT and in-situ temperature measurements, and (lower plot) the number of matches per LSWT data quality level for all the lakes with in-situ data.

Figure 7: Annual variation in (upper plot) the robust standard deviation in in-situ temperature difference (Satellite-derived LSWT minus in-situ temperature measurements), and (lower plot) the number of matches per LSWT data quality level for all the lakes with in-situ data.

The median and robust standard deviation have been also inspected per lake. Figure 8 and Figure 9 show the plots together with the correspondent number of matches where the lakes have been ordered by total number of matches across the quality levels. Higher numbers of matches are found for lakes where data were available for longer periods but also where hourly/sub-hourly measurements were available and for sites far from the lake shore. The median and robust standard deviation are consistently better for quality level 5 throughout the lakes. In addition, as the number of matches per lake increases, the median difference converges towards a small negative number for all the quality levels above two, and the robust standard deviation becomes more stable across the lakes. However, for some lakes, a relatively higher value of the RSD can be found. For example, lake 1115 (lake Neusiedl in Austria) has a higher value of RSD than lakes with a similar number of matches. Lake Neusiedl is a shallow lake with maximum depth of 1.8 m and therefore strong variations in temperature are expected to be found in both the in-situ and satellite data, as shown in Figure 10. In this case, since the temporal match is within three hours, the difference can be substantial.  Regarding the median difference, lake 8 (lake Baikal in Russia) has a very high median difference for quality level 4. This is due to the fact that most of the in-situ measurements are monthly data. Moreover, most of the matches have LSWT of quality level 5 (the numbers of matches are only 3, 10, 23 and 80 for quality levels 2,3,4 and 5, respectively).

Figure 8. Median temperature difference between satellite-derived LSWT measurements and in-situ measurements across the various lakes (upper plot), and the number of matches (in log scale) per LSWT data quality level used for the analysis of each lake (lower plot). The lake ID is given on the horizontal axis.

Figure 9. Variation across the lakes examined in the robust standard deviation of satellite minus in-situ temperature differences (upper plot), and the number of matches that underpinned each lake analysis (lower plot), per quality level. Differences were calculated between satellite-derived and in-situ measurements of LSWT . The lake ID is given on the horizontal axis.

Figure 10. Satellite observations (dots), in-situ data that match the satellite observations (white dots), daily in-situ measurements (black line with standard deviation as a grey band), satellite minus in-situ T difference for quality levels 4,5 (green line) and climatology (golden line with climatological variability as the yellow band) for lake Neusiedl (Austria; lake ID 1115) in 2019.

For Lake Superior (lake ID 2), where many sites are available, the robust statistics of the difference for all the matches of quality level 4, 5 have been plotted per sites in Figure 12, showing consistency for close sites and higher variability/median difference close to the lake shore.

Figure 11. Satellite minus in-situ temperature difference median for all the sites on Lake Superior (lake ID 2) for quality level 4,5 (latitude starts from 46.5N).

Figure 12. Satellite minus in-situ temperature difference robust standard deviation for all the sites on Lake Superior (lake ID 2) for quality level 4,5 (latitude starts from 46.5N).

The satellite minus in-situ temperature differences across all data have been plotted against the in-situ temperature in Figure 13. The best fit trend in difference against in-situ temperature is also shown as a straight line. An offset in this line indicates overall relative bias. A slope in this line indicates that the satellite minus in-situ difference is different for lower vs. higher temperature relative to the in-situ.

Figure 13: Satellite minus in-situ temperature difference against in-situ temperature per quality level, where the red line is the best fit. The colour of the dots represents the data density.

For quality level 5, the slope is -0.013 K K^-1, which means that over the 25 K range of lake temperatures in the data, the satellite is warmer relative to in-situ observations by 0.325 K for the lowest temperatures compared to the warmest temperatures. The slopes for QL 4, 3 and 2 are -0.002, 0.23 and 0.012 K/K, respectively.

The time series of the satellite and the in-situ temperature together with their difference have been inspected and they are reported here for two challenging lakes. The first is a small lake (lake Erken in Sweden, lake 6785). The second is a site where daily averages were available (Sea of Galilee, lake 1196).

The location where the in-situ measurements have been collected on Lake Erken is shown in Figure 14 (red dot). Figure 15 and Figure 16 show the satellite observations and the in-situ measurements in 1997, when only ATSR2 was utilised, and in 2008, when observations from AATSR, MODIS and AVHRR-A were used. For both years, the satellite observations follow the in-situ measurements, which were temporal high frequency measurements, remarkably well. Figure 17 shows a very good agreements between satellite observations and in-situ measurements in 2020 when MODIS and SLSTR on both Sentinel3A and Sentinel3B were used.

