Approach to Predict Farmland Gross Primary Productivity (GPP) for Sustainable Food Production Using Remote Sensing Data

Abstract To address numerous farmland challenges, Russia has been implementing abundant programs that have been demonstrated to significantly reduce the ecological degradation of farming. Food security and sustainable agricultural production constitute significant concerns for society. This research analyzes various data to explore these aspects in Russia, such as: soil moisture, net primary productivity (NPP), gross primary productivity, actual evapotranspiration (ETa), precipitation (mm), land surface temperature (°C), normalized difference vegetation Index, leaf area index, and digital elevation model (DEM), and crop grain production in 2010–2020. The goal of this study is to develop a linear regression model for predicting farmland gross primary productivity (GPP) by integrating remote sensing data with various environmental and agricultural parameters. The annual farmland GPP increased from 2010 to 2020. The results illustrate that the differences in crop cereal production data were not consistent with the annual pattern. Therefore, the recommended linear model for farmland GPP prediction performed significantly better in the regional studies. Our findings demonstrate that linear regression-based farmland GPP products can be utilized in the adequate assessment of agricultural ecosystems at the field and regional scales. It is also beneficial for food safety and security for the growing population.