 |
Online ISSN : 2349-8080 Issues : 12 per year Publisher : Excellent Publishers Email : editorinchiefijcrbp@gmail.com |
Timely and accurate crop area estimation is a critical component of precision agriculture and agricultural resource management. This study evaluates the utility of multi-temporal Sentinel-1A C-band Synthetic Aperture Radar (SAR) data for kharif paddy area estimation in Thiruvarur district, Tamil Nadu—an agriculturally vital region of the Cauvery Delta. SAR data acquired at 12-day intervals from May to October 2019 were pre-processed using a fully automated chain in MAPscape-RICE software, including steps such as strip mosaicking, temporal co-registration, speckle filtering, terrain geocoding, radiometric calibration, and anisotropic non-linear diffusion filtering. A rule-based classification algorithm was employed to exploit temporal σ° backscatter signatures specific to paddy phenology, guided by agronomic knowledge and ground truth observations from 124 geo-referenced locations. Temporal features—such as minimum and maximum σ°, variation metrics, and flooding duration—were used to parameterize a classification model optimized for the agro-ecological conditions of the region. The classification output revealed that Thiruvarur district had a total kharif paddy area of 17,141.5 hectares, with block-wise distributions mapped and analyzed. Accuracy assessment using 94 independent validation points produced an overall classification accuracy of 91.9% and a Kappa coefficient of 0.77, indicating a strong correlation with ground truth data. The results confirm that SAR remote sensing, with its all-weather, day-and-night imaging capabilities, is a robust alternative to optical sensors for paddy mapping, particularly under persistent cloud cover
