dataesr/scientific-paragraphs-categorization huggingface dataset:
Study area The original objective of this data set was to obtain long-term measurements of French lakes which are monitored under the Water Framework Directive. Initially this comprised a total of 475 lakes of surface area larger than 0. 5 km 2 in metropolitan France, including 64 natural lakes, 328 reservoirs, 43 ponds, 34 gravel pit lakes and 6 quarry lakes. However, the final data set contains data for 442 lakes, the rest having been excluded because of insufficient availability of data (Fig. 1 ). The complete list of water bodies with geographical coordinates is included in the file 01_lake_data. txt of the data set. The geographical and morphometric data were extracted from the French lake database (PLAN_DEAU) maintained by the AFB-Irstea consortium for R&D on Water Bodies Hydroecology at Aix-en-Provence (France).
In situ data for quality assessment Satellite-based water temperature estimations were compared to continuous in situ measurements of water temperature at different depths at five French water bodies (Table 1, Fig. 1 ). The in situ measurements were provided by the ONEMA and are part of a French continuous monitoring network of lake water temperature. Water temperature was measured through chains of HOBO Water Temp Pro V2/U22-001 thermistors installed on a buoy at the deepest part of the water body. The thermistors have a precision of ±0. 2 • C between 0 and 50 • C, and a resolution of 0. 02 • C at 25 • C. Temperatures were measured at depths between 0. 5 m and the bottom at intervals of 1 to 4 m, depending on the depth (measurements were more spaced in the hypolimnion). Measurement frequency varied from once every 15 min to once every hour, depending on the water body and time period.
Water In addition to in situ data, meteorological data were gathered for the five French water bodies from the SAFRAN reanalysis data  (Quintana-Seguí et al. , 2008; Vidal et al. , 2010) , available for the study period at the nearest grid point at a daily resolution. The following variables were extracted: air temperature, specific humidity, wind speed, liquid precipitation, solid precipitation, downwelling longwave radiation and incoming solar radiation. An adiabatic correction of -0. 0065 • C m -1 was applied to air temperature data to account for the difference in altitudes between the measurement point and the reservoir of Bimont. Additionally, we calculated solar elevation and extraterrestrial solar radiation following  Lenoble (1993). We calculated the daily clearness index as k t = R s /R e , (1) where R s is the daily solar radiation at the Earth surface and R e is the extraterrestrial daily solar radiation through a plane parallel to the surface.
Deriving skin temperature from Landsat imagery The thermal infrared band of both Landsat 5 TM (Thematic Mapper) and Landsat 7 ETM+ (Enhanced Thematic Mapper Plus) instruments were used for deriving skin temperature. Although Landsat 8 was already operative at the beginning of this study, we did not process its thermal infrared images at first, because of stray light artefacts in the thermalband measurements  (Montanaro et al. , 2014). An algorithm has recently been implemented that largely solves this problem  (Gerace and Montanaro, 2017)  and we expect to include Landsat 8 data in future versions of the data set. The TIR band (band 6) of Landsat 5 TM and Landsat 7 ETM+ sensors measured emitted radiation at wavelengths of 10. 40-12. 50 µm. Both satellites have similar orbits, with a delay of 8 days, and they fly over France at 10:00-10:30 UTC.
Two consecutive scenes overlap at 5 % of their surface and are taken with a delay of 23. 92 s  (NASA, 2011). The TM instrument acquired Band 6 data at a 120 m resolution and the ETM+ instrument acquired Band 6 data at a 60 m resolution. TIR Landsat data were extracted from Landsat Climate Data Records (CDR) data. Landsat CDR consist of surface reflectance products from the Landsat archive  (Masek et al. , 2006)  which are freely available from the United States Geological Survey (USGS). Thermal infrared data are provided as top-of-atmosphere (TOA) brightness temperature images resampled via cubic convolution to a pixel size of 30 m. Useful mask layers for clouds, cloud shadows, adjacent clouds, snow, land and water, and quality flags are also provided. The noise-equivalent delta temperature of Landsat measurements is 0.
