Methodology for prediction of corn yield using remote sensing satellite data in Central Mexico

Résumé 0

The main goal of agricultural crop management in any country is to guarantee food resources for its population. The heterogeneity of corn-growing conditions in many countries, especially in Mexico makes accurate predictions of yield ahead of harvest time difficult. Such predictions are needed by the government to estimate, ahead of time, the amount of corn required to be imported to meet the expected domestic shortfall. In this paper, therefore, a methodology for the estimation of corn yield ahead of harvest time is developed for the conditions of intensive production systems in central Mexico. The method is based on the multi-temporal analysis of NOAA-AVHRR satellite images, and uses normalized difference vegetation indices (NDVIs), Degree-Days (DDs) and Leaf Area Indices (LAIs) to predict corn occurrence and yield. Results of the application of the methodology to successfully identify sites with corn, and to predict corn yield in Central Mexico, are presented and discussed.