This data set contains sea surface temperature (SST) data on a monthly 1 degree grid from the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua spacecraft. The data were produced by Remote Sensing Systems in support of the CMIP5 (Coupled Model Intercomparison Project Phase 5) under the World Climate Research Program (WCRP). Along with this dataset, two additional ancillary data files are included in the same directory which contain the number of observations and standard error co-located on the same 1 degree grids. AMSR-E, a passive-microwave radiometer launched on the Aqua platform on 4 May 2002, was provided by the National Space Development Agency (NASDA) of Japan to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including SST, wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields. For more information, see ftp://podaac.jpl.nasa.gov/OceanTemperature/amsre/L3/sst_1deg_1mo/docs/tosTechNote_AMSRE_L3_v7_200206-201012.pdf

Reach-Scale Floodplain Manning’s Roughness Dataset for the Conterminous United States Derived from Remote Sensing and Machine Learning Gabriel Barinas1,2, Stephen Good1,2, Samuel Rivera1,3 1Water Resources Graduate Program, Oregon State University, Corvallis OR, USA 2Department of Biological and Ecological Engineering, Oregon State University, Corvallis OR, USA 3School of Civil and Construction Engineering, Oregon State University, Corvallis OR, USA Correspondence to: Gabriel Barinas (barinasg@oregonstate.edu)

Floodplain roughness, quantified through Manning’s coefficient n, is a critical parameter in hydrological models for predicting flood dynamics and managing water resources. Traditional methods to determine n rely on generalized land cover types and often fail to capture the spatial and structural variability of floodplains, resulting in limited understanding of floodplain roughness variation at regional scales. This study integrates high-resolution remotely sensed canopy height and biomass data from NASA’s Global Ecosystem Dynamics Investigation with other spatially distributed data to map Manning’s roughness at reach scales across the conterminous United States. After evaluation of six machine learning models, the best performing approach (Random Forest) was trained on 4,927 roughness estimates from 804 sites and applied to estimate n at 17.8 million reaches within the National Hydrography Database (NHDPlus HR). These n estimates have an R² of 0.51, a root mean squared error of 0.084, and a mean absolute percentage error of 122, capturing spatial variability in floodplain roughness that traditional static methods fail to represent. We find the sparsely vegetated southwest US region exhibits the lowest mean roughness, while the Appalachian region and parts of the southeast US exhibit moderate to high mean values due to denser and more varied floodplain vegetation. Canopy height and biomass were identified as influential non-linear predictors of n, highlighting the importance of vegetation structure on floodplain roughness. This integration of remote sensing data with machine learning models provides spatially distributed estimates of Manning’s n that elucidate patterns in floodplain roughness variability from reach to continental scales. The dataset and companion code are openly available here.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Remote Sensing. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Remote Sensing. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

These are quarter degree datasets that contain the percent of nine land use classes in each quarter degree cell covering the Southeastern United States. Classes are designed to be compatible with the classes in the Land-Use Harmonization 2 dataset (LUH2) (Chini et al. 2021). This dataset includes a new 'actively-managed forest' class that represents pine plantations that are being actively managed, using forest thinning as a proxy for active management. Thins are identified based on the Landsat analysis outlined in Thomas et al. (2021), but applied in Google Earth Engine to the entire Southeastern U.S. in three year increments from 1987-2019. Note that the aggregate area of active management should be interpreted as the total number of pixels within a 0.25 degree cell that were identified as active management at some point during the time period, but may not have been actively managed the entire time period. The other 8 LUH-compatible classes were generated by aggregating the 2016 release of the National Land Cover Dataset (NLCD) (Homer et al. 2020, Jin et al. 2019) for the most recent corresponding year, based on the reclassification scheme described in the readme file.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Environmental Remote Sensing. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Environmental Remote Sensing. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during four flights aboard a DOE B-200 aircraft over Nevada, Arizona, and New Mexico, U.S., from 2007-10-01 to 2007-10-04. A focus of this data collection was the USDA Jornada Experimental Range (Jornada) in southern New Mexico. To complement the programs of ground measurements, JORNEX (JORNada EXperiment) began in 1995 to collect remotely sensed data from aircraft and satellite platforms to provide spatial and temporal data on physical and biological states of the Jornada rangeland. JORNEX uses remote sensing techniques to study arid rangeland and the responses of vegetation to changing hydrologic fluxes and atmospheric driving forces. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.

