Monitoring Soybean Condition and Predicting Yield Using MODIS Vegetation Index 250-m during Critical Stages: A Case of Study in Iowa and Minnesota.

Monitoring crop condition and predicting yield on regional and national scales is becoming increasingly important in developing countries and has sustained importance for government agencies, private industry, and researchers under current weather and climate change conditions. In recent years, crop yields forecasting has become more challenging since anomalous weather conditions (e.g., droughts, heat waves, freezes, and floods) have been observed in major United States crop-producing regions.  Changes in weather patterns magnify the importance for the development of robust science-based tools that allow government and federal agencies to identify, measure, and monitor the effects of climate variability and extreme weather events on crop condition and yield during the growing season. 

This study based its analysis on soybean (Glycine max, L. Merr) yield formation and critical stages during the growing season that can be used to monitor soybean condition and detect yield variability.  The main objective of this study was to evaluate data retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Index 250 meter 16 day composite product (MOD13Q1) to detect critical stages and to monitor soybean condition and predict final yield.  Soybean critical stages were detected using the temporal behavior of reflectance from individual bands during the growing season.  Results indicated that soybean yield models develop for specific locations can be used to detect soybean yield variability without environment adjustment.  This technique offers a rapid way to detect variability of soybean final yield at agricultural statistical district using MODIS 250-m products.