dataesr/scientific-paragraphs-categorization huggingface dataset:
Availability of the ADAM Product and Online Calculation Tools for Plotting The ADAM climatologies and calculation toolkit are catalogued in the ESA Earth observation portal database ([70] DOI: 10. 5270/esa-6riial9), providing a metadata description and link to the dataset on the dedicated ADAM portal at  [71]. The ADAM portal offers additional functionalities in addition to simple data access. It includes the documentation related to ADAM, including the Algorithmic Theoretical Basis Document  [37]  and a detailed description of the API and the input/output file formats. The web application enables the display of global maps of the Earth's surface reflectance in predefined spectral bands (either in the seven MODIS channels or in broad spectral domains spanning the 240-4000 nm spectral range) or as false color composite images (depending on user choice relative to these predefined bands), with the possibility to zoom in/zoom out over user-defined regions.
Representative Simulations with ADAM API and Database We present below a few illustrative simulations performed using ADAM (climatology and API) over the main surface types (Figure  3 ). For the vegetation pixels and the soil pixel considered, the reflectance spectra are calculated in an illumination/observation geometry (solar and zenith angles at 30 • ) that is different from the ADAM standard geometry (for which the MODIS-FDS reflectances are seen as red dots in Figure  3c ); the result illustrates both the spectral and the directional extrapolation models. The three "snow" pixels considered in the spectral graph (Figure  3e ) present low reflectance values in the blue, characteristic of a mixture of snow and vegetation/soil. The simulated spectra then results from the calculation of the stepwise model that combines the contributions of the two surface elements (snow and soil/vegetation), fitted to MODIS-FDS normalized reflectances (red dots).
Evaluation Dataset We evaluated ADAM spectro-directional reflectance simulations over vegetation and soil surfaces against multi-spectral/multi-directional reflectance products from the POLDER instrument  [72]. POLDER monitors the Earth's surface in six channels in the visible and near-infrared (490, 565, 670, 765, 865, and 1020 nm) at coarse resolution (~7 km). We used the POLDER BRDF database described by Bréon and Maignan  [32] , which compiled atmospherically corrected (bottom-of-atmosphere) surface reflectances over clear pixels for the year 2008 (with therefore a high number of multi-directional observations-about 170 on average-available over one year for each pixel). We recall that the ADAM database is compiled from atmospherically corrected MODIS observations for the year 2005. The comparison below will therefore be affected by inter-annual variability and differences in the satellite sensor spatial, temporal, angular, and spectral representability.
Methods Using the ADAM product (database and API), we calculated for each pixel the reflectance in the corresponding POLDER wavebands and observation geometries. We used the approach of Kobayashi and Salam  [73]  to decompose the mean square deviation (MSD) between ADAM simulations (x) and POLDER observations (y) into three terms quantifying (i) the square bias (SB, using their notations) between the two datasets, characterized by the square difference of their means (x and y); (ii) their differences relative to their magnitude of fluctuation and characterized by their standard deviation σ (SDSD); and iii) the lack of correlation between them (LCS), which depends on the correlation coefficient R. MSD = (x -y) 2 + σ x -σ y 2 + 2 σ x. σ y (1 -R) where (x -y) 2 = SB, σ x -σ y 2 = SDSD, 2 σ x.
Note that the lesser performances are obtained over the pixels corresponding to deciduous needleleaf forest, grassland (partly explained by the issue of snow coverage discussed earlier), and barren/sparsely vegetated areas. Apart from the modeling approach, the use of a climatological database (ADAM based on MODIS observations from 2005 vs. POLDER data from 2008) also makes a contribution to the model-data discrepancy. This can be seen for some grassland pixels (IGBP 10) where some ADAM simulations exhibit high reflectance values (above 0. 6) that are attributed to snow, which was not present at the time of POLDER monitoring over the pixels considered. The MSD decomposition that is presented in Figure  5  shows that the model-data bias (SB) is usually small for all soil/vegetation classes and for the considered wavebands.
