RF-CLASS：A remote-sensing-based lfood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making

摘要

Floods often cause signiifcant crop loss in the United States. Timely and objective information on lfood-related crop loss, such as lfooded acreage and degree of crop damage, is very important for crop monitoring and risk management in ag-ricultural and disaster-related decision-making at many concerned agencies. Currently concerned agencies mostly rely on ifeld surveys to obtain crop loss information and compensate farmers’ loss claim. Such methods are expensive, labor intensive, and time consumptive, especialy for a large lfood that affects a large geographic area. The results from such methods suffer from inaccuracy, subjectiveness, untimeliness, and lack of reproducibility. Recent studies have demonstrated that Earth observation (EO) data could be used in post-lfood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. However, there is no operational decision support system, which employs such EO-based data and algorithms for operational lfood-related crop decision-making. This paper describes the development of an EO-based lfood crop loss assessment cyber-service system, RF-CLASS, for supporting lfood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability speciifcations 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 automaticaly fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationaly produce a set of lfood-related products from EO data, such as lfood frequency, lfooded acreage, and degree of crop damage, for supporting decision-making in lfood statistics and lfood crop insurance policy. The system leverages recent advances in the remote sensing-based lfood 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 prototypical system has automaticaly generated the lfood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Evaluation of system by the end-user agencies indicates that signiifcant improvement on lfood-related crop decision-making has been achieved with the system.