This dataset shows the amount of land (percent area) in the southeastern United States where both clear cuts and forest thins occurred at the same location but on different dates. This type of forest management is characteristic of industrial forest plantations for loblolly pine (Pinus taeda) in the region. Clear cuts and thins were identified according to the algorithm published in Thomas et al. (2021), which was applied to 30 m Landsat multitemporal data (all images, with cloud masks applied) for every three years from 1987-2019 and summarized according to 0.25 degree grid cells as the percent of the total area. Areas are considered to be actively managed plantations if they were classified by the National Landcover Database (NLCD) products (2016 release) as evergreen, mixed forest, or woody wetlands prior to the harvest year and exhibit both of these harvest types. Results should be interpreted such that actively-managed plantations have occurred at that location at some point during the time period, but not necessarily the entire period, depending on land use transitions.
Note that for 1987-2001, the VCT-based NACP North American Forest Dynamics Project: Forest Disturbance and Regrowth Data (Goward et al. 2012) (change year) was used as a predictor variable in place of the Global Forest Change product (Hansen et al. 2013) (loss year).

This data set consists of a subset for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N) of the University of Maryland (UMD) 1-degree Global Land Cover product in ASCII GRID and binary image formats.The UMD 1-degree Global Land Cover product was produced by researchers at the Laboratory for Global Remote Sensing Studies (LGRSS) at UMD. The product is based on Advanced Very High Resolution Radiometer (AVHRR) maximum monthly composites for 1987 of Normalized Difference Vegetation Index (NDVI) values at approximately 8-km resolution, averaged to one-by-one degree resolution. This coarse- resolution data set was used as the basis for a supervised classification of eleven cover types that broadly represent the major biomes of the world. Because of missing values at high latitudes, the Pathfinder AVHRR data set for 1987 for summer monthly NDVI and red reflectance values were used to distinguish the following cover types: tundra, high latitude deciduous forest and woodland, coniferous evergreen forest and woodland.The 1-degree global land cover product is available for download from the Global Land Cover Facility (GLCF)[http://glcf.umiacs.umd.edu/data/landcover/index.shtml] web site. The data are available as a global coverage in both binary and ASCII format. Additional information and references on this data set can be found at the GLCF web site as well as at the LGRSS web site (link provided at the GLCF web site ) and in the readme file found along with the data [ ftp://daac.ornl.gov/data/lba/land_use_change/land_cover_data_1deg/comp/README].

The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP) data set contains monthly 1.0-degree ocean-only estimates of cloud liquid water path (MACLWP_mean), total water path (MACTWP_mean) which includes both cloud and rain water, and monthly climatologies of cloud liquid water path diurnal cycle amplitudes and phases (MACLWP_diurnal). The MACTWP_mean field can also be used as a quality-control screen for the MACLWP_mean field as discussed in Elsaesser et al. (2017), where uncertainty increases as the ratio of cloud to total water path increases. The MAC-LWP algorithm uses as input the Remote Sensing Systems (RSS) Version 7 0.25 degree-resolution retrieval products (produced using the SSM/I, AMSR-E, TMI, AMSR-2, GMI, SSMIS, and WindSat satellite sensors), and performs a bias correction on all input RSS cloud water path products based on AMSR-E matchups to clear-sky MODIS scenes. The MAC-LWP algorithm ensures that spurious trends and variability in the cloud fields arising from drifting satellite overpass times are mitigated by simultaneously solving for the monthly average cloud and total water paths and monthly-mean diurnal cycles, as discussed in O’Dell et al. (2008). Additional details on the algorithm and data fields can be found in Elsaesser et al. (2017).

Regression analysis for the model Yi = a+bXi+εi, where Yi is the degree of human impact from the control data, Xi is the degree of human impact from the participants.

Regression analysis for the degree of human impact.

Introduction

In 2015, the United Nations established 17 Sustainable Development Goals (SDGs), with Goal 7 focusing on ensuring access to affordable, reliable, and sustainable modern energy for all by 2030. By 2022, approximately 760 million people, or 1 in 11 globally still lacked electricity access according to Tracking SDG7 :The Energy Progress Report 2022, posing significant challenges to achieving this goal. Traditional survey methods for estimating the proportion of people with electricity access are often costly, infrequently updated, and hindered by the need for interpolation of historical data.

To address these challenges, this dataset employs a nighttime light remote sensing estimation framework that integrates DMSP-CCNL and NPP/VIIRS data with GlobPOP population data. This approach produces a global 0.1-degree grid and national-scale electricity access index (EAI) maps from 1992 to 2022.