Rationales The Ross-Li-HS BRDF model used in the ADAM API has already shown good performances for representing the directional signatures of soil and vegetation surfaces. This has been evaluated using spaceborne POLDER measurements  [35]. However, there are very few observations at or close to the backscatter direction to validate the BRDF model and, more precisely, the reflectance enhancement as the measurement phase angle gets close to zero (exact backscatter direction). There is typically a factor of two between reflectance in the backscatter direction divided the same away from the The main contributor to the model-data disagreement is LCS which measures at the same time the lack of correlation between the datasets and their respective variability (LCS and SDSD have confounding effects  [74] ).
Evaluation Dataset We used the level-1 measurements (normalized backscatter) of the CALIPSO (Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations) mission  [75]  focused on monitoring the atmospheric aerosol and clouds for 2008. In order to identify the aerosol types, CALIPSO acquired measurements at two wavelengths: 532 and 1064 nm. The analysis of the quality index on the signal saturation indicates that most 1064 nm data are saturated, except over water. We thus only evaluated ADAM using the 532 nm data. The surface reflectance is sufficiently low that most observations of this channel are not saturated, except over snow. It should also be noted that the ADAM database represents the year 2005 and the CALIPSO dataset the year 2008, which will lead to some spread in the comparison due to inter-annual variability.
Discussion While most of the Earth's natural surfaces are largely anisotropic for scattering in the solar spectral range, operational algorithms dedicated to atmospheric correction or characterization of atmospheric properties usually rely on the assumption of Lambertian surfaces  [8] [9] [10] [11] [12] [13] [14] [15] [16] [17]. This assumption may lead to substantial retrieval errors  [18] [19] [20] [21] [22]. To date, there very few Earth observation databases on a global scale that can be used to support the development of novel processing chains accounting for the anisotropy of the Earth's surfaces over a large spectral range. Traditional multi-spectral/multi-directional reflectance datasets [31-33] or reflectance anisotropy parameters  [27]  remain limited to specific broad-bands in the visible to near-infrared range.
To our knowledge, there is only one other database like ADAM capable of simulating hyper-spectral/multi-directional surface For barren surfaces, ADAM product tends to overestimate the backscatter reflectances by a factor of about 1. 5. For other (vegetated) surface types, the CALIPSO reflectances are significantly larger than the ADAM product. The ratio appears to be in the order of two for forested targets (the five first biome types in Figure  6 ), and in the order of 1. 5 for other surface types. Figure  6  shows that the CALIPSO measurements vary less between surface types than what is simulated with ADAM.
Discussion While most of the Earth's natural surfaces are largely anisotropic for scattering in the solar spectral range, operational algorithms dedicated to atmospheric correction or characterization of atmospheric properties usually rely on the assumption of Lambertian surfaces  [8] [9] [10] [11] [12] [13] [14] [15] [16] [17]. This assumption may lead to substantial retrieval errors  [18] [19] [20] [21] [22]. To date, there very few Earth observation databases on a global scale that can be used to support the development of novel processing chains accounting for the anisotropy of the Earth's surfaces over a large spectral range. Traditional multi-spectral/multi-directional reflectance datasets  [31] [32] [33]  or reflectance anisotropy parameters  [27]  remain limited to specific broad-bands in the visible to near-infrared range.
To our knowledge, there is only one other database like ADAM capable of simulating hyper-spectral/multi-directional surface reflectances  [1]. However, compared to ADAM, this atlas only covers the 400-2500 nm spectral range and only applies to land surfaces. The ADAM database contains a monthly mean climatology, which means that ADAM simulations should provide realistic monthly values across a full year, but which may be different to the exact value of the surface reflectance for any given day and time. Such a monthly climatology does not represent high temporal variations of some components of the surface, such as the wind speed over oceans that controls the glint reflectance. Specific temporal windows were considered for the ADAM input datasets (focusing on year 2005 mostly for land surfaces and sea ice and using ocean data during 1999-2009).