Figure 14: Location of the in-situ measurements (red dot) on lake Erken in Sweden (lake ID 6785) where each dot represents a 1/120° resolution cell.

Figure 15: Satellite observations (dots), in-situ data that match the satellite observations (white dots), in-situ daily measurements (black line), satellite minus in-situ temperature difference for quality levels 4,5 (green line) and climatology (golden line with climatological variability as the yellow band) for lake Erken (Sweden; lake ID 6785) in 1997.

Figure 16: Satellite observations (dots), in-situ data that match the satellite observations (white dots), in-situ measurements (black line), satellite minus in-situ temperature difference for quality levels 4,5 (green line) and climatology (golden line) for lake Erken (Sweden, lake ID 6785) in 2018.

Figure 17: Satellite observations (dots), in-situ data that match the satellite observations (white dots), in-situ measurements (black line), satellite minus in-situ temperature difference for quality levels 4,5 (green line) and climatology (golden line) for lake Erken (Sweden; lake ID 6785) in 2020.

Figure 18 shows the satellite observations and the in-situ measurements in 2008 and 2011 for the Sea of Galilee in Israel (lake 1196), where in 2008 only daily measurements were available. The plot shows an agreement for the few matches on a lake where we assume that probably not many temperature variations take place as confirmed by the 2011 plot where high frequency in-situ data were available.

Figure 18: Satellite observations (dots), in-situ data that match the satellite observations (white dots), in-situ daily measurements (black line), AVHRR-A minus in-situ T difference for quality levels 4,5 (green line) and climatology (golden line) for the Sea of Galilee (Israel; lake ID 1196) in 2008 (low frequency in-situ data; above) and in 2011 (high frequency in-situ data; below).

Figure 19 and Figure 20 show the satellite observations and the in-situ measurements in 2021 and 2022 for Lake Superior (lake ID 2) at the site 01 (Figure 19) and at the site 02 (Figure 20). Lake Superior was particularly warm in 2021 whereas in 2022 it was cooler than average. In 2022, a sharp increase in temperature appears in August for both locations. However, in 2022, the LSWT for site 02 increases abruptly to a higher temperature than the LSWT for site 01 (see Figure 19 and Figure 20 upper plots). In site 02, a LSWT of 15°C is reached after the steep rise, whereas in the site 01, the LSWT of 15°C is reached only in mid-September. Both sites are far from the lake shore but on different parts of the lake (see Figure 21), highlighting the temperature spatial structure. As shown in Figure 19 and Figure 20 on the upper plot, in 2021, in both locations the water at the surface was warmer than the climatology plus one standard deviation of the climatology for most of the year. All this is reflected in both the in-situ and the satellite LSWT. The high number of matches is due to the fact that three instruments were employed for the satellite measurements.

Figure 19: Satellite observations (dots), in-situ data that match the satellite observations (white dots), in-situ daily measurements (black line), AVHRR-A minus in-situ T difference for quality levels 4,5 (green line) and climatology (golden line) for site 01 on Lake Superior (Canada/USA; lake ID 2) in 2021 (above) and 2022 (below).

Figure 20: Satellite observations (dots), in-situ data that match the satellite observations (white dots), in-situ daily measurements (black line), AVHRR-A minus in-situ T difference for quality levels 4,5 (green line) and climatology (golden line) for site 02 on lake Superior (Canada/USA; lake ID 2) in 2021 (above) and 2022 (below).

Figure 21: Location of the in-situ measurement sites on Lake Superior in Canada/USA (lake ID 2).

2.2. Validation of the ESA CCI LAKES/C3S L3S LSWT v4.5 uncertainty

The LSWT uncertainty has been validated comparing the satellite minus in-situ temperature differences and the correspondent LSWT and in-situ uncertainties. The following quantity is calculated for each match:

Where T indicates temperature for LSWT and in-situ data as indicated in the subscripts. σ is the measurement uncertainty (for LSWT and in-situ) and the uncertainty derived for the point-to-pixel representativity effects (indicated by repr in the subscript). The in-situ measurement uncertainty is not known for the data we have, and we assume it to be σ_INSITU=0.2 K, a value based from deployment of similar measurement technologies to the ocean. The representativity effect is presently unquantified and we set it to 0 K; neglecting representativity has the tendency to make the LSWT uncertainty look underestimated. σ²_LSWT is context sensitive and is provided in the products, and therefore varies from match to match.

The distribution of Δ should be a Gaussian distribution with mean equal to 0 and standard deviation equal to 1 when all the uncertainties are well estimated. Figure 22 shows the histograms of the uncertainties per quality level including the fitted Gaussian and the target Gaussian distributions.

