dataesr/scientific-paragraphs-categorization huggingface dataset:
The black current measurement of each image, which was integrated into the raw data headers, was also taken into account. Some of the lines acquired in the north-south direction have been contaminated by the sun glint, which were removed from the raw data as follows. A pixel-by-pixel analysis was carried out to check whether the saturation due to sun glint did not exceed 16 adjacent spectral bands. If not, a spectral interpolation was performed on saturated zones by an Akima-spline interpolator  [54]. The pixels exceeding the threshold value of 16 adjacent saturated bands were substituted by the nearest neighbor (among the 4 above, left, right, and bottom) for which the spectral Euclidean distance to the pixel considered on the portion of the unsaturated spectrum was weakest. Remote Sens. 2021, 13, x FOR PEER REVIEW 5 of 18 lator  [54].
In Situ Data Water quality parameter measurements were performed during the flight for 10 sampling stations (Figure  2 ). Chlorophyll (Chl) concentration data were acquired using the multi-parameter data probe HYDROLAB DS5 equipped with a fluorometric probe. Samples were collected to measure suspended particulate matter (SPM) and colored dissolved organic matter (CDOM). The water samples were acquired at 50 cm beneath the water surface, and they were filtered and weighted to calculate the SPM concentrations  [55]. The absorbance was measured in the laboratory using a spectrometer equipped with a doublebeam monochromator to calculate the CDOM absorption from an excitation range of 220-600 nm at a 0. 2 nm resolution (UV 1800 Shimadzu)  [55]. Chlorophyll concentration, suspended particulate matter, and colored dissolved organic matter for each station are given in Table  1.
In Situ Data Water quality parameter measurements were performed during the flight for 10 sampling stations (Figure  2 ). Chlorophyll (Chl) concentration data were acquired using the multiparameter data probe HYDROLAB DS5 equipped with a fluorometric probe. Samples were collected to measure suspended particulate matter (SPM) and colored dissolved organic matter (CDOM). The water samples were acquired at 50 cm beneath the water surface, and they were filtered and weighted to calculate the SPM concentrations  [55]. The absorbance was measured in the laboratory using a spectrometer equipped with a double-beam monochromator to calculate the CDOM absorption from an excitation range of 220-600 nm at a 0. 2 nm resolution (UV 1800 Shimadzu)  [55]. Chlorophyll concentration, suspended particulate matter, and colored dissolved organic matter for each station are given in Table  1.
The SILIOS images were further used to validate the bottom species composition retrieved from the inversion of satellite data. In parallel to underwater images acquisition, a visual observation using a diving mask was carried out to check the consistency of this approach. Underwater in situ images were acquired on 17 July 2019 in five stations (Figure  2 ) across the study area to characterize the benthic diversity and the associated representative spectra of each class of bottom species. Note that the sampling stations used for acquiring these underwater bottom data are not the same as the water quality sampling stations. This is because priority was given to cover the spatial bottom variability of the entire lagoon. The multispectral camera developed by the SILIOS company (http://www. silios.
To account for the variation in the illumination caused by the different times of acquisition between each sampling station, the training area was chosen within the images independently for each station. The proportion of each bottom species class within each image was calculated, and the median value over all the images was calculated for each bottom type. The SILIOS images were further used to validate the bottom species composition retrieved from the inversion of satellite data. In parallel to underwater images acquisition, a visual observation using a diving mask was carried out to check the consistency of this approach. Macrophyte species data have recently been manually collected in the study area and reported by RNNC  [50]. The macrophytes data include abundances of overlapping vegetation that was removed by changing the proportion by making them sum to one so that a fair comparison would be possible with the inversion from simulated datasets.
The proportion of sediments was also excluded in this comparison because they were not collected by RNNC. The bottom reflectances for the four benthic classes considered in the study were measured using a portable ASD Handheld-2 device working at a spectral resolution of 1 Macrophyte species data have recently been manually collected in the study area and reported by RNNC  [50]. The macrophytes data include abundances of overlapping vegetation that was removed by changing the proportion by making them sum to one so that a fair comparison would be possible with the inversion from simulated datasets. The proportion of sediments was also excluded in this comparison because they were not collected by RNNC. The bottom reflectances for the four benthic classes considered in the study were measured using a portable ASD Handheld-2 device working at a spectral resolution of 1 nm in the range between 350 and 1000 nm.
