dataesr/scientific-paragraphs-categorization huggingface dataset:
• Lake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e. g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions). The dataset is the result of four years of the ESA CCI Lakes consortium joint effort but it has roots in methodological development spanning multiple decades. Each of the thematic lake variable datasets has been derived from observations by different instruments and with different retrieval techniques. Therefore, the procedures used in producing the data are described for each variable separately. In particular, input and ancillary data, the core algorithm(s), outputs, uncertainty estimates and quality indicators are detailed as relevant for each thematic ECV in the dataset.
A detailed description is provided in the ESA CCI Lakes Algorithm Theoretical Basis Document  12. The spatial coverage of the dataset presented in this paper includes 2024 lakes and reservoirs (Fig. 1 ), selected to be a globally representative sample of the largest inland waters, which cover a wide range of ecological settings and characteristics. The lakes included in this collection are deemed suitable for remote sensing methods based on a priori expectations of their morphology. The maximum distance to land  13  has been used to select the target water bodies. Given the variable instrument resolution, the maximum distance to land gives an estimate of how likely a lake (or a portion of it) will be observed and how likely the observed pixels are to (seasonally or occasionally) include land.
Since the spatial resolution of the presented dataset is approximately 1 km × 1 km, only lakes with distance to land greater than 1 km were selected, with the exception of ten lakes that were of particular interest for LWLR which is retrieved with higher resolution instruments. Figure  1  shows the geographical distribution of the lakes. The list of the lake selection is available at the project website https://climate. esa. int/en/projects/lakes/data as a csv file and through a link CEDA which can be accessed through the lake dedicated website at the University of Reading (http://www. laketemp. net/home_CCI). The link is https://gws-access. jasmin. ac. uk/public/cds_c3s_lakes/CCI_LAKES/CCI_LAKE_LIST_v2/LAKE_ LIST_MASK_CCI_v2_UoR_fv1. 0. html.
LWL ancillary data. Polygons of lake contours from the Hydrolake database  14  were used to determine the satellite tracks that cross each of the lake in the database and the measurements that must be considered. A threshold for each lake is also used for filtering outliers: it is based on a priori information on the historical 'speed' (or rate) of water level changes. If compared to the previous LWL value, the instantaneous rate of change overpasses the threshold, the LWL is removed from the time series. At the level of individual altimetry measurements, an 'editing' is also used based on a threshold on the backscatter of the reflected energy (it must be ranged between 7 and 40 dB). When a lake surface is too smooth, it provokes a so-called sigma bloom with very high backscattering and quasi-specular echo which cannot be processed by the waveform re-tracking algorithm.
LWE auxiliary data. Polygons of lake contours from the Hydrolake 14 database are used to determine the region of interest which must be considered for each lake. In few cases, these polygons have to be redrawn in order to fit in a better way with the region of interest. LWE core algorithm. To calculate LWE we use a combination of a lake's water surface extent and water height at different dates, and then establish a relationship between these variables called hypsometry. This calculation has been used in many studies  [21] [22] [23] [24] [25]. LWL historical time series inferred from satellite altimetry are used to determine when the lake was at low, medium and high level. We, then, collect images covering these different periods. The hypsometry is then adjusted ideally using a set of about 10 to 15 pairs of (LWL, LWE) and a simple least square adjustment.
This choice generates many gaps in the time series for extreme values of lake elevation. Lake surface water temperature. The algorithms to generate the LSWTs draw on developments in the ESA funded projects ARCLake, the CCI for Sea Surface Temperature as well as UK Natural Environment Research Council project GloboLakes. The main algorithmic steps are: identification of water-only pixels for valid retrieval; inversion of thermal infrared radiances to LSWT based on physical modelling; estimation of total LSWT uncertainty and uncertainty components; evaluation of confidence level for each pixel; cross-sensor LSWT harmonization. The LSWT dataset for this project has been created utilising only daytime imagery and single-view level-1b data. LSWT input data. The input data are level-1b satellite images, which by definition consist of calibrated, geo-located brightness temperatures (BTs) and radiances.
• Lake emissivity is acquired through a lookup of emissivity derived from the refractive index for a range of wavenumbers, view angles, temperature and wind speed  33, 34. • Fixed error covariance matrices (uncertainty information) are input parameters to the LSWT retrieval scheme. LSWT core algorithm. The LSWT algorithm is based on a per-pixel processing consisting of four main components: the identification of the lake pixels filled with water, a forward model (the radiative transfer model is used), the LSWT retrieval based on optimal estimation and finally the remapping into a regular grid. Water detection (WD) is applied to potential inland water pixels that belong to a lake. WD operates by calculating a score against several metrics, derived from the reflectance channels available. For this reason, the LSWT is obtained only during daytime for this version of the dataset.