Depending on the value of the snow grain size and of the sun zenith angle, the theoretical range of variation of the snow reflectance in the visible (400-700 nm) varies typically from 0. 8 to 1. 1 for viewing at nadir, as seen from in situ measurements  [3]  or space-borne observations  [43]. In the FDS input database however, the surface reflectance in the visible can reach up to 1. 3 for some snow pixels. The impacted pixels are at high latitudes when the sun zenith angle is very large. Such high values are indeed obtained under very oblique illumination angles  [44]  for which our modeling framework relying on ART is not well suited. As the normalization procedure used in the FDS processing chain is also based on the original version of the ART model for snow, we chose to cap reflectance values of snow pixels to 1.
Conclusions ADAM is a centralized climatological database coupled to a calculation toolkit that enables simulating typical and realistic hyperspectral/directional reflectance values over land and water surfaces at a global scale (bottom-of-atmosphere), at a monthly/0. 1 • × 0. 1 • resolution and over the 240-4000 nm spectral range. ADAM aims to support the preparation and exploitation of optical Earth observation missions by characterizing the expected "at sensor" signal-to-noise ratio, as well as the design of operational processing chains for the retrieval of atmospheric parameters (accounting for the surface anisotropy in particular). An evaluation of the ADAM product was undertaken over land pixels only and not over ocean surfaces. The reasons are two-fold. Firstly, the accuracy of spaceborne estimates of surface reflectance is much higher over land than over water because of the lower reflected radiance for the latter.
Secondly, land surface reflectances exhibit smoother temporal variations than ocean reflectances (which are largely dominated by the glint contribution that is dependent on the wind speed variability) and are therefore best suited to be represented by a monthly climatology. The evaluation of ADAM output simulations against the POLDER database includes different contributors to the error budget. The first one is associated with the spectral extrapolation approach; whereas POLDER wavebands are centered at 490, 565, 670, and 865 nm, and mostly constrained by MODIS-FDS reflectances at 469, 555, 645, and 858 nm, there are no MODIS bands close to the POLDER ones at 765 and 1020 nm. The second one concerns inter-annual variations of the reflectance given that we used POLDER data for 2008 while ADAM is representative of 2005 over land.
from http://www. cryoclim. net/cryoclim/subsites/data_portal,and is currently being moved to https://adc. met. no/. The equivalent daily sea ice concentration product is available at: http://www. osi-saf. org/. The reflectance spectra used to generate the EOFs for the spectral model over vegetation and soil surfaces were obtained from the  DLR  spectral archive (http://cocoon. caf. dlr. de/intro_en. html),the ASTER spectral library (http://speclib. jpl. nasa. gov/),and the  USGS database  (http://speclab. cr. usgs. gov/spectral. lib06/). The spectral variation of the imaginary part of the ice refractive index was downloaded from http://refractiveindex. info/?group=CRYSTALS&material=H2O-ice. The water absorption coefficient was obtained at https://omlc. org/spectra/water/data/segelstein81. txt.
Integrative research on water resources requires a wide range of socio-environmental datasets to better understand human-water interactions and inform decision-making. However, in transboundary watersheds, integrating cross-disciplinary and multinational datasets is a daunting task due to the disparity of data sources and the inconsistencies in data format, content, resolution, and language. this paper introduces a socio-environmental geodatabase that transcends political and disciplinary boundaries in the Rio Grande/Río Bravo basin (RGB). The geodatabase aggregates 145 GIS data layers on five main themes: (i) Water & Land Governance, (ii) Hydrology, (iii) Water Use & Hydraulic Infrastructures, (iv) Socio-Economics, and (v) Biophysical Environment. Datasets were primarily collected from public open-access data sources, processed with arcGIS, and documented through the FGCD metadata standard.
Synthesizing a broad range of environmental and social datasets is further critical for in-depth understanding of watershed dynamics  8  and for assessing the sustainability of water allocation  9. In large areas, spatial datasets are also valuable to capture the spatial heterogeneity of local conditions and for better understanding of different development trends across a region  10. However, integrating scattered cross-disciplinary and multinational datasets is challenged by the disparity of data sources, transboundary discontinuity  11  and the inconsistencies in format, content, spatial and temporal resolution, languages  12  , and institutional norms. This can limit the study of transboundary socio-environmental systems, and calls for the development of a harmonized, transboundary, open-access database.