The reflectance spectra showed that seagrass and algae-class spectra exhibited a significant increase beyond 700 nm, which is typical of vegetation-like spectra (Figure  4 ). The spectral shape of Sediment-class reflectance showed an increasing trend with wavelength across the visible range. Unfortunately, no Lidar data  [56, 57]  were available on the Camargue site  [58]. Bathymetry data were provided by the RNNC. They are based on the merging of data measured over several field campaigns between 1999 and 2013. Due to the shallow depth of the Vaccarès lagoon (average water depth of about 1 m at a water level of 0 m NGF (General Leveling of France))  [58] , measurements were carried out using a ruler to determine the water depth, and the corresponding water depth was calculated with a correction of the water level.
Satellite Sensor Features The BIODIVERSITY-like sensor will provide a spatial resolution of 8 m and a total of 53 bands between 413 and 990 nm, including 26 bands in the visible range, and a fullwidth at half-maximum (FWHM) of 10 nm is expected for each band. Unfortunately, no Lidar data  [56, 57]  were available on the Camargue site  [58]. Bathymetry data were provided by the RNNC. They are based on the merging of data measured over several field campaigns between 1999 and 2013. Due to the shallow depth of the Vaccarès lagoon (average water depth of about 1 m at a water level of 0 m NGF (General Leveling of France))  [58] , measurements were carried out using a ruler to determine the water depth, and the corresponding water depth was calculated with a correction of the water level. The raster file of 8 m resolution was obtained by using methods of triangulation and interpolation between the measurements (using the free and open-source cross-platform Qgis, version 3.
Simulation of Satellite Images from Airborne Hyperspectral Data The atmospherically corrected hyperspectral airborne data provide the surface water reflectance. Such airborne-derived water reflectance data were used as input of the simulation. The contribution of the atmosphere from the surface level to the satellite level was then added using the MODTRAN radiative transfer model  [60]  to obtain the top-ofatmosphere radiance. The spatial and spectral resolution of the resulting image was then degraded to match with the satellite sensor's specifications (BIODIVERSITY), in addition to introducing the noise caused by the sensor itself and inter-band calibration errors. The spectral response function usually used for a hyperspectral sensor is a rectangular function because spectral filters are not employed to decompose the light at various bands, as a diffraction grating is typically used.
The optimization is driven by a nonlinear curve-fitting in a least-squares sense using the "lsqcurvefit" function in MATLAB with given bounds for each parameter. The inversion results are the optimized values of Chl, SPM, CDOM, and z, and the bottom abundances (a i ) of Sediments, Zosters, Green algae, and Red algae for each pixel. When the inversion is achieved, the spatial distributions of the retrieved parameters are then obtained.
Validation The parameters estimated from the satellite images were compared with the in situ sampling data as validation. The fractional abundance of the bottom composition was validated using the SILIOS camera underwater images. The performance of the estimation from the satellite data was quantified using the root-mean-square error (RMSE) and relative error (RE). The coefficient of determination (R 2 ) was used to quantify the performance of the estimation of the fractional abundance of different classes estimated from the satellite data in comparison with SILIOS ground-truth data. Finally, the bottom composition retrieved from real hyperspectral images acquired by DESIS was compared with the in situ camera measurements. The validation was operated using all the available reference values.
A significant correlation between the datasets was observed, as depicted in Figure  7a  (R 2 value of 0. 77). image; the data were thus not linked to any spatial resolution. The fractional abundance of Sediments derived from the simulated satellite images varied between 0 and 26%, while the same varied between 0 and 16% in underwater camera-based estimations. Similarly, the fractional abundance of Zosters derived from the simulated satellite images varied between 0 and 66%, while the same varied between 0 and 84% in underwater camera-based estimations. For Green algae, the ranges of variation were 0-55% and 0-78%, respectively. For Red algae, the ranges of variation were 19-50% and 8-49%, respectively. A significant correlation between the datasets was observed, as depicted in Figure  7a  (R 2 value of 0.