The remapping from the L2 data to the fixed level-3 (L3) grid at (1/120)° is a destination-pull algorithm where, given the L3 cell coordinate of the centre, the closest L2 pixels LSWT value is assigned to the L3 cell of the L3U (uncollated) file. The polar orbiting satellite carrying the sensors used for this dataset typically complete 14-15 orbits each day resulting in the same number of L2 or L3U files. The LSWT outputs are collated to produce one file for each 24-hour period, corresponding to day-time observations. Following the GHRSST conventions  41  , the selection of the best observation is done choosing the input cells with the highest quality level and if multiple observations have the same quality level, then the average is computed. Finally, an adjustment due to different sensors has been carried out using as reference the LSWT from AVHRR on MetOpA since the validation in GloboLakes indicated a better agreement throughout the lakes with the in situ data.
X, metric Score definition Thresholds LSWT uncertainty estimate. The standard uncertainty is defined as the standard deviation of the estimated error distribution. Standard uncertainty is evaluated for each pixel at L2 and then propagated into the gridded dataset accounting for the correlation structure of errors between pixels. The overall uncertainty attributed accounts for instrument, retrieval and sampling effects. The instrument part addresses the propagation of error in the satellite observations (the brightness temperatures, BTs) through the retrieval process, using equations appropriate for the type of retrieval used (optimal estimation, see below). The retrieval uncertainty expresses the range of possible LSWTs compatible with the observations even if they were error free, since the intervening atmosphere produces some ambiguity in the relationship between the surface LSWT and the top of atmosphere satellite BTs.
Lake ice cover. LIC input data. The input data for the LIC product were MODIS (Terra and Aqua) level-1b calibrated and geolocated at-aperture radiances (MOD/MYD02QKM 250 m, bands 1-2; MOD/MYD02HKM 500 m, bands: 3-7 Top of Atmosphere Reflectance), Collection 6. 1, obtained from the National Aeronautics and Space Administration (NASA) for the period 2000-2020. The input data streams are summarised in Table  6 , where the time range and the typical local time of observation are reported. LIC auxiliary data. Three auxiliary datasets are used as part of the LIC product generation. The first dataset provides a raster delineation of lakes based on the maximum water extent derived from the land/water mask available from ESA's CCI Land Cover (v4. 0) at 150 m resolution. Two other datasets are used in a preprocessing step, prior to entering the main processing chain, to flag which of the lakes are likely to form or not to form ice.
This step is introduced to narrow down the number of lakes to process globally as a mean to decrease computational costs. The datasets used for this step consist of ERA5 ice depth (thickness) data provided by the ECMWF  31  and ice thickness determined from Canadian Lake Ice Model (CLIMo 42 ) simulations. Ice cover is deemed possible to form on a lake if ice depth is determined to have reached a thickness greater than 0. 001 m on any day from either CLIMo or ERA5 over the period 2000-2020. In the case of discrepancies between ERA5 and CLIMo derived ice cover, and for alpine regions where deep glacial lakes are found (e. g. European Alps, Southern Alps of New Zealand, and the Andes), an additional check is done for these lakes through visual examination of MODIS RGB reflectance images to determine if any of the lakes form Table  5.
Thus, uncertainties are not assessed at a pixel level, but rather from classification error calculated from multiple samples/images. The reference data for validation are collected from the visual interpretation of imagery from a large number of lakes globally and over several ice seasons (freeze-up and break-up periods) by skilled ice analysts. Classification error (expressed as %) derived from the confusion matrix is the metric used to report total uncertainty for each class (ice, water, cloud). Pixels belonging to the same class are simply assigned the same % error value in the uncertainty band provided. Probability of class belonging, which informs about confidence in the classification, is considered as a valuable proxy for the uncertainty associated with machine learning (ML) algorithms.
It is not currently provided in the ESA CCI LIC product v2. 0. 2 but planned in future releases. Lake water leaving reflectance. LWLR input data. The primary input data for LWLR products were level-1b data from the MERIS sensor on Envisat (3 rd reprocessing) and the Ocean and Land Colour Instrument (OLCI) sensor on Sentinel-3A and Sentinel-3B. The data were obtained from ESA for the period 2002-2012 and 2016-2020, respectively. MODIS level-1b data were also acquired from the National Aeronautics and Space Administration (NASA) for the period 2009-2019, to provide overlap with and continuity between MERIS and OLCI observation periods. The input data streams are summarised in Table  7 , where the time range and the typical local time of observation are reported. LWLR auxiliary data. The delineation of water bodies for LWLR was based on a set of polygons describing their maximum water extent at a resolution of 150 m, based on the land/water mask of the ESA CCI Land Cover project v4.