, 2011)  provided by the ECMWF (European Centre for Medium-Range Weather Forecasts) at a resolution of 0. 25 •. The water vapour content at the time the image was taken was interpolated from data at 06:00 and 12:00 UTC. During the preprocessing phase, we extracted the valid pixels for each water body and image. For each image the preprocessing included the following steps: extraction of the TIR band, masks and metadata from the original hdf files; selection of water pixels using land-water mask and vector file containing the water body outline; suppression of unexploitable pixels by application of mask products associated to the image. For the image processing, brightness temperature in the remaining pixels was converted to surface temperature following the single-channel algorithm proposed by Jiménez-Muñoz and  Sobrino (2003) , adapted to Landsat images  (Jiménez-Muñoz et al.
Warm layer estimations based on field measurements We used data from the 0. 55 m chain installed at the reservoir of Bimont to assess the relation between surface temperature and temperature at 0. 55 m. The instrumentation used was not adapted to measure the cool skin effect, since the surface thermistor measured temperature at a depth of about 1 cm, below the cool skin. However, the Bimont data could be used to measure the warm layer effect. We calculated the warm layer effect at 0. 55 m as follows: T w (0. 55 m) = T 0. 01 m -T 0. 55 m. (3).
2 ). The FORTRAN code and test data were obtained from ftp://ftp1. esrl. noaa. gov/psd3/cruises/NTAS_2009/RHB/ Scientific_analysis/programs/VOCALS2008_programs_ leg1/coare/bulkalg/cor3_0/. In the original parameterisation, the physical properties of water (viscosity, density, thermal expansivity, salinity expansivity and thermal conductivity) are constant and adapted to the seawater case. We modified the algorithm to calculate the physical properties of water as a function of temperature and salinity using expressions provided by  Sharqawy et al. (2010). An R file (Coare_3_0. R) with the implementation of the model is supplied in the Supplement. The simulation of T w for the test data was almost equal for both algorithms. However, the algorithm with physical properties of water depending on temperature and salinity simulated a less intense cool skin effect (with an average difference of 0.
Assessment of the precision and accuracy of satellite-based surface water temperature measurements Consecutive satellite scenes overlap by 5 %. As a consequence, at these overlapping zones repeated temperature measurements are available with just a few seconds of difference. We used the data from 45 water bodies located in the overlapping zones between consecutive scenes to estimate the precision of satellite-based surface water temperatures. The assessment of the accuracy of satellite measurements was made by comparing them to in situ SML temperature and temperature at 0. 5 m for 5 lakes with continuous moni- toring (Table  1 ). We calculated the bias for the five water bodies with continuous measurements as the difference between field measurements and satellite temperature measurements at the nearest pixel (T sat ) as b 0.
50 m = T sat -T 0. 50 m , (8) b sml = T sat -T sml , (9) where b 0. 50 m is the bias in relation to temperature at 0. 50 m and b sml is bias in relation to the SML temperature. The bias depended on the distance of the measurement location to the centre of the nearest pixel, because of spatial variability of the surface temperature (Fig. 3 ). Absolute bias increased for distances greater than 500 m. Most of the satellite-field data pairs separated by more than 500 m correspond to SCR04, the largest water body. Satellite temperatures measured near the field measurement point at this reservoir (near the dam) were warmer than in the open waters. To exclude this distance effect, we kept for further analyses the data pairs separated by less than 400 m. A more robust validation method would be the comparison of corrected satellite radiances to in situ measurements with a thermal radiometer.
Software used We analysed the data using Python 2. 7 and R 3. 2. 0 (R Core  Team, 2015). We used the Python packages NumPy (van der  Walt et al. , 2011) , Matplotlib  (Hunter, 2007)  and  Pandas (McKinney, 2010)  and the R packages MASS  (Venables and Ripley, 2002)  and rLakeAnalyzer  (Winslow et al. , 2016).
For z r = 0. 55 m, the coefficient of correlation between measurements and estimations was 0. 45 and the root mean square error was 0. 22 • C, with the model overestimating the surface thermal gradient. The highest correlation between estimated and measured warm layer effect was found for z r = 2. 50 m, with a correlation coefficient of 0. 78 and RMSE = 0. 32 • C. The discrepancies between the simulation and the measurements can be attributed to several factors: -The model proposed by  Fairall et al. (1996a)  does not allow for negative warm layers (or cool layers). Some of the effects of convective cooling are included in the definition of the cool skin, but they are constrained to the upper millimetres of the water column. -The meteorological data used to force the COARE algorithm were measured at about 10 km from the study site.
