Evaluating the Consistency of Radiometric Cross-Calibrated TOA Radiance Products Between Landsat-8 OLI and Landsat-9 OLI2

As China's space technology rapidly progresses, the frequency of remote sensing satellite launches has significantly increased, elevating the demand for data consistency in integrated multiple sensors applications. Thus, the radiometric consistency of multiple sensors should be verified. The Gaofen-6 (GF-6) satellite, the first high-resolution optical remote sensing satellite dedicated to precise agricultural monitoring in China, is equipped with panchromatic and multispectral sensor (PMS) and wide field of view (WFV) and plays a pivotal role in agricultural production assessment and other related fields. In this study, evaluating the consistency of GF-6 and Sentinel-2 Multispectral Imager (MSI) was performed by incorporating spectral band adjustment factor (SBAF) and bidirectional reflectance distribution function (BRDF) corrections with Landsat-8 Operational Land Imager (OLI) as the reference sensor. Additionally, the radiometric consistency of top of atmosphere (TOA) radiance products following cross-calibration was evaluated. The results were compared with those from Landsat-9 OLI2 regarding TOA radiance and surface reflectance accuracy. The fitting slopes of TOA radiance for PMS and MSI, and for WFV and MSI were 0.985 and 1.038, respectively, with better consistency observed in the visible bands compared to the NIR bands. Furthermore, the comparison with Landsat-9 OLI2 TOA radiance revealed that the fitting slopes were close to 1, correlation coefficients exceeded 0.84 for both PMS and WFV, and relative errors were less than 6%. The cross-calibration approach significantly outperformed vicarious calibration in accuracy. Overall, the Landsat-8 OLI-based cross-calibration method substantially improved the consistency of TOA radiance products among PMS, WFV, and MSI sensors and maintaining stable radiometric agreement with OLI2. This study has validated the reliability and effectiveness of the OLI-based cross-calibration approach, laying a robust foundation for ensuring the consistency of multi-source remote sensing data in quantitative applications.