An additional two non-water reflectance signatures, equivalent to OWTs but derived from near shore observations in small lakes, were added to identify the effect of adjacent land on observations over water. The applicability to global lake observations, corresponding to a given subset of OWTs, of each of the algorithms selected for chlorophyll-a (Chla) and turbidity was established using in situ observation data from LIMNADES (Lake Bio-optical Measurements and Matchup Data for Remote Sensing: https://limnades. stir. ac. uk) to tune specific algorithm coefficients, to define their applicable concentration range, and to characterize their respective uncertainty. This meta-dataset combined data from 25 sources from over 200 inland water bodies across the globe. The tuning techniques described by Neil et al.
50 were used to assign Chla and turbidity algorithms to specific OWTs, as shown in Tables  8, 9 , respectively. The same set of Chla and turbidity algorithms are used with both MERIS and OLCI products based on in situ data corresponding to MERIS observations, whereas MODIS algorithms were independently optimized. For each pixel, the algorithm results corresponding to the three OWTs with the highest similarity scores were averaged using the OWT membership score as weighting factor to get the per-pixel weighted blend of each biogeochemical product. This procedure reduces discontinuities in Chla and turbidity maps when selected algorithms are applied near the edge of their applicable range. LWLR output of core algorithm. Outputs of the core LWLR processing chain include the unprojected, atmospherically corrected LWLR for all input satellite wavebands of each sensor, the OWT scores, each biogeochemical algorithm and their OWT-based weighted-blended products and the pixel identification flags.
The latter uncertainty product ignores systematic bias and reflects the uncertainty in LWLR which can be propagated into the Chla and turbidity products (the systematic bias is already removed by individually optimizing the algorithms). The application boundary constraints of the LWLR uncertainty models were determined based on the range of observations in the matchup dataset from LIMNADES. If an uncertainty model could not be established (e. g. in situ data were too sparse) or if the LWLR amplitude is out of the application range, the observation is given with unknown uncertainty. The uncertainty models corresponding to specific biogeochemical algorithm-OWT combinations for Chla and turbidity were expressed as a function of OWT class membership score. Each model was evaluated against the full match-up dataset available for the observation period of the satellite sensor.
This is done to ensure that the uncertainty model captures the reduction of uncertainty with increasing OWT class membership, which is ultimately how the per-pixel uncertainty is generated. A weighted-blending of the uncertainty for the three OWTs with the highest similarity scores was then applied to obtain the final uncertainty of each pixel, using the same weighting as for the blended biogeochemical product  51. LWLR quality indicators and/or data gaps. The associated uncertainty product for each LWLR variable can be used as an approximate indicator of product quality, and it is further noted that the uncertainty product is expressed in absolute percentage values  51. The LWLR product is the result of calibrated at-sensor radiance following removal of atmospheric effects and contaminated pixels over water area, and therefore it contains gaps spatially and temporally resulting from cloud cover, ice cover, land adjacency, sun glint, white cap, satellite coverage and satellite revisiting time, etc.
Data Records The ESA CCI Lakes dataset  52   10. Not all the thematic ECV products are offered for all the 2024 lakes and throughout the full period. All lakes are covered by LSWT, LWLR and LIC, but not LWL and LWE due to the more limited along-track spatial coverage of radar altimeters. The temporal coverage for each is reported in Table  11. In the version v2. 0. 2 of the dataset, each of the variables is present in the files only within the period indicated in the table. The files contain the variables that define the dimensions of longitude, latitude and time. The time variable is also encoded in the filename. The filename follows the CCI data standards v2. 3 and it has the form ESACCI-LAKES-<Processing Level>-<Data Type>-<Product String>-<Indicative Date>-fv<version>. nc where <Processing Level> is L3S for the dataset presented the processing level and indicates super-collated data where observations from multiple instruments and observation times are combined into a common spatio-temporal grid; <Data Type> is LK_PRODUCTS indicating that the file contains lake products; <Product String> is MERGED to indicate that the product come from more than one platform and sensor; <Indicative Date> is the date of observation and it is encoded as YYYYMMDD; <version>: the version of the dataset.
technical Validation All the thematic ECVs of the dataset have been validated against in situ measurements, where possible, according to the CCI Product Validation Plan  54. Validation of the dataset has been carried out through direct comparison between remote sensing products and in situ data and/or other remote sensing datasets for each of thematic variable individually and/or through manual inspection of a selection of lakes. The level of spatio-temporal consistency between thematic variables was investigated separately to ensure the data are mutually compatible. Lake water level validation. Validation of LWL is performed using sets of in situ measurements in different regions. The accuracy of LWL calculated using satellite altimeter is strongly dependent on several conditions: the shape, the size, and the surrounding environment of the lakes.
