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RF-CLASS: Remote-sensing-based Flood Crop Loss Assessment Service System

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.

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FieldValue
Tags
RF-CLASS
Remote-sensing-based Flood Crop Loss Assessment Service System
Modified
2019-08-05
Release Date
2018-12-21
Identifier
9ea68a0e-e7bf-4c08-97cb-b9d912385aae
Spatial / Geographical Coverage Area
POLYGON ((-95.393066704273 49.390718622262, -122.99072295427 49.046272144223, -124.39697295427 47.880743391454, -124.74853545427 42.562524148417, -122.81494170427 37.589572543393, -119.12353545427 33.29533705186, -116.31103545427 32.260816853318, -114.55322295427 32.705654921671, -110.68603545427 31.214370510697, -108.40087920427 31.214370510697, -107.52197295427 31.813788042556, -106.64306670427 31.813788042556, -103.12744170427 29.086579846985, -102.07275420427 29.699190146047, -100.13916045427 28.624713962673, -97.678222954273 25.494524269057, -96.623535454273 28.160807105531, -94.689941704273 29.086579846985, -91.701660454273 29.546383858427, -90.119629204273 28.93285262998, -89.240722954273 30.004105722576, -86.604004204273 30.156213563649, -84.143066704273 29.851764343352, -82.385254204273 28.470304068247, -81.154785454273 25.653084153224, -80.275879204273 25.653084153224, -79.748535454273 26.757059094238, -81.330566704273 30.45972578952, -80.803222954273 31.664294451237, -75.529785454273 35.184287601126, -74.123535454273 39.784622524538, -72.717285454273 40.989576410551, -70.256347954273 41.780872961995, -70.256347954273 43.716860745734, -67.268066704273 44.599596818623, -67.443847954273 47.048918724375, -69.025879204273 47.525858859652, -70.080566704273 46.203916132075, -71.311035454273 45.59226206518, -74.475097954273 45.098086535875, -76.232910454273 44.22289853526, -78.869629204273 43.843774374786, -79.221191704273 42.691859658448, -82.736816704273 41.518177644287, -82.209472954273 43.334503843965, -83.615722954273 46.082124300382, -84.670410454273 46.92901151486, -89.768066704273 47.880743391454))
Publisher
Center for Spatial Information Science and Systems (CSISS), George Mason University
Spatial / Geographical Coverage Location
Contiguous United States (CONUS)
License
U.S. Public Domain
Contact Name
Yu, Eugene Genong
Contact Email
gyu@gmu.edu
Public Access Level
Public
Bureau Code
005:15 - National Agricultural Statistics Service
Extended Metadata: 
FieldValue
Authors
Center for Spatial Information Science and Systems (CSISS), George Mason University
Peer Reviewed
No
Intended Use
RF-CLASS supports flood-related crop statistics and insurance decision-making.
Funding Sources
National Aeronautics and Space Administration
NNX12AQ31G
National Aeronautics and Space Administration
NNX14AP91G
Primary Article

Di, L., Yu, E. G., Kang, L., Shrestha, R., & BAI, Y.- qi. (2017). RF-CLASS: A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making. Journal of Integrative Agriculture, 16(2), 408-423. https://doi.org/10.1016/s2095-3119(16)61499-5

AgID: 10113/5629478
Method Citation

Di, L., Yu, E. G., Kang, L., Shrestha, R., & BAI, Y.- qi. (2017). RF-CLASS: A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making. Journal of Integrative Agriculture, 16(2), 408-423. https://doi.org/10.1016/s2095-3119(16)61499-5

AgID: 10113/5629478
Product Type
Dataset
Related Content
A Remote-sensing-based Flood Crop Loss Assessment Service System (RF-CLASS) for Supporting USDA Crop Statistics and Insurance Decision Making [NASA Applied Sciences Program]
FieldValue
ISO Topic(s)
climatologyMeteorologyAtmosphere
environment
farming
geoscientificInformation
imageryBaseMapsEarthCover
inlandWaters
location
planningCadastre
State or Territory
United States Of America
National Agricultural Library Thesaurus Term
crop losses
statistics
decision making
floods
United States
acreage
crop damage
monitoring
risk management
surveys
farmers
labor
decision support systems
algorithms
National Aeronautics and Space Administration
information systems
Internet
crop insurance
issues and policy
remote sensing
Ag Data Commons Keywords
  • Maps & Multimedia
  • Agroecosystems & Environment
  • Water
  • Hydrology
  • Agroecosystems & Environment
  • Water
  • Agroecosystems & Environment
  • Weather and Climate
  • Agroecosystems & Environment
  • Management
  • Agroecosystems & Environment
  • Socioeconomics
  • Agricultural Economics
  • Agroecosystems & Environment
  • Plants & Crops

Suggested Citations:

(original article) Di, L., Yu, E. G., Kang, L., Shrestha, R., & BAI, Y.- qi. (2017). RF-CLASS: A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making. Journal of Integrative Agriculture, 16(2), 408-423. https://doi.org/10.1016/s2095-3119(16)61499-5

(dataset) Center for Spatial Information Science and Systems (CSISS), George Mason University (2018). RF-CLASS: Remote-sensing-based Flood Crop Loss Assessment Service System. Center for Spatial Information Science and Systems (CSISS), George Mason University. https://data.nal.usda.gov/dataset/rf-class-remote-sensing-based-flood-crop-loss-assessment-service-system. Accessed 2023-04-01.