The fact that the SNR of BIODIVER-SITY is higher than that of Sentinel-2 will lead to a lower influence of the noise on the retrieved parameters. The present study showed that the future BIODIVERSITY sensor will be able to estimate the water depth comparable with an RMSE of 0. 28 m, RE value of 14. 11%, and consistent benthic fractional abundance (0. 77 with underwater images and 0. 8 with measurements of the macrophytes abundance that were measured by SNPN). To compare the estimation of the bio-optical parameters retrieved from BIODIVERSITY simulations with those retrieved from real hyperspectral satellite images, the inversion methodology was applied to DESIS real data for the same location. Two DESIS images were processed. One was acquired on 4 September 2019 and the other was acquired on 2 February 2020.
An R 2 value of 0. 68 was obtained. However, the same comparison made using the BIODIVERSITY-like simulated image yielded an R 2 value of 0. 80 (Figure  7 ), which highlights again the advantage of using a BIODIVERSITY-like sensor for the benthic habitat mapping in inland shallow waters. Besides, a high standard deviation was obtained for the DESIS-derived macrophyte data in comparison with the in situ measurements, which was lower in the case of the BIODIVERSITY-like simulated image (Figure  7 ). The water depth validation was operated using the bathymetric reference corrected from the water level. The raster file was resized for comparison with the water depth obtained from DESIS (30 m resolution). Such a resizing was operated using a 3. 75x3. 75-pixel window followed by a downsampling in order to obtain a 30 m resolution raster file.
The water depth patterns were observed to be different from those observed using either the BIODIVERSITY-like sensor (Figure  5a ) or the in situ ground truth data (Figure  5b ). This is likely due to the lower spatial resolution of the DESIS sensor (30 m) in contrast to the BIODIVERSITY-like sensor (8 m). Black pixels in the southern part of the DESIS scene were not processed, because they were covered by a very small cloud. The RMSE between the DESIS-derived water depth and the in situ water depth was 0. 38 m, and the RE was 17%. The BIODIVERSITY-like sensor-retrieved water depth (Figure  5 ) was observed to have a lower RMSE (0. 28 m) and RE (14. 11%) than those of the DESIS retrieval, thus revealing the great interest and potential of a satellite sensor, showing similar features as the BIODIVERSITY sensor.
An R 2 value of 0. 68 was obtained. However, the same comparison made using the BIODIVERSITY-like simulated image yielded an R 2 value of 0. 80 (Figure  7 ), which highlights again the advantage of using a BIODIVERSITY-like sensor for the benthic habitat mapping in inland shallow waters. Besides, a high standard deviation was obtained for the DESIS-derived macrophyte data in comparison with the in situ measurements, which was lower in the case of the BIODIVERSITY-like simulated image (Figure  7 ). In Figure  10 , it seems that the retrieval of the fractional abundance from DESIS data yielded a higher number of samples that were classified either as 0% or 100% for a given class as compared to BIODIVERSITY data. This indicates a systematic misclassification due to the low spatial resolution of DESIS (30 m) compared with the BIODIVERSITY resolution (8 m).
Compared to the validation operated with the BIODIVERSTIY sensor (Figure  7b ), the abundances of these two classes were better estimated using the BIODIVERSITY sensor. This was probably due to the higher resolution of BIODIVERSITY compared to DESIS. This study was carried out for an inland area; however, it should be noted that our results cannot be generalized to all inland waters. For example, in the current study, the waters were very shallow (<2 m), which allows the depth and benthic abundance to be satisfactorily estimated. The retrievals would probably be less for darker waters. Finally, the summertime period was selected for acquiring the data to optimize the detection of the benthic habitat.
It was demonstrated that the BIODIVERSITY-like sensor configuration will be beneficial in comparison with existing sensors like DESIS for estimating the water depth and This study was carried out for an inland area; however, it should be noted that our results cannot be generalized to all inland waters. For example, in the current study, the waters were very shallow (<2 m), which allows the depth and benthic abundance to be satisfactorily estimated. The retrievals would probably be less for darker waters. Finally, the summertime period was selected for acquiring the data to optimize the detection of the benthic habitat.
