In the rapidly evolving world of remote sensing, ensuring data consistency across multiple satellites is crucial for accurate agricultural monitoring and assessment. A recent study published in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing has shed light on this very issue, focusing on the radiometric consistency of China’s Gaofen-6 (GF-6) satellite and its counterparts, Landsat-8 OLI and Landsat-9 OLI2. The research, led by Qilong Zhou from the College of Geoscience and Surveying Engineering at China University of Mining and Technology in Beijing, delves into the intricacies of cross-calibrating top-of-atmosphere (TOA) radiance products, with significant implications for the energy sector and beyond.
The Gaofen-6 satellite, China’s first high-resolution optical remote sensing satellite dedicated to precise agricultural monitoring, is equipped with advanced sensors that play a pivotal role in agricultural production assessment. However, with the increasing frequency of satellite launches, the demand for data consistency in integrated multiple sensors’ applications has become more pressing than ever. “The radiometric consistency of multiple sensors should be verified to ensure the reliability of the data,” Zhou emphasized.
The study evaluated the consistency of GF-6 and Sentinel-2 multispectral imager (MSI) by incorporating spectral band adjustment factors and bidirectional reflectance distribution function corrections, using Landsat-8 OLI as the reference sensor. The results were then compared with those from Landsat-9 OLI2 regarding TOA radiance and surface reflectance accuracy. The findings revealed that the fitting slopes of TOA radiance for PMS and MSI and for WFV and MSI were 0.985 and 1.038, respectively, indicating better consistency in the visible bands compared with the near-infrared (NIR) bands.
Moreover, the comparison with Landsat-9 OLI2 TOA radiance showed 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,” Zhou noted. This suggests that the Landsat-8 OLI-based cross-calibration method substantially improved the consistency of TOA radiance products among PMS, WFV, and MSI sensors while maintaining stable radiometric agreement with OLI2.
The implications of this research are far-reaching, particularly for the energy sector. Accurate and consistent remote sensing data is essential for monitoring solar farms, assessing vegetation health for biomass energy, and even predicting crop yields for biofuel production. As the world transitions towards renewable energy sources, the need for reliable and consistent satellite data becomes ever more critical.
This study has validated the reliability and effectiveness of the OLI-based cross-calibration approach, laying a robust foundation for ensuring the consistency of multisource remote sensing data in quantitative applications. As we look to the future, this research paves the way for more integrated and accurate remote sensing applications, benefiting not only the agricultural sector but also the energy industry and beyond. With the increasing launch of remote sensing satellites, the demand for data consistency will only grow, making studies like Zhou’s invaluable in shaping the future of the field. The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, translated to English, is a leading publication in the field, and this research is a testament to the journal’s commitment to advancing the science of remote sensing.