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Researchers have introduced an innovative approach, the Cross-Satellite Atmospheric Correction (CSAC) system, to standardize satellite ocean color data from diverse sources. This development enables the creation of reliable, global-scale, long-term records of bio-optical properties in the upper ocean. These "big data" records are essential for assessing marine ecosystems and their responses to c by Clarence Oxford
Los Angeles CA (SPX) Dec 13, 2024
Researchers have introduced an innovative approach, the Cross-Satellite Atmospheric Correction (CSAC) system, to standardize satellite ocean color data from diverse sources. This development enables the creation of reliable, global-scale, long-term records of bio-optical properties in the upper ocean. These "big data" records are essential for assessing marine ecosystems and their responses to climate changes.
Ocean color satellites, operational since the late 1990s, have transformed our understanding of the ocean's upper layers. They provide critical insights into properties like water clarity and phytoplankton distribution. However, differences in sensor designs and atmospheric correction algorithms across satellite missions have created challenges in unifying this data. Overcoming these discrepancies is vital to producing comprehensive datasets for climate monitoring.
The State Key Laboratory of Marine Environmental Science at Xiamen University and the National Satellite Ocean Application Service unveiled the CSAC system in a study published in the *Journal of Remote Sensing* on November 7, 2024 (DOI: 10.34133/remotesensing.0302). The CSAC system integrates data from multiple satellites, using MODIS-Aqua's high-quality reflectance data as a benchmark. This AI-powered system harmonizes top-of-atmosphere reflectance data from sensors like SeaWiFS and MERIS, significantly reducing inconsistencies and enhancing the reliability of ocean color records.
By employing artificial intelligence, CSAC processes satellite data uniformly, bypassing the need for sensor-specific algorithms. Its reference dataset, based on over 20 years of MODIS-Aqua observations, ensures unmatched accuracy. Tests revealed that CSAC reduced mean absolute percentage differences in reflectance by up to 50% compared to traditional methods, enabling scientists to track marine ecosystem dynamics and climate trends with greater precision.
"The CSAC system represents a significant advancement in satellite ocean color remote sensing," said Dr. Zhongping Lee, one of the study's lead researchers. "By harnessing decades of the highest-quality MODIS-Aqua data and sophisticated machine-learning techniques, we have resolved critical inconsistencies in Rrs among different satellites. This not only improves data reliability but also empowers the scientific community to create accurate, long-term records of ocean bio-optical properties, essential for climate studies."
The CSAC system's implications are transformative. It provides the scientific community with consistent, long-term satellite-derived datasets to monitor ocean ecosystems, study the ocean's role in the carbon cycle, and evaluate climate change impacts. By shifting from traditional radiative-transfer-based methods to advanced AI-based approaches, CSAC sets a new standard for satellite data processing.
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