18 Figure 5. 185 Figure 5. (a) Retrieved water depth from the simulated BIODIVERSITY datasets; (b) water depth measured in situ using a sounder provided by RNC institute; (c) west-east distribution of water depth along a transect at 43. 54° latitude, (d) north-south water depth along a transect at 4. 57° longitude. The two transects are identified on Figure 5a by white lines.
A larger tropospheric NO 2 decrease of about 47 % is found from SAOZ observations between 2019 and 2020 at both studied stations (see Fig. 6 ). Prunet et al. (2020)  found P4 31 Oct to 15 Dec Second lockdown, where schools opened but universities still closed. Some activities are allowed, including some nonessential stores opened with strong restrictions. Some restrictions, such as travel of <1 km maximum, are relaxed at the end of November. an even larger decrease in NO 2 values, varying from 52 % to 86 %, during the lockdown in a 120 km region around Paris using yearly 2019-2020 TROPOMI data and the city-scale NO 2 plume mass method. It should be noted that the SAOZ data sets have shown a long-term negative trend since 2011. Font et al. (2019)  have used in situ data to study the impact of policy initiatives in different megacities.
6±15. 7 % at Paris and 45. 6±11. 8 % at Guyancourt. If the tropospheric median column of NO 2 in 2019 had been used as a reference for comparison, slightly higher declines would have been obtained within ±1σ :56. 7 ± 9. 1 % and 52. 6±14. 5 % at Paris and Guyancourt, respectively. Choosing other reference years would obviously yield different results, e. g. , a slightly lower value at Paris (55±10. 7 %) and an even higher value at Guyancourt (58. 9±12. 5 %) when using the year 2018 as a reference (Fig. 7 ). Moreover, choosing earlier years as a reference would pose the problem of NO 2 variability factors associated with both the lockdown and the long-term NO 2 reductions. This confirms the advantage of our method that calculates the reference from a decadal database and corrects for the long-term trend.
Meteorological conditions and vegetation We used data from the ERA-Interim database  (Dee et al. , 2011)  to characterize the meteorological conditions of the selected study areas (Fig. A. 2, Supplementary information). The dataset covers the 2005-2015 period, which was the latest available when the study was performed. Rainfall shows the typical seasonal pattern of the Southern Amazonian region  (Marengo, 2005) , with a wet season from October to May and a dry season (with a cumulative rainfall below 100 mm/month;  Nobre et al. , 2016)  from June to September. July is the driest month, with a minimum of about 25 mm/month, while the first months of the year are the wettest, attaining values up to 300 mm/month. Net radiation time series show strong seasonal fluctuations in counter phase with precipitation patterns.
A generic water balance can be written as  (Sokolov and Chapman, 1974) : P = ET + R + D + ΔW (1) In this work, we investigate the relationship between vegetation cover and water balance. We compare the outputs of three different water partitioning models: the Global Land Evaporation Amsterdam Model (GLEAM) (see section 2. 3), the software HYDRUS 1-D (section 2. 4) and the Soil-Vegetation-Water (SOVEWA) model, developed in this work as a tool with intermediate characteristics between the previous two (section 2. 5). All three models simulate hydrological flows on a daily scale based on earth observation data but are based on different modelling assumptions and have different levels of flexibility for the user. A comprehensive assessment of the influence of vegetation on the water balance requires an explicit description of how plant biomass dynamics and soil water dynamics interact.
Gleam GLEAM  (Martens et al. , 2017; Miralles et al. , 2011)  is a set of algorithms using land cover and other satellite measures to evaluate the different components of evapotranspiration (transpiration, interception, evaporation from bare soil and water bodies, and snow sublimation), and satellite soil moisture data as a constraint on potential evaporation rates. The model is composed of four separate modules to estimate (i) potential evapotranspiration (ETp), based on temperature and net radiation data through Priestley-Taylor's equation  (Priestley and Taylor, 1972) ; (ii) interception, with Gash's analytical model  (Gash, 1979) ; (iii) evaporation; and (iv) actual ET, estimated from ETp through a multiplicative stress factor ranging from zero to one and accounting for rootzone and vegetation water content.
