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

Abstract

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.

Crop Yield Estimation Using Sentinel-3 SLSTR, Soil Data, and Topographic Features Combined with Machine Learning Modeling: A Case Study of Nepal

“…This finding is consistent with the observed distribution of average wheat yield per district, where lower nitrogen content tends to correlate with higher yields. Furthermore, variables like slope, total column water vapor, and organic matter have also revealed varying degrees of influence on crop yield, as highlighted in previous studies [130][131][132]. These features demonstrate great potential for application in agronomy-based studies and agricultural management systems.…”

A Novel and Extensible Remote Sensing Collaboration Platform: Architecture Design and Prototype Implementation

“…There are also many application cases in production activities and public services. Karimli [9] used Sentinel-2 raster data and SRTM DEM data to calculate vegetation and soil indices to estimate winter wheat yield. Su et al [10] used fishery data and satellite images to model the spatial distribution of swordfish to assist fishery management and conservation.…”

A New Remote Sensing Service Mode for Agricultural Production and Management Based on Satellite–Air–Ground Spatiotemporal Monitoring

“…Many scholars have continuously improved the accuracy of crop mapping by utilizing high-resolution remote sensing data [23,24], multisource feature sets [25], and advanced classification algorithms [26,27]. In terms of crop growth monitoring [28,29] and yield estimation [30][31][32], various types of data from different sensor platforms, such as satellites, UAVs, and the IoT on the ground, have become the main data sources [33]. On this basis, the combination of crop growth models and deep learning models is the development direction of crop growth monitoring and yield estimation research at present and in the future.…”