Despite chronically severe water scarcity, human water demand has grown along with an increase in population (current population is estimated at 10. 5 million people  18  ). Furthermore, irrigated agriculture makes up approximately 83. 5% of the total surface and groundwater withdrawals  19, 20  while cultivated areas cover only 3. 5% of the basin  21. Projected climate change and population growth are likely to lead to a growing imbalance between water supply and demand, potentially impacting the sustainability of the basin  22  and resulting in a cascade of negative impacts (e. g. , increased risk of wildfires)  23. To overcome the challenges of data-and information sharing in transboundary basins, several initiatives were launched in recent years. The Transboundary Freshwater Dispute Database gathers global and regional information for the world's 310 international river basins, including the RGB  24.
Examples of topics include population, land-cover, irrigation, rainfall, and dams (http://gis. nacse. org/tfdd/index. php). In the RGB, a basin level database synthesizing hydrologic, administrative, land-use/cover, and water management datasets, was also developed at a finer resolution  25, 26. However, we are not aware of a similar approach to capture the social heterogeneity and complexity across the basin  27. Hence, we compiled a comprehensive socio-environmental geodatabase for the RGB, encompassing geospatial data sets related to Water & Land Governance, Hydrology, Water Use & Hydraulic Infrastructures, Socio-Economics, and Biophysical Environment. An innovative contribution of our geodatabase is to provide open-access to a thorough collection of reliable and well documented, basin-wide geospatial data sets, as well as, to our knowledge, the most extensive spatial picture of multi-scale water governance in the RGB.
The geodatabase expands the availability of information detailing the social and environmental characteristics of the basin and enables an integrated and cross-disciplinary approach to the watershed. Many outputs can be derived from the geodatabase, including maps, spatially explicit integrated models, and statistical analyses (see Usage Notes for additional details). The data synthesis approach presented here is transferable to other transboundary basins, and the geodatabase will help the research, management, and policy-making communities to foster transboundary collaboration.
Methods Dataset preparation. The development of the socio-environmental geodatabase followed four steps: (1)   data identification; (2) collection of raw data; (3) assembling, harmonizing, and geoprocessing of datasets; and (4) organization of the final geospatial data sets into the geodatabase (Fig. 1 ). First, we drew on an interdisciplinary collaboration among social and environmental scientists to identify critical data requirements to support socio-environmental research in the RGB. The environmental anthropologists shared primary data analysis from ethnographic research and developed a qualitative typology of actors that we used to identify water and land governance datasets. We integrated land management as a key component for the study of socio-environmental dynamics in the watershed because land management decisions affect water run-off, stream flow, aquifer recharge, and soil erosion.
The actor typology distinguishes the actors according to their roles in water and land management (water supply, consumption, infrastructure management, environmental protection, recreation) and their spatial scale of action (from local to bi-national level; Supplementary Information 1). Second, we gathered 210 raw datasets from 49 distinct sources totalling around 53 GB. Where possible, and if the spatial resolution was high enough, we prioritized global datasets over national datasets and national datasets over regional datasets to limit data inconsistency and heterogeneity. Most of the raw datasets were collected from national agencies (67%), followed by state agencies (13%), global agencies (2%) and binational agencies (7%). We also collected datasets from joint research projects (5%) and private institutions (6%).
Most of the input datasets were available open-access online. We also contacted public or private institutions to access unpublished information and were granted permission to publish the synthesized data. The original formatting of the raw datasets varied greatly. 62% were vector data (51. 5% shapefiles, 3% KML/KMZ files, 7. 5% feature class), 19% were raster files, and 19% were only available in tabulated format (comma-delimited text or Microsoft Excel files). In terms of language, 73% of the raw datasets were in English and 27% in Spanish. The detailed list of raw datasets used to produce the geospatial output datasets is available in Supplementary Information 2. For each raw dataset, we included a short description, the spatial and temporal dimensions, the format, language, data source, and date of access.