We have no measure of the incidence of these artefacts in the database, but we have observed banding in some images (e. g. Fig. 10 ). Also, in Fig. 10  temperature varies spatially in an east-west direction. Such spatial variability may be due to upwellings caused by the wind  (Marti-Cardona et al. , 2008). However, the presence of banding may indicate that memory effects are also present. To decrease the impact of artefacts, we recommend using the median temperature for each satellite image. The advantage of using the median is that it is a robust statistic resistant to the effect of extreme values. To validate the spatial variability of temperature in satellite images, several spatially distributed measurements points in a water body would be necessary. In addition, on 31 May 2003 the Scan Line Corrector of the Landsat 7 satellite failed.
e. above the water) rarely available meteorological data, because of the micromwww. earth-syst-sci-data. net/10/727/2018/ Earth Syst. Sci. Data, 10, 727-743, 2018 eteorological conditions above inland water bodies. For these reasons, we applied the same correction, derived from the results for the reservoir of Bimont, to the five validation sites as a first approach. When more validation data become available, more complex corrections may be envisaged. The average expected cool skin effect at the reservoir of Bimont was T c = -0. 46 • C (Sect. 4. 2) and the average warm layer effect at the time of the satellite overpass was T w (0. 50 m) ≈ 0. 07 • C and T w (sml) = 0. 12 • C (Sect. 5. 1). So, we estimated surface temperature as T * 0. 50 m = T skin -T c -T w (0. 50 m) = T skin + 0. 39 • C, (11) T * sml = T skin -T c -T w (sml) = T skin + 0.
25, p value = 6. 1 × 10 -9 ) between the warm layer effect and solar radiation at 10:00 UTC for clear-sky conditions. However, in situ radiometric skin temperature measurements would be necessary to prove this point. Also, given the high correlation between T a and R s (ρ = 0. 68, p value = 1. 3 × 10 -13 ), Earth Syst. Sci. Data, 10, 727-743, 2018 www. earth-syst-sci-data. net/10/727/2018/ the term R s might partially account for air temperature effects. Although not correlated to emittance, air temperature is well correlated to sky temperature  (Aubinet, 1994) , affecting the longwave radiation balance. The dependence between air temperature and longwave radiation balance is not taken into account by the SC algorithm. In fact, the results of SC algorithms, including the one used here, can be improved by using T a in addition to water vapour as input data  (Qin et al.
Applications Satellite data can be used to demonstrate inter-lake spatial and temporal patterns of surface water temperature. A first analysis of spatial patterns was carried by  Prats and Danis (2015)  using a preliminary version of the database and showing a good correlation between surface temperature and latitude and altitude. Since satellite water temperature measurements are a continuous source of data, they can be used for long-term studies, such as the monitoring of climate change effects. This was demonstrated by Schneider and Hook (2010) and  Schneider et al. (2009)  by calculating surface temperature trends obtained from AVHRR (Advanced Very High Resolution Radiometers) and ATSR (Along-Track Scanning Radiometer) instruments. Such studies have demonstrated warming rates of 0. 11 • C yr -1 for lakes in California and Nevada over the period 1992-2008  (Schneider et al.
Conclusions The LakeSST data set contains skin surface water temperature data for 442 French water bodies for the period 1999-2016 obtained from archives of Landsat 5 and Landsat 7 thermal infrared images. The overall RMSE of the satellitederived temperature measurements is about 1. 2 • C, similar to other applications of satellite images to estimate freshwater surface temperatures. The spatial and temporal coverage of the database make it an ideal resource for studies on the temporal evolution of lake surface temperature and for geographical studies of temperature patterns. Since cool skin and warm layer algorithms have been developed mainly for the sea and have not been extensively tested in a freshwater environment -except in extreme cases such as cooling ponds  (Wesely, 1979; Adams et al.
