Using Satellite and Field Data with Crop Growth Modeling to Monitor and Estimate Corn Yield in Mexico

Abstract

ABSTRACTThe large‐scale monitoring and estimation of crop yield is essential for food security in Mexico. This study developed and validated a method of monitoring and estimating corn (Zea mays L.) yield by means of satellite and ground‐based data. In autumn–winter 1999 and spring–summer 2000, eight locations under irrigated and nonirrigated conditions in corn valleys of Mexico were localized by Global Positioning Systems (GPS) and were sampled every 15 d. Photosynthetic active radiation (PAR), leaf area index (LAI), crop development stage (DVS), planting dates, and grain yield data were gathered from the field. The normalized difference vegetation index (NDVI) was derived from NOAA‐Advanced Very High Resolution Radiometer (AVHRR) images. A growth model was developed to integrate satellite and ground data. Net primary productivity (NPP) was estimated using PAR and NDVI. Dry weight increase (kg ha−1 d−1) was determined considering NPP and the partitioning factor. Results indicated that the model accounts for 89% of the variability in yields under irrigated conditions and 76% under nonirrigated conditions. The methodology seems advantageous in large‐scale monitoring and assessment of corn yield.