Flooding can destroy crop and cropland on a large scale. The USDA National Agricultural Statistics Services (NASS) has the mandate to report crop loss after all flood events in the U.S. and the USDA Risk Management Agency (RMA) manages crop insurance policy and the after-flood compensation. Both agencies need early recovery assessment and in-depth assessment of crop loss, with RMA especially needs the in-depth assessment. Currently both agencies mostly rely on surveys from NASS field investigators to obtain crop loss information for decision making. The approach has several severe limitations, including (1) sparse samplings (very poor spatial resolution) due to limited number of field investigators, (2) subjective results due to the uneven knowledge of field investigators, (3) slow response due to the length sample data collection and analysis process, and (4) difficulty in data integration due to data-interpolation and data-interoperability problems. Studies have shown that satellite-based Earth Observations (EO) are potentially the most efficient data source for completing the after-flood crop loss assessment for a large geographic area timely and accurately. In this project, we propose to develop, evaluate, and eventually operate a remote-sensing-based flood crop loss assessment service system (RF-CLASS) for supporting USDA crop statistics and insurance decision making. EO data from multiple NASA and non-NASA sensors will be used to produce two important products: affected crop acreage and degree of damage. Open specifications will be adopted in developing the system. The project will leverage the recent advances made by the project team and others in the remote sensing-based flood monitoring and assessment, the near-real-time availability of EO data, the standard-based geospatial web service technology, and the service-oriental architecture and geospatial interoperability. NASA EOSDIS data will be the major data source for this project. In the feasibility stage of this project, we will prototype the RF-CLASS system for automatically generating the flood crop loss products and demonstrate the feasibility of using such products to improve the decision making in the two USDA agencies. The decision stage of the project will refine the RF-CLASS implementation, make it operational ready, and transfer the system to two USDA agencies for operational use. Anticipated outcomes for the project are: (1) an operational RF-CLASS that will significantly improve the objectiveness, timeliness, and accuracy of flood-related decision making at USDA/NASS and USDA/RMA; (2) improved timeliness, accuracy, and spatial resolution of crop loss acreage and degree of damage products; (3) reduced turn-around time from NASA satellite and in-situ observations to crop flood loss information and related decision making; and (4) improved scalability and performance of decision supporting through cloud-computing-ready geospatial Web service. This feasibility study is directly relevant to the priority topic of post-disaster assessment for flood.