For quality level 5, the Gaussian fit has width 2.38, which means that observed differences are more different than expected from the quoted uncertainties. This may be partly caused by the underestimation of the product uncertainties, but could also arise to some degree by the fact that lake in-situ data (being more diverse) have larger uncertainty than the assumed value (based on experience of ocean observations), and because representativity is neglected. For this reason, interpretation of this outcome is currently ambiguous, and research is needed to better understand the in-situ uncertainty and representativity effects.

If σ_INSITU=0.5 K, the Gaussian fit has width 1.54 and mean -0.28 for quality level 5, which means that the observed differences are closer to the quoted uncertainties (Figure 23).

Figure 22: LSWT uncertainty validation per quality level (indicated in legend): histograms of Δ (see equation 1) for σ_INSITU=0.2 K. The black curve is the Gaussian curve with zero mean and standard deviation equal to 1. The red curve is the gaussian fit of the histogram. 

Figure 23: LSWT uncertainty validation per quality level (indicated in legend): histograms of Δ (see equation 1) for σ_INSITU=0.5 K. The black curve is the Gaussian curve with zero mean and standard deviation equal to 1. The red curve is the gaussian fit of the histogram.

Figure 24 presents the corresponding quantile-quantile (Q-Q) plots, which show the values of actual percentiles of the distribution relative to their theoretical values assuming the ideal Gaussian distribution. The points do not lie on a straight line, showing that the differences include more extreme values than would be expected if they truly came from a normal distribution.

Figure 24: LSWT uncertainty validation per quality level: Quantile-quantile (Q-Q) plot of Δ (see equation 1). The blue line is the line with 45° inclination and the red line is the linear fit. The points do not lie on a straight line, showing that the differences include more extreme values than would be expected if they originated from a normal distribution.

2.3. Missing lakes and number of observations

For 12 target lakes, no LSWT has been obtained, largely due to the fact that they are too small. These lakes are listed in Table 5 together with the estimated maximum distance to land. All the lakes are too small with respect to the instrument resolution (~1 km), except three lakes which have been labelled as sea in the mask.

Table 5. Lakes in ESA CCI LAKES with no LSWT observations in the product. All the lakes are too small with respect to the instrument resolution (~1 km), except the lakes "labelled as sea" in the mask.

Where LSWT is available, an assessment of the number of days between 1995 and 2022 has been carried out at the lake centre, defined as the location on the lake most distant from land (Carrea et al., 2015). Figure 25 shows the number of days with satellite observations in the ESA CCI LAKES/C3S LSWT v4.5 dataset for quality levels 2, 3, 4 and 5. The most observed lakes are at mid latitudes in the Northern and Southern Hemisphere. The lake with the most observed lake centre is the Gariep dam (lake ID 460) in South Africa with 6,408 days of observations and the lake with the least observed centre is lake Jijila (lake ID 16662) in Romania, with only two observations. Altogether, there are 9,757 days when we have some observation over the globe, which means that along 9,757 days, there was at least one satellite recording valid measurements over any of the 2,024 CCI lakes. If we consider only quality level 4 and 5, the number of observations of the lake centre decreases. For example, the centre of the Embalse Duale Peripa in Ecuador (lake ID 308) has seven LSWTs of quality level 4,5 and 167 of quality level 2,3. The Gariep dam has most of the observations of the lake centre of quality level 4,5. The LSWTs of quality level 2,3 are only 374.

Figure 25: Map of the number of observations at the lake centre in the ESA CCI LAKES/C3S LSWT v4.5 dataset for quality levels 2, 3, 4 and 5. In order to reduce overlapping, the longitude of the lake centre has been shifted where necessary, but the latitude has been maintained.

3. Application(s) specific assessment

No application(s) specific assessments have been undertaken for the version 4.5 of the C3S lake surface water temperature dataset.

4. Compliance with user requirements

The requirements for the C3S Lake water levels are described in the 2023 Target Requirements and Gap Analysis document (TRGAD) [D1].

Table 6: Compliance with user requirements for the C3S Lake surface water temperature.

The user requirements as defined in the TRGAD [D1] are mostly met. The spatial coverage can be considered as global since the lakes are distributed in all the continents. More lakes could be included to increase the coverage and also to increase the number of smaller lakes which are the most difficult in regard to LSWT retrieval, given the instrument resolution. The spatial resolution is limited by the current satellite resolution for thermal remote sensing which is about 1 km. An improvement in the spatial resolution could be brought in by re-gridding into a 0.025° regular grid, although at high latitude some gaps may appear with the current re-gridding algorithm and different techniques may be required. The temporal resolution is limited ultimately by the satellite swath and varies among lakes and among periods (the ATSRs have smaller swath with respect to the AVHRRs). The uncertainty varies among lakes but a big portion of the lakes throughout the years have uncertainties between the target (0.25 K) and the threshold (1 K) values from Global Climate Observing System (GCOS). The validation of the uncertainty can indicate that it is underestimated, although this validation cannot be considered conclusive since the uncertainty of the in-situ measurements is unknown.

