A Regional Crops Forecasting Model Integrating Satellite Remote Sensing with Localized Epic Model.

Airborne and satellite remote sensing data adds tremendous value in assessing crops phenology, green biomass and yield forecasting. Also, satellite based real/near- real time data used for monitoring growth rates, water and nutrient status, and crop responses to biotic and abiotic stress conditions could assist efficient crop management decisions.  The objectives of the study were to develop a regional crop forecasting model for winter wheat in the Texas Panhandle integrating satellite remote sensing data with the localized EPIC crop forecasting model. The Normalized Difference Vegetation Index (NDVI) pixel-level precision data from MODIS sensor (Terra & Aqua satellites) and Landsat 7 (ETM+) spanning 15 years (2000-2015) for the Texas Panhandle region (~64,562 sq. km covering 26 counties) were used for this research and Regional Model development efforts. As part of the study, a sophisticated Java based Application Programming Interfaces (API) was developed for processing massive amount of satellite data. The software is capable of doing complex image processing including Supervised Classification taking a minimal training data set. This tool is also loaded with the capabilities for pixel level geophysical positioning and area computation. In this study, the simulated wheat green biomass information by the EPIC model has been recalibrated and mapped to real-time satellite and ground based NDVI data for predicting total crops yield. The results suggest that the new hybrid and integrated model can predict both the total Acreage and total Grain Yield (Bushel) for the winter wheat crop with 70% - 80% accuracy except any anomalous weather related contingency.