Estimating Midwestern Maize Grain Yield From Crop Biophysical Parameters Using Remote Sensing.

Assessment of maize growing conditions and accurate maize yield predictions are important issues for food prices, food security and crucial decisions affecting agricultural policy and trade. Remote sensing has made important contributions to monitoring crops and estimating final yield (FY) over regional levels. One limitation, regarding the retrieval of useful information for crop yield prediction, is the lack of understanding of how crops change according to developmental stage or crop dynamics in order to evaluate potential capabilities and limitations of satellite data. A better understanding of how crop yield is formed and which crop biophysical parameter (CBP) is mostly involved in determining yield should improve the accuracy of agricultural crop monitoring and enhance FY estimates.

This study based its analysis on maize yield formation, a key CBP, and optimum developmental stages during the growing season that can be used to monitor and detect variability of maize grain FY. The main objective of this study was to detect variability of maize grain yield using estimates of green leaf area index (GLAI) obtained from vegetation indices using data retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Index 250 meter 16 day composite (MOD13Q1) during the critical stages. Estimates of GLAI obtained during critical stages showed a strong correlation with maize grain FY reported by the US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) over selected Midwestern states. The approach presented in this study provides a robust technique for early FY estimation because it is based on a key CBP at the optimum development stage closely related with maize FY. This technique offers a rapid way to detect variability of FY at county, district, and state levels using MODIS 250-m products.