Temporal monitoring of corn (Zea mays L.) yield using grami model, satellite imagery, and climate data in a semi-arid area
Keywords:cor, yield estimate, GRAMI, climate data, satellite image, Landsat 8
Corn yield estimation constitutes a critical issue in agricultural management and food supply, especially in demographic pressure and climate change contexts. In light of precision and smart agriculture, this study aims to develop a diagnostic approach to temporally monitor and estimate corn yields using GRAMI (a model for simulating the growth and yield of grain crops), satellite images, and climate data at regional scale. The GRAMI-corn model is controlled by vegetation indices (VIs) derived from Landsat 8 satellite images and calibrated by climate data. The model performed and validated using information collected from twenty-five cornfields in a semiarid region in Ravansar, Iran. The average of under- or over-estimate yields was 919 kg ha−1. In addition, the absolute error between the average observed and estimated yield values for the region was 19.21% for the 2016 corn season. The results using the GRAMI-corn model showed an acceptable agreement with field measurements.