Spectroradiometer At each grid point, reflectance data were taken from the surface water using a RAMSES model TriOS spectroradiometer, which operates between the ultraviolet and near-infrared spectral bands. It is composed of three sensors (two for radiance and one for irradiance) positioned outside the water in a geometric spatial distribution. The first measures upward water radiance (Lu) at 30 • off nadir; the second, at 30 • with the zenith, measures downward radiance from the sky reflected by the water (Ld); and the third, positioned at 90 • , measures irradiance arriving at a specific point (Ed). To calculate reflectance, the following Equation (1) was used  [15, 22] : Reflectance = (Lu -f × Ld)/Ed, (1) in which f is a factor that depends on the wavelength size and is used to correct a portion of blue from the sky reflected by the water; 0.
Reflectance Calibration Collected SSC were used to calibrate the reflectance data measured by the spectroradiometer. When the best calibration was found, it was used to estimate SSC through the remote-sensing reflectance. To correlate reflectance measured in the field with MODIS reflectance, band homogenization was needed. This was done because remote-sensing data use one band for red and another for near infrared, while the spectroradiometer measures 16 bands for red and 12 for near infrared. The units were also homogenized by multiplying the spectroradiometer reflectance by (10,000•π), because MODIS data are in area units and the measured reflectance is in stereo-radian units  [15].
However, only a few countries provide this data at a regional scale. Regarding the economic size, which is widely used for statistical and policy purposes within the European Union (EU) farms with less than 8 Economic Size Units (ESU) of Standard Gross Margin (SGM) are considered as small farms  (EC, 2011). This threshold was used by  Angelova and Bojnec (2012)  to separate very small and small farms from high-income farms. Since 2010, the economic size of farms is measured as the total Standard Output (SO) expressed in euro. Definitions involving use of additional criteria to farm size are more comprehensive, particularly those including indicators of the farm economic output, but data availability is often a limitation  (Ruane, 2016).
Data sources and indicators Data at NUTS-3 level for all European countries except for Germany (NUTS-2 level due to data availability) (Fig. 1 ) on the Utilized Agricultural Area 2  (UAA) and on the Number of Holdings 3 (hereafter Number of Farms, NF) distributed by structural and by economic size classes were extracted from EUROSTAT (Structure of agricultural holdings by NUTS-3 regionshttp://ec. europa. eu/eurostat/en/web/ products-datasets/-/EF_R_NUTS) and from official national databases on agricultural statistics. The NUTS-3 level was selected as it is the most detailed administrative level to which data can be found for all European countries. Data comes from the latest agricultural censuses and farm structure surveys  (2007) (2008) (2009) (2010) , although there are two exceptions for which the data are less up-to-date (Croatia and Austria).
In our study we used farm's SGM instead of economic output, due to data availability at NUTS-3 level. Data on family labour force was also collected as a proxy for the importance of small farms in a region. Previous studies have shown that farms in mountain areas tend to be small (e. g. Pinter and Kirner, 2014; Salvioni et al. , 2014)  and, therefore, number of farms and land area in agriculture in mountain ranges were also collected. Nevertheless, decoupling the analysis from the total land area used for farming of each region can lead to misleading interpretations. Since we are aiming to identify different regional types of small farms and to assess their distribution, we needed to take the regional agricultural system into account. For this purpose, we also included the percentage of utilized agricultural area (pUAA) in each region in our analysis.
Data analysis The analysis of the data was divided into two fundamental steps: a) analysis of the distribution of utilized agricultural area (UAA) and number of farms (NF) by farm size classes, and b) cluster analysis using the data listed in Table  1  to assess the distribution of small farms in Europe at the NUTS-3 scale. All the analysis was conducted in R version 3. 1. 3 (R Development Core Team, 2015) and ArcGIS 10 (ESRI, 2011). The distribution of the UAA and the NF by structural and by economic farm size was analysed through notched box plots  (McGill et al. , 1978) , where the box shows the interquartile range, the whiskers the non-outlier range, and the notch displays the 95% confidence interval surrounding the median. The non-parametric Wilcoxon rank sum test was applied to compare the distribution of the number of small farms and the area covered by these farms, defined by the thresholds considered in our study (5 ha of utilized agricultural area for the structural size and 8 ESU for the economic size).
g. Marini et al. , 2011; Bojnec and Latruffe, 2013; Battaglini et al. , 2014; Niedermayr et al. , 2015) , being unsuitable to be used as proxy variable of the prevalence of small farms at regional level (see Section A1 and Fig. A2  in the Supplementary Material). The clValid package for R  (Brock et al. , 2008)  was used to select the clustering algorithm from five clustering methods: divisive analysis (DIANA), partition around the medoids (PAM), clustering large applications (CLARA), self-organizing tree algorithm (SOTA) and modelbased clustering (MODEL). Dunn index  (Dunn, 1974) , silhouette width  (Rousseeuw, 1987)  and connectivity  (Handl and Knowles, 2005)  were used to assess the compactness, connectedness, and separation of the cluster partitions  (Brock et al. , 2008). The comparison between the performances of the clustering algorithms are presented in Fig.