The framework results' correlation coefficient (R) with World Bank survey data from 1992 to 2022 is 0.87, and the RMSE is 15.4, demonstrating its reliability at the national level. By effectively capturing geospatial changes, this dataset supports SDG 7.1.1 monitoring and offers valuable insights for policymakers to address electricity access disparities and promote sustainable energy transitions.

Data Description

1. This dataset consists of 0.1-degree grid Electricity Access Index (EAI) data in GeoTIFF format, where each pixel value represents the proportion of the population with access to electricity within that area.

Example Filename: EAI_0dot1_Deg_WGS84_F32_1992

• Field 1: EAI (Proportion of people with access to electricity)
• Field 2&3: Spatial resolution is 0.1 degree
• Field 4: Coordinate system is WGS84
• Field 5: Data type is F32 (Float32)
• Field 6: Year "1992"

2. Aggregated EAI data at the national scale is provided in both Shapefile and CSV formats:

• Table Filename: EAI_Level_0_1992_2022.csv

  • Fields include:

    • SOC (Country code)
    • Name (Country name)
    • National EAI data from 1992 to 2022
• Shape Filename: EAI_Level_0_1992_2022.shp
  • • Boundary data sourced from GADM (Database of Global Administrative Areas)

3. The pixel-level (30 arc-seconds) Electricity Accessed Population Density is provided in GeoTIFF format, as identified through nighttime light (NTL) data.

Example Filename: Elec_PopDen_WGS84_30arc_F32_1992

• Field 1 & 2: Population Density with access to electricity (per km^2)
• Field 3: Coordinate system is WGS84
• Field 4: Spatial resolution is 30 arc-seconds
• Field 5: Data type is F32 (Float32)
• Field 6: Year "1992"

If you encounter any issues, please contact us via email at liu.luling.k2@s.mail.nagoya-u.ac.jp.

More Information

The source codes are publicly available at GitHub: https://github.com/lulingliu/EAI.

This dataset contains information on the Surface Soil Moisture (SM) content derived from satellite observations in the microwave domain.

Dataset paper (public preprint)

A description of this dataset, including the methodology and validation results, is available at:

Preimesberger, W., Stradiotti, P., and Dorigo, W.: ESA CCI Soil Moisture GAPFILLED: An independent global gap-free satellite climate data record with uncertainty estimates, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-610, in review, 2025.

Abstract

ESA CCI Soil Moisture is a multi-satellite climate data record that consists of harmonized, daily observations coming from 19 satellites (as of v09.1) operating in the microwave domain. The wealth of satellite information, particularly over the last decade, facilitates the creation of a data record with the highest possible data consistency and coverage.
However, data gaps are still found in the record. This is particularly notable in earlier periods when a limited number of satellites were in operation, but can also arise from various retrieval issues, such as frozen soils, dense vegetation, and radio frequency interference (RFI). These data gaps present a challenge for many users, as they have the potential to obscure relevant events within a study area or are incompatible with (machine learning) software that often relies on gap-free inputs.
Since the requirement of a gap-free ESA CCI SM product was identified, various studies have demonstrated the suitability of different statistical methods to achieve this goal. A fundamental feature of such gap-filling method is to rely only on the original observational record, without need for ancillary variable or model-based information. Due to the intrinsic challenge, there was until present no global, long-term univariate gap-filled product available. In this version of the record, data gaps due to missing satellite overpasses and invalid measurements are filled using the Discrete Cosine Transform (DCT) Penalized Least Squares (PLS) algorithm (Garcia, 2010). A linear interpolation is applied over periods of (potentially) frozen soils with little to no variability in (frozen) soil moisture content. Uncertainty estimates are based on models calibrated in experiments to fill satellite-like gaps introduced to GLDAS Noah reanalysis soil moisture (Rodell et al., 2004), and consider the gap size and local vegetation conditions as parameters that affect the gapfilling performance.

Summary

• Gap-filled global estimates of volumetric surface soil moisture from 1991-2023 at 0.25° sampling
• Fields of application (partial): climate variability and change, land-atmosphere interactions, global biogeochemical cycles and ecology, hydrological and land surface modelling, drought applications, and meteorology
• Method: Modified version of DCT-PLS (Garcia, 2010) interpolation/smoothing algorithm, linear interpolation over periods of frozen soils. Uncertainty estimates are provided for all data points.
• More information: See Preimesberger et al. (2025) and https://doi.org/10.5281/zenodo.8320869" target="_blank" rel="noopener">ESA CCI SM Algorithm Theoretical Baseline Document [Chapter 7.2.9] (Dorigo et al., 2023)

Programmatic Download

You can use command line tools such as wget or curl to download (and extract) data for multiple years. The following command will download and extract the complete data set to the local directory ~/Download on Linux or macOS systems.