It also depends on the quality of the on-board instruments that have strongly evolved from the radar altimeters launched in the 1990s. The validation of LWL is essential since the accuracy of LWL calculated using satellite altimetry may differ from one type of lake to another one. Therefore, a large set of in situ water level data was collected to cover most of the different cases (small/large lakes, with or without ice cover in winter, mountain lakes, etc. ). The validation of this variable therefore has to be carried out for lakes with different characteristics. Due to the fact that generally the geodetic reference system used by satellite and in situ measurements are not the same, and not tied, the validation consists in most of the cases in inter-comparison of the water level changes after removing some biases between the time series as reported in a large number of papers during the last 20 to 25 years.
Other studies focused on the validation of satellite altimetry over specific lakes  15, 58, [64] [65] [66] [67] [68]  or on group of lakes in specific environment  19, 55, 59, 69. If in such analysis the in situ data are considered as ground truth, there are still difficulties to directly compared them since the physical properties and the scaling of the measurements are different. Satellite measurements are collected along the track of the satellite with a footprint of several square kilometres, while the in situ measurements are generally taken on the lake shore very often far from the satellite track by several kilometres. Moreover, the reference system of each of the measurements are different, and the uncertainty on the geoid over the lake adds a source of bias between both types of measurement.
In addition, technical issues with gauges, data gaps, human error in collecting the data can easily increase the gauge error level to few centimeters, making them an unreliable source of ground truth for small variations of lake level. The inter-comparison between the two types of dataset is also complex because the in situ measurements are generally daily or monthly average values, while the frequency of the satellite flight over the lake is fully determined by its orbit. The comparison requires then an interpolation, which can also be a source of error, especially when the lake water level variations are abrupt, or in presence of seiche for example. The errors of satellite altimetry over lakes depend on several factors. Depending on the size and shape of lake shore, the altimetry telemetry waveform which is analysed for the calculation of water level over the footprint can be more complex than the usual shape of such signal from the open surface over the oceans, or over very large lakes.
Total number of files 10353 Table  10. Data record information for the gridded (level-3S), multi-sensor, multi-variable ESA CCI Lake product. Lake water extent validation. Validation of water extent is challenging since in situ measurements generally do not exist  25, 70. Field work for surveying the water bodies is limited to walking along the shore with a GPS tracking, or using a boat or kayak to follow the shore but it remains limited to very small number of lakes  71. It is indeed not always possible to walk around lake, since many lakes are not easily accessible. Moreover, in case of shallow water, the exact distance between the boat and the shore, for example 10 m (or more) is equivalent to 2 to up to10 Sentinel pixels. Water extent extraction from satellite observations generates a large amount of uncertainty and once inter-comparisons between radar or optical imageries using different types of methods are done, selection of images to construct the hypsometry curve can be done based on statistical analysis of the dispersion of the coupled variable: LWL/LWE.
An additional way to evaluate the accuracy of the LWE extraction is to compare the lake contour from satellite sensor used with high resolution (HR) and very high resolution (VHR) imageries like those provided by the Pléiades constellation  72. Another problem is the difficulty to precisely define the real contour of a lake, since a lake can be surrounded by wetlands or covered by floating vegetation. In addition, the definition of lake could include the ice cover other than the open free water area. Many tests have been done before we could produce LWE for the CCI dataset. In order to select the appropriate methodology for optical imagery used for the LWE production, we have implemented an inter-comparison process with VHR data using the Pleiades constellation  73. The comparison requested pair of HR and VHR images acquired within a very short time.
Where, the LWE represents two stages of infilling, we observe a large omission belt around the lake shore. This belt corresponds mostly to an increase of the surface of water within one day. This case study allows also evaluating, in the context of infilling reservoir, the part of the 24 h of delay between the two acquisitions. In such case, we observed a large omission belt around the lake shore. This belt in fact corresponds to the increase of the surface of water within one day. So, of course, what is seen as water on the Pleiades image cannot be described as water on the Sentinel-2 image acquired a day before. This is illustrated in Fig. 4. We systematically determine the precision of each time series of LWE calculating the RMSE of the difference between LWE inferred from the polynomial function of the hypsometry of the lake, and LWE directly measured by the satellite image, over the whole dataset of images used to calculate the hypsometry coefficient.
Hypsometry has been used only when the RMSE obtained was lower than 10% of the total average extent of the lake. Most of the time, the RMSE kept within 1 to 2%. The idea is also that, when comparing LWE derived with different methodological /sensors approaches the best water times series would produce the best hypsometric curves and therefore statistical correlation can be used to corroborate the method. An example for lake Kariba is reported in Fig. 5. Lake surface water temperature validation. The validation of the LSWT is based on matchups between satellite and in situ measurements which are collected through personal communication with limnologists and agencies who are willing to share the data for validation purposes. The in situ temperature data have been collected also through the ARCLake project, the GloboLakes project, the EU Surface Temperature for All Corners of Earth (EUSTACE) project and the Copernicus Climate Change Service (C3S) product.