Digitalization of Crop Production for Transition to Climate-optimized Agriculture Using Spring Wheat in East Kazakhstan as an Example

Abstract

This paper presents the results of testing certain remote sensing indices (vegetation health index, temperature index, normalized drought difference index, and others). Multitemporal data of nine indices from Sentinel-2 and MODIS satellites for the vegetation period over 5 years were used to simulate the spring wheat yield of the Experimental farm of oilseeds. Results of spring wheat yield modeling have shown that the range of mean absolute error (MAE) values was 3.2 to 3.7, and fit index (d) values were 0.78 to 0.86. It was found that the best model R² = 0.53 based on NDWI, VSDI, and NDVI indices can serve as the most effective predictor for monitoring and forecasting spring wheat yields in eastern Kazakhstan.