Third, we assembled, harmonized, and geoprocessed the datasets using ESRI's ArcGIS 10. 6 and, where appropriate, the Python programming language (version 2. 7) and the arcpy package for automating the geoprocessing workflow. Geoprocessing operations varied according to the type of raw dataset, and a detailed description of these operations is available in the metadata for each geographic layer. In general, we tried to synthesize scattered state and national raw data into one data layer. Where necessary, we harmonized the information in the datasets (variables, values, units of measurement) and translated Spanish information to English. Geospatial operations included the projection of the final datasets to the most appropriate projected coordinate system that minimized areal distortion for the whole RGB (North America Albers Equal Area Conic), and clipping datasets to the RGB boundary.
Instead of using the hydrological basin boundary, we used the spatial boundaries of the Rio Grande/ Río Bravo socio-environmental system as delineated by Koch et al. 27. For all political jurisdictional boundary information (national, state, or county), given the spatial mismatch with the catchment boundary, we prioritized the use of administrative boundaries over catchment boundaries in locations where they overlap. Fourth, we stored the resulting 145 geographic layers, including 125 vector data and 20 raster data (GRID), in a geodatabase with a total size of 1. 40 GB. We organized the datasets into five main themes: Water & Land Governance, Hydrology, Water Use & Hydraulic Infrastructures, Socio-Economics, and Biophysical Environment.
Metadata. For each individual data layer, we produced a standardized metadata record, following the Federal Geographic Data Committee Content Standard for Digital Geospatial Metadata (FGDC CSDGM). The standard includes seven types of information: Identification Information (basic information about the dataset such as the citation, description, spatial domain, access constraints, etc. ), Data Quality Information, Spatial Data Organization Information, Spatial Reference Information (Geographic Coordinate System Definition), Entity and Attribute Information, Distribution Information, and the Metadata Reference Information. The metadata Fig. 1  Schematic overview of the workflow applied to produce the socio-environmental geodatabase for the Rio Grande/Río Bravo basin. records have been created through the ArcMap graphical user interface and are automatically included with the hosted layer item.
Data Records The socio-environmental geodatabase is freely and publicly available on the OSF repository  28. The collection of datasets can be downloaded as a single file (. gdb) or as individual geospatial output layers (shapefiles or raster files). Here, we describe the geospatial datasets available for the five themes Water & Land Governance, Hydrology, Water Use & Hydraulic Infrastructures, Socio-Economics, and Biophysical Environment. Online-only Table  1  and Tables 1-4 provide a systematic overview of the individual layers, i. e. , the name, description, format and data sources for each theme. A comprehensive list of acronyms is included in Supplementary Information 3.
Political jurisdiction boundaries. The geodatabase provides the boundaries of four political jurisdiction divisions: nation, state, county/municipio, and places (cities, towns and villages). The geodatabase also provides a point dataset of the most populated places in both countries, which includes the main U. S. -MX sister cities along the border. Binational surface water management agencies. The geodatabase gathers 13 vector datasets of binational infrastructure that spatializes the joint operations of the U. S. International Boundary and Water Commission (IBWC) and its Mexican counterpart the Comisión Internacional de Límites y Aguas (CILA), regarding national allocation of water, dam and reservoir operations, river course management (the banks, the berms, the vegetation), water quality, sanitation, and flood control in the border region.
Federal surface water management agencies. This sub-category includes two datasets of federal dams. One gathers all federal dams operated by the Comisión Nacional del Agua (CONAGUA) in Mexico. The other maps all dams owned by a federal agency in the U. S. , including the U. S. Army Corps of Engineers (USACE), the Bureau of Indian Affairs (BIA), the Bureau of Land Management (BLM), the Bureau of Reclamation (USBR), the U. S. Fish and Wildlife Service (USFWS) and the U. S. Forest Service (USFS). State and intrastate surface water management agencies. This sub-category depicts the boundaries of the Offices of the State Engineer (OSE) and their regional offices (divisions and districts) in charge of the administration of water rights and interstate compacts in the U. S. For Mexico, we generated spatial layers representing state-level offices of the federal agency CONAGUA.