Crop Monitoring System Using MODIS Time-Series Data for Within-Season Prediction of Yield and Production of US Corn and Soybeans

Abstract

In terms of contribution to global food security, this study aimed to build a crop monitoring system for within-season yield prediction of US corn and soybeans by using the Moderate Resolution Imaging Spectroradiometer (time-series data, which consists of three essential core algorithms (crop phenology detection, early crop classification, and crop yield prediction methods)). Within-season predictions for 2018–2022 were then made to evaluate the perfor- mance of the proposed system by comparing it with the United States Department of Agriculture's (USDA's) monthly forecasts and the fixed statistical data. The absolute percentage errors of the proposed system for predicting national-level yield and production were less than 5% for all simulation years as of day of year (DOY) 279. The prediction accuracy as of DOY 247 and DOY 279 were comparable to the USDA's forecasts. The proposed system would enable us to make a comprehensive understanding about overview of US corn and soybean crop condition by visualizing detail spatial pattern of good- or poor harvest regions on a within-season basis.