Remote Sensing-Based Yield Estimation of Winter Wheat Using Vegetation and Soil Indices in Jalilabad, Azerbaijan

Concerns about the expanding human population's adequate supply of food draw attention to the field of Food Security. Future-focused analysis and processing of agricultural data not only improve planning capabilities in this field but also enables the required precautions to be taken beforehand. However, given the breadth and number of these regions, field research would be an expensive and time-consuming endeavour. With the advent of remote sensing and optical sensors, it is now possible to acquire diverse data remotely, quickly, and inexpensively. This study investigated the limitations and capabilities of remote sensing data application in the field of planning Food Security. As a result, Sentinel 2 and Shuttle Radar Topography Mission (SRTM) data were used to estimate winter wheat yields with a high degree of accuracy (98.03%) using the Mamatkulov technique and the MEDALUS model, which was both free and widely available. This method can make it possible to make predictions about the productivity of newly created crop fields or for which we do not have information about the productivity of previous years, without the need to wait for building regression models or any field studies. Considering the outcome, wide-range and larger analyses on this topic can be carried through.

Automated summary

This study investigated the limitations and capabilities of remote sensing data application in the field of planning Food Security, and used Sentinel 2 and Shuttle Radar Topography Mission (SRTM) data to estimate winter wheat yields with a high degree of accuracy (98.03%). This method makes it possible to predict the productivity of newly created crop fields without the need for regression models or field studies.