Innovative Approaches to Bengal gram Yield Mapping: Integration of Sentinel-1 SAR and Crop Simulation Models for Precision Agriculture
Sellaperumal Pazhanivelan *
Centre for Water and Geospatial Studies, Tamil Nadu Agricultural University, India.
N.S. Sudarmanian
Centre for Water and Geospatial Studies, Tamil Nadu Agricultural University, India.
S. Satheesh
Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, India.
K.P. Ragunath
Centre for Water and Geospatial Studies, Tamil Nadu Agricultural University, India.
*Author to whom correspondence should be addressed.
Abstract
Accurate spatial yield estimation is crucial for optimizing agricultural management and ensuring food security. This study integrates Sentinel-1A SAR remote sensing data and the DSSAT crop simulation model to predict Bengal gram (chickpea) yield in Nagaur district, Rajasthan, India. Sentinel-1A backscatter data were processed for crop area mapping, achieving an overall classification accuracy of 85.1% and a kappa index of 0.70, demonstrating the reliability of SAR for agricultural monitoring under diverse weather conditions. Leaf Area Index (LAI) was derived from SAR backscatter values and linked to DSSAT-simulated yields, generating spatial yield predictions. Validation using Crop Cutting Experiment (CCE) data showed a high agreement of 91.3% between predicted and observed yields, with low root mean square error (RMSE), confirming model accuracy. This research highlights the synergistic potential of SAR-based remote sensing and simulation models for large-scale yield forecasting, advancing precision agriculture. Future efforts may incorporate additional sensors and machine learning to further enhance prediction accuracy and adaptability to climate variability.
Keywords: Backscatter value, crop simulation model, remote sensing, SAR data