HYDRUS-1D Among the existing hydrological models  (Devia et al. , 2015; Sood and Smakhtin, 2015) , we selected HYDRUS-1D  ( Šimůnek and Hopmans, 2009; Šimůnek and Van Genuchten, 2008). The freely accessible version of this software includes a finite-element model for the simulation of water, heat and solute movements through a mono-dimensional medium. The model is able to disaggregate ET into its distinct components, as well as to describe the dynamics of soil water content and water uptake by vegetation  (Feddes et al. , 1978). The main modelling assumptions of HYDRUS-1D are the following. Vertical water movement into the soil is described via Richard's equation  (Richards, 1931). Surface runoff includes both runoff from soil saturation and from excess of rainfall intensity. ETp is estimated with Penman-Monteith's equation  (Kaelbling et al.
, 2014). Another possible reason of discrepancy among the results of the three models is the choice of the dataset used to run the simulations. While for HYDRUS-1D and SOVEWA we used a single, self-consistent dataset (the ERA-Interim reanalysis), GLEAM bases its simulations on a broader dataset, including but not limited to the ERA-Interim database  (Martens et al. , 2017). Due to the spatiotemporal variability of evapotranspiration processes, analyses at the mesoscale are considered more suitable to investigate the complex interactions between vegetation dynamics and the water cycle compared to studies conducted at the large geographic scale  (D'Almeida et al. , 2007). Hydrological models with different spatial (local, mesoscale or global) or temporal (daily, seasonal, annual or multiannual) resolution can thus provide significantly different results  (D'Almeida et al.
X-ray diffraction analysis The mineralogy of the soils and parent material was characterized by powder X-ray diffraction (XRD). Samples were finely ground using a McCrone Micronizing Mill (McCrone Scientific Ltd, London, UK) and homogeneously packed into powder sample holders. The samples were analyzed on a Bruker D8 Advance diffractometer using Cu Kα 1,2 radiation and a high-resolution energy-dispersive 1D detector (Bruker AXS GmbH, Karlsruhe, Germany). The data were collected in Bragg-Brentano geometry from 4°to 70°2θ with 0. 02°2θ steps and 10 s acquisition time per step. For the determination of "amorphous" matter contents, X-ray diffractograms were also collected after mixing powdered samples with 33% w/w crystalline corundum powder (Al 2 O 3 ) as an internal standard. The diffractograms were analyzed with Rietveld quantitative-phase analysis using TOPAS Version 5 (Bruker DIFFRAC.
Phosphorus K-edge bulk-XANES spectroscopy Bulk Phosphorus K-edge XANES spectroscopic measurements of all soil samples were carried out at two different synchrotron facilities -Stanford Synchrotron Radiation Lightsource (SSRL) at SLAC National Accelerator Laboratory in California, United States and Berlin Electron Storage Ring Society for Synchrotron Radiation (BESSY II) at Helmholtz-Zentrum Berlin (HZB), Germany. At beamline 14-3 of the SSRL, incident beam energy was selected using a Si(1 1 1) double-crystal monochromator, and storage ring current was 495-500 mA in top-up mode. P K-edge XANES spectra were measured in fluorescence mode using a Vortex Si-drift solid-state detector (Canberra) in a helium atmosphere from 2130 eV to 2200 eV in steps of 0. 2 eV at room temperature; sample preparation and data collection were conducted as described in Massey (2019).
First, two-dimensional micro-XRF elemental maps (ranging from 106 × 112 µm 2 to 300 × 300 µm 2 ) were collected above the P K-edge by raster scanning the sample with respect to the X-ray beam with a step size of 0. 7-5 µm, depending on beam geometry. Afterwards, finer micro-XRF maps were collected from interesting map regions. On selected points of interest, P K-edge micro-XANES spectra were collected from 2130 to 2200 eV with 0. 2-2 eV energy steps. All measurements were performed by collecting the fluorescence signal, using a Si-drift detector. To optimize the discrimination of the various XRF line contributions, the XRF spectra from ESRF and Soleil were batch fitted at each map pixel using the PyMCA software  (Solé et al. 2007). At the SSRL the software Sam Webb's SMAK (Sam's Microanalysis Toolkit;  Webb, 2011)  was used for fitting and image processing.