Forecasting Crop Yields Based on Earth Remote Sensing Methods

Abstract

Observations of the dynamics of crop development using remote sensing data showed that in the spectral characteristics space, each crop species forms a compact cluster (a set of homogeneous photometric points) at a certain time and stage of development. It was found that derivation of images from satellite data by processing according to special algorithms in selected spectral regions allows studying plant productivity, biomass, photosynthesis intensity, and other parameters. In the present work, the authors developed a preliminary version of the algorithm for forecasting crop yield on the example of sunflower, which showed good accuracy. The method allows for early forecasting. Approximation of the dynamic curves corresponding to the values of the seven-day composite NDVI (Normalized Difference Vegetation Index) indices has been proposed using the Gaussian function and the Levenberg-Marquardt algorithm. The use of the approximating function for predicting the annual maximum NDVI on the arable land mask showed the magnitude of the average absolute prediction error depending on the predicted week ranging from 0.67 to 10.7 % per year in the evaluated period, which is acceptable accuracy for seasonal forecasts.

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