#!/bin/bash

# Set download directory
DOWNLOAD_DIR=~/Downloads

base_url="https://researchdata.tuwien.at/records/3fcxr-cde10/files"

# Loop through years 1991 to 2023 and download & extract data
for year in {1991..2023}; do
  echo "Downloading $year.zip..."
  wget -q -P "$DOWNLOAD_DIR" "$base_url/$year.zip"
  unzip -o "$DOWNLOAD_DIR/$year.zip" -d $DOWNLOAD_DIR
  rm "$DOWNLOAD_DIR/$year.zip"
done

Data details

The dataset provides global daily estimates for the 1991-2023 period at 0.25° (~25 km) horizontal grid resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD), month (MM) in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name has the following convention:

ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED_GAPFILLED-YYYYMMDD000000-fv09.1r1.nc

Data Variables

Each netCDF file contains 3 coordinate variables (WGS84 longitude, latitude and time stamp), as well as the following data variables:

• sm: (float) The Soil Moisture variable reflects estimates of daily average volumetric soil moisture content (m3/m3) in the soil surface layer (~0-5 cm) over a whole grid cell (0.25 degree).
• sm_uncertainty: (float) The Soil Moisture Uncertainty variable reflects the uncertainty (random error) of the original satellite observations and of the predictions used to fill observation data gaps.
• sm_anomaly: Soil moisture anomalies (reference period 1991-2020) derived from the gap-filled values (`sm`)
• sm_smoothed: Contains DCT-PLS predictions used to fill data gaps in the original soil moisture field. These values are also provided for cases where an observation was initially available (compare `gapmask`). In this case, they provided a smoothed version of the original data.
• gapmask: (0 | 1) Indicates grid cells where a satellite observation is available (1), and where the interpolated (smoothed) values are used instead (0) in the 'sm' field.
• frozenmask: (0 | 1) Indicates grid cells where ERA5 soil temperature is <0 °C. In this case, a linear interpolation over time is applied.

Additional information for each variable is given in the netCDF attributes.

Version Changelog

Changes in v9.1r1 (previous version was v09.1):

• This version uses a novel uncertainty estimation scheme as described in Preimesberger et al. (2025).

Software to open netCDF files

These data can be read by any software that supports Climate and Forecast (CF) conform metadata standards for netCDF files, such as:

• https://github.com/pydata/xarray" target="_blank" rel="noopener">Xarray (python)
• https://unidata.github.io/netcdf4-python/" target="_blank" rel="noopener">netCDF4 (python)
• https://github.com/TUW-GEO/esa_cci_sm">esa_cci_sm (python)
• Similar tools exists for other programming languages (Matlab, R, etc.)
• Software packages and GIS tools can open netCDF files, e.g. CDO, NCO, QGIS, ArCGIS
• You can also use the GUI software Panoply to view the contents of each file

References

• Preimesberger, W., Stradiotti, P., and Dorigo, W.: ESA CCI Soil Moisture GAPFILLED: An independent global gap-free satellite climate data record with uncertainty estimates, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-610, in review, 2025.
• Dorigo, W., Preimesberger, W., Stradiotti, P., Kidd, R., van der Schalie, R., van der Vliet, M., Rodriguez-Fernandez, N., Madelon, R., & Baghdadi, N. (2023). ESA Climate Change Initiative Plus - Soil Moisture Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version 08.1 (version 1.1). Zenodo. https://doi.org/10.5281/zenodo.8320869
• Garcia, D., 2010. Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics & Data Analysis, 54(4), pp.1167-1178. Available at: https://doi.org/10.1016/j.csda.2009.09.020
• Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data Assimilation System, Bulletin of the American Meteorological Society, 85, 381 – 394, https://doi.org/10.1175/BAMS-85-3-381, 2004.

Related Records

The following records are all part of the Soil Moisture Climate Data Records from satellites community

A Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.09-degree grid at Remote Sensing Systems. This product uses optimal interpolation (OI) from both microwave (MW) sensors including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W1 satellite, and WindSat operates on the Coriolis satellite, and infrared (IR) sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platform and the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi-NPP satellite. The through-cloud capabilities of microwave radiometers provide a valuable picture of global sea surface temperature (SST) while infrared radiometers (i.e., MODIS) have a higher spatial resolution. This analysis does not use any in situ SST data such as drifting buoy SST. Comparing with previous version 4.0 dataset, the version 5.0 has made the updates in several areas, including the diurnal warming model, the sensor-specific error statistics (SSES) for each microwave sensor, the sensor correlation model, and the quality mask. Version Description:

This is a gridded degree of curing (DOC) dataset over Australia based on vegetation optical depth (VOD) and normalised difference vegetation index (NDVI) that can reasonably reproduce groundbased observations in space and time.