More details can be found in the LSWT TRGAD [D1].

We strongly recommend the users to utilize the LSWT data together with the quality levels which indicate the confidence in the result. We recommend quality levels 4 and 5, while 3 and 2 can be use with care after a thorough inspection.

Acknowledgements

The authors would like to thank Lestyn R. Woolway who has established the contacts to set up the in-situ database. We would also like to thank all the institutions listed in Table 2 that have provided us with in-situ data, and in particular:

• Enner Alcantara, São Paolo State University, São Paolo, Brazil
• Gil Bohrer, The Ohio State University, Columbus, USA
• Jean-Francois Cretaux, LEGOS, Toulouse, France
• Margaret Dix, Universidad del Valle de Guatemala, Guatemala
• Martin Dokulil, Mondsee, Austria
• Hilary Dugan, Center for Limnology, University of Wisconsin-Madison, USA
• Gideon Gal, Yigal Allon Kinneret Limnological Laboratory, Israel Oceanographic and Limnological Research, Migdal, Israel
• Claudia Giardino, Istituto per il Rilavemento Elettromagnetico dell'Ambiente, National Research Council of Italy, Italy
• Johanna Korhonen, SYKE, Helsinki, Finland
• April James, Nipissing University, Canada
• Ilga Kokorite, University of Latvia and Latvian Environmental Geology and Meteorology Centre, Latvia
• Ben Kraemer, Leibniz institute for freshwater ecology and inland fisheries, Berlin, Germany
• Alo Laas, Estonian University of Life Sciences, Tartu, Estonia
• Eric Leibensperger, Ithaca College, Ithaca, NY USA
• Junsheng Li, Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, China
• Alessandro Ludovisi, Dipartimento di Biologia Cellulare e Ambientale, Universita' degli Studi di Perugia, Italy
• Shin-ichiro Matsuzaki, National Institute for Environmental Studies, Japan
• Linda May, Centre for Ecology and Hydrology, Edinburgh, Scotland UK
• Ghislaine Monet, UMR CARRTEL, Thonon le Bains, France
• Tiina Nogesand Peeter Noges - Estonian University of Life Sciences, Tartu, Estonia
• Sajid Pareeth – Institute of Water Education, Delft, The Netherlands
• Sebastiano Piccolroaz, Department of Civil, Environmental and Mechanical Engineering, University of Trento, Italy
• Kishcha Pavel Telaviv University, Telaviv, Israel
• Pierre-Denis Plisnier
• Don Pierson, Uppsala University, Sweden
• Merja Pulkkanen, SYKE, Helsinki, Finland
• Antti Raike, SYKE, Helsinki, Finland
• Michela Rogora, CNR Institute for Water Research, Verbania, Italy
• Geoffrey Schladow, UC-Davis Tahoe Environmental Research Center, USA
• Eugene Silow, Irkutsk State University, Russia
• Lewis Sitoki, Department of Earth Environmental Science and Technology, Technical University of Kenya, Nairobi
• Evangelos Spyrakos, Biological and Environmental Science, University of Stirling, Scotland UK
• Wim Thiery, Department of Earth and Environmental Sciences, KU Leuven, Belgium
• Piet Verburg, NIWA, New Zealand
• Gesa Weyhenmeyer, Department of Ecology and Genetics, Uppsala University, Sweden
• Caroline Wynne, Environmental Protection Agency, Ireland
• Eva Mendl, Hungarian Meteorological Service, Hungary

References

Carrea, L., Embury, O. and Merchant, C. J. (2015) Datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifiers and lake-centre co-ordinates. Geoscience Data Journal, 2(2). pp. 83-97. ISSN 2049-6060 doi:10.1002/gdj3.32

Embury, O., Merchant, C. J. and Corlett G.K. (2012) A reprocessing for climate of sea surface temperature from the along-track scanning radiometers: Initial validation, accounting for skin and diurnal variability effects. Remote Sensing of Environment, 116. pp. 62-78. ISSN 0034-4257 doi:10.1016/j.rse.2011.02.028

Hondzo, M., You, J., Taylor, J., Bartlet, G., & Voller, V. R. (2022). Measurement and scaling of lake surface skin temperatures. Geophysical Research Letters, 49, e2021GL093226. https://doi.org/10.1029/2021GL093226

Saunders, P.M. (1967) The temperature at the ocean-air interface. Journal of the Atmospheric Science, 24. pp. 269-273. doi: 0.1175/1520-0469(1967)024<0269:TTATOA>2.0.CO;2

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