Forecasting grain yields of winter crops in Kherson oblast using satellite-based vegetation indices

Abstract

The work is devoted to the development of an early grain yield prediction model for winter cereals (wheat and barley) for Kherson oblast by the values of spatial indices (normalized differential vegetation index NDVI and enhanced vegetation index EVI), calculated on the base of satellite data. For this purpose, we conducted a regression analysis of the data to determine the correlation between the yields of the above-mentioned crops by years (for the period of 2012-2019) with the values of the vegetation indices by the months of the studied period. The region-averaged vegetation indices were calculated based on a smoothed 250-m MODIS Terrain NDVI and MODIS Terrain EVI imaginary series using GDAL QGIS 3.10 raster analysis toolkit, excluding vegetation-free zones. Based on the results of the work, models of early (30-45 days before the start of mass harvesting) high-precision winter wheat and barley grain yields according to the regional NDVI and EVI are proposed. The novelty of the research is in the development of a convenient and high-precision forecasting tool for the identification of the risks connected with food shortage and planning a strategy for the region's export capabilities and calculation of the level of local grain supply.