Radiometric Cross Validation Of Resourcesat-2/2A Liss-3 Sensor Reflectance Products With RadCalNet Data

Validation of satellite-derived surface reflectance (SR) or bottom of the atmosphere (BOA) products enhances the credibility for reliable resource monitoring and usability for a wide range of applications such as land cover mapping, forest monitoring, climate studies and precision agriculture. As satellite remote sensing continues to play a pivotal role in global environmental monitoring, accurate and traceable calibration/validation remains a critical component. This study presents a novel approach to the radiometric validation of surface reflectance products derived from Resourcesat-2(R2)/2A (RA) LISS3 sensor using RadCalNet (RCN) data, a comprehensive and high-quality ground-measured spectral radiance and reflectance dataset. By leveraging the spectral and radiometric information from RadCalNet and incorporating atmospheric correction, sensor-specific response functions and statistical techniques a traceable and accurate validation of SR products is established. SR products are well correlated with RCN-SR values having correlation coefficient value greater than 85% for R2 and greater than 94% for RA. Stability of SR values since launch was also evaluated with RCN measurements and found to be consistent. (Within 15 %(R2) and less than 10 %(RA)).