901 / 2024-09-19 19:06:49
Cross-Sensor Atmospheric Correction for Consistent Remote Sensing Reflectance Products in the Ross Sea
Cross-sensor atmospheric correction, Remote sensing reflectance; Neural networks; Ross Sea; Ocean color
Session 7 - Advances in the Oceanography of the Ross Sea
Abstract Accepted
Merging bio-optical products from multiple ocean color missions requires consistent remote sensing reflectance (Rrs) measurements. However, differences in sensor characteristics and data processing have led to significant discrepancies in the Rrs products, particularly in polar oceans. In this study, we introduce a Cross-Sensor Atmospheric Correction (CSAC) algorithm that uses neural networks to achieve consistent Rrs retrievals between Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua in the Ross Sea, Southern Ocean. The CSAC converts VIIRS top-of-atmosphere reflectance (ρt-VIIRS) into Rrs at equivalent MODIS-Aqua bands (Rrs-MAE), where a large dataset of matched high-quality MODIS-Aqua Rrs and ρt-VIIRS was used to train the algorithm (N > 9 million). With CSAC, the averaged absolute percent difference (APD) between VIIRS-coverted Rrs-MAE and the standard MODIS-Aqua Rrs (Rrs-MA) ranges from 8.8% to 22.3% across wavelengths of 412–667, which are significantly better than the APD of standard VIIRS Rrs product (Rrs-VIIRS) (14.7%–85.9%). Importantly, CSAC also increases the number of valid Rrs retrievals in the Ross Sea compared to Rrs-VIIRS, greatly enhancing data coverage, particularly for observation with high solar and viewing angles. These results highlight the effectiveness of CSAC in generating higher-quality, more consistent ocean color data across sensors, which is essential for long-term monitoring of polar marine ecosystems.