Estimation of chickpea yield in the using remote sensing and GIS

Abstract

This study aims to evaluate the agricultural productivity of the Dediapada taluka using remote sensing and GIS technologies, focusing on NDVI and fAPAR data to estimate chickpea yield. The research spans two growing seasons, 2022-2023 and 2023-2024, utilizing satellite imagery from MODIS and Sentinel-2 combined with ground data collected from local sources. NDVI and fAPAR analyses reveal significant variations in vegetation health and productivity across the study area. The NDVI maps indicate high values during peak greenness periods, suggesting healthy crop growth. The fAPAR maps, derived from NDVI values, highlight regions with high photo synthetically active radiation absorption, correlating with higher productivity zones. Yield estimation models, incorporating APAR and other crop parameters, provide accurate predictions of chickpea yield. For the 2022-2023 season, the highest yield areas contributed 43.51% and 41.42% of the total area, with yields between 1.2-1.5 t/ha and 1.5-2.0 t/ha, respectively. The total estimated yield was approximately 1.59 t/ha. In the 2023-2024 season, the high-yield areas increased to 47.19% and 37.55%, with similar yield ranges. The total estimated yield improved to approximately 1.74 t/ha. The study underscores the importance of remote sensing data in providing timely and accurate yield estimates, which are crucial for effective agricultural planning and decision-making. The integration of NDVI and fAPAR indices with GIS tools presents a robust framework for monitoring and enhancing crop productivity in the Dediapada Taluka.