2a-c. Considering that the connectivity should be minimized (Fig. 2a ), and both the Dunn index (Fig. 2b ) and the silhouette width (Fig. 2c ) should be maximized, the results have showed that partition around the medoids (PAM) was the best solution for our data. After the selection of the clustering method, cluster analysis was implemented using the cluster package for R  (Maechler et al. , 2017) , and the number of clusters was determined through the Gap Statistic  (Tibshirani et al. , 2001)  which were obtained using 100 Monte Carlo samples. Euclidean distance was used as the dissimilarity measure. Gap statistics suggested examining our data based on an 8-cluster solution (Fig. 2d ). Maximum and average dissimilarities, diameter, separation and average silhouette were retained to assess the performance of the cluster partition, and Kruskall-Wallis and Median tests were performed to evaluate intergroup differences.
However, it is possible to associate some aspects of land use, land cover change processes and biophysical constraints which characterize the regions in each class. Our aim was not to capture the diversity, heterogeneity and/or variability in each country, particularly in those where the most extreme situations occur. For this, more detailed national scale analysis is required, and only in some countries the available data makes such an analysis possible. The goal of the presented analysis was to progress beyond the state of the art, using the existing (and limited) datasets at European level. Even considering the above mentioned limitations of the datasets, our applied approach allow to differentiate the Scottish highlands from the lowlands, identify the three main farm structures characterized by  Kostrowicki (1970)  in Poland, individualize the regions subjected to intense afforestation and where the small individual plots remain in Portugal, separate the coastal and mountain or Apennine areas in Italy, and differentiate the Southern and Alpine areas from the rest of the regions in France, which appear rather homogenous in terms of small farms.
Acknowledgements This work was supported by the  SALSA  project  (Small farms, small food businesses and sustainable food and nutrition security ) (Project  ID :  677 , 363 ) funded under  H2020-EU. 3. 2. -Societal Challenges -Food security, sustainable agriculture and forestry, marine, maritime and inland water research and the bioeconomy; and by  FEDER Funds through the Operational Programme for Competitiveness Factors -COMPETE and National Funds through FCT -Foundation for Science and Technology. Helen Sooväli-Sepping  was funded by the  Estonian Ministry of Education  target-financed project IUT3-2 Culturescapes in transformation: towards an integrated theory of meaning making.
Land surface models (LSMs) are widely used to study the continental part of the water cycle. However, even though their accuracy is increasing, inherent model uncertainties can not be avoided. In the meantime, remotely sensed observations of the continental water cycle variables such as soil moisture, lakes and river elevations are more frequent and accurate. Therefore, those two different types of information can be combined, using data assimilation techniques to reduce a model's uncertainties in its state variables or/and in its input parameters. The objective of this study is to present a data assimilation platform that assimilates into the large-scale ISBA-CTRIP LSM a punctual river discharge product, derived from ENVISAT nadir altimeter water elevation measurements and rating curves, over the whole Amazon basin.
For all the DA experiments, the observation errors are those described in Sect. 3. 1. 2, and the model errors are those presented in Sect. 3. 2. 3. Moreover, each experiment in Table 3 lasts 365 assimilation cycles of 1 day (so 1 year of assimilation) from 1 January to 31 December 2009. We chose this period as it overlaps with another altimetry mission (namely JASON-2), and future works may include comparison of the two datasets' contribution. The numerous ensemble ISBA-CTRIP simulations were realized with the CALMIP high-performance computation platform (https: //www. calmip. univ-toulouse. fr/spip/) with the EOS supercomputer. In the SE1-direct experiment, ENVISAT discharges are assimilated to correct the initial surface reservoir storage in TRIP (and inherently TRIP simulated discharges).