The gridded DOC data is produced via estimation models using the VOD dataset from AMSR-E (0.1 degree; 8-day) and NDVI dataset from MODIS Terra MOD09A1 (0.005 degree; 8-day). The estimation models are derived from the calibration and evaluation of VOD and NDVI datset with field observed DOC over Australia. Matlab was used for the calibration and evaluation of these models.

There are 2 variations based on the following estimation models: DOC_M1 = 145.57-260.82(NDVI)+137.19(VOD)(NDVI) DOC_M2 = 48.70+147.60(VOD)-259.95(VOD)(NDVI) The domain covered is Australia with a 0.05 degree spatial resolution. Temporal resolution is 8-day composites from 04/07/2002 to 26/06/2011 .

These experiments were executed by Waisin Chaivaranont of the ARC Centre of Excellence for Climate System Science (ARCCSS) research program "The role of land surface forcing and feedbacks for regional climate".

Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) can be a valuable proxy for photosynthesis. TROPOspheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel-5P mission enables significant improvements in measuring SIF, but the short temporal coverage of the data records has limited its applications in long-term studies. This dataset uses machine learning to reconstruct TROPOMI SIF (RTSIF) for 2001-2020 with a spatial resolution of 0.05° and a temporal resolution of 8 days. Our machine learning model has high accuracy on the training and testing data (R2 = 0.907, regression slope = 1.001). The RTSIF dataset is in good agreement with the original TROPOMI SIF, and its accuracy is further validated against tower-based SIF. The RTSIF dataset is also compared with other satellite-derived SIF (GOME-2 SIF and OCO-2 SIF). Comparing RTSIF with Gross Primary Production (GPP) illustrates the potential of RTSIF for estimating carbon fluxes. We anticipate that this new dataset will be valuable in assessing long-term terrestrial photosynthesis and constraining the global carbon budget and associated water fluxes.

The Remote Sensing Systems (RSS) Monthly 1-degree Merged Wind Climatology netCDF dataset V7R01 provides global gridded wind speed data over ocean areas. This dataset contains a 12-month climatology using January 1, 1988 to March 31, 2016 data, monthly anomaly maps, a trend map with associated global and tropical wind speed time series, and a time-latitude plot. The wind climatology dataset is a merged ocean product constructed using the version-7 (V7) passive microwave geophysical ocean products made publicly available by Remote Sensing Systems (www.remss.com). Ocean wind measurements used to create this dataset were acquired from the following satellite microwave radiometers: SSM/I F08 through F15, SSMIS F16 and F17, AMSR-E, AMSR-2, and WindSat. The radiometers used to construct this dataset were inter-calibrated at the brightness temperature level, while the V7 ocean products were produced using a consistent processing methodology across sensors.

Monthly mean sea surface temperature (in degree-C at 9km resolution) derived from the PATHFINDER sensor (Satellite remote sensing Ocean color data): Sea surface temperature is the temperature of the water close to the sea surface. SST is a standard product from satellite-based thermal infra-red sensors, and optical sensors complemented with infrared bands.

Consistency of response to degree of human impact.

The Remote-sensing-based Flood Crop Loss Assessment Service System (RF-CLASS) is an Earth Observation (EO) based flood crop loss assessment cyber-service system operated by the Center for Spatial Information Science and Systems (CSISS), George Mason University. RF-CLASS supports flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems, particularly the National Aeronautics and Space Administration (NASA) Earth Observing System Data and Information System (EOSDIS), for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data, such as flood frequency, flooded acreage, and degree of crop damage, for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment, the near-real-time availability of EO data, the service-oriented architecture, geospatial interoperability standards, and the standard-based geospatial web service technology. The prototype system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Resources in this dataset:Resource Title: Website Pointer to RF-CLASS: Remote-sensing-based Flood Crop Loss Assessment Service System. File Name: Web Page, url: https://dss.csiss.gmu.edu/RFCLASS/ Basic Layers: Global Cover: CDL: CDL 2012, Crop Mask; Boundaries,:Counties, States, ASD; Water Layers: Rivers, Lakes; Road Layers: Freeway System (National), Major Highways (Regional); Flood Data Layers: flood_frequency. Products: Type: Flood; Crop Fraction; Crop Loss; Flood Events; NDVI; VCI; RVCI; RMVCI; MVCI. (Period/Year/Date)