Atmospheric correction(AC)is a critical step in ocean color remote sensing,particularly for coastal waters that still face challenges from high concentrations of suspended materials and absorbing aerosols.To address t...Atmospheric correction(AC)is a critical step in ocean color remote sensing,particularly for coastal waters that still face challenges from high concentrations of suspended materials and absorbing aerosols.To address these limitations,this study presents a novel AC algorithm,termed ACA-SIM(atmospheric correction based on satellite-in situ matchup data),based on extensive matchups between satellite measurements(ρ_(t))and in situ remote sensing reflectance(R_(rs))from Aerosol Robotic Network-Ocean Color(AERONET-OC),with neural networks as the processing tool.Unlike the Ocean Color-Simultaneous Marine and Aerosol Retrieval Tool(OC-SMART) algorithm,which employs a similar strategy but relies on simulated data,ACA-SIM uses real-world matchups betweenρ_(t) and in situ R_(rs),capturing sensor-specific effects such as striping and encompassing a wide range of water and aerosol properties.Validations with independent AERONET-OC measurements demonstrated that ACA-SIM outperformed both the NASA Standard and OC-SMART,achieving higher accuracy of R_(rs) across all spectral bands.In particular,for the blue bands,ACA-SIM reduced the mean absolute percentage difference(MAPD)of derived R_(rs) to~15%,compared to an MAPD of~32%by OC-SMART,and maintained robust performance even under challenging conditions.Moreover,when applied to Moderate-Resolution Imaging Spectroradiometer-Aqua images in highly turbid and dust-or smoke-affected regions,such as the Bohai and Yellow Seas,the West Coast of North Africa,and areas impacted by Australian bushfires,ACA-SIM demonstrated exceptional capability in minimizing striping effects and generating reliable R_(rs) products.This study advances AC techniques for coastal waters and reinforces the importance of the AERONET-OC network.Furthermore,it lays a foundation for extending ACA-SIM to other satellite sensors,enabling the generation of consistent and accurate ocean color products across multiple satellite platforms.展开更多
基金support from the National Key Research and Development Program of China(2022YFC3104901)the National Natural Science Foundation of China(#42430107)Fujian Ocean Innovation Laboratory,Fujian Satellite Data Development,Co.,Ltd.,and Fujian Haisi Digital Technology Co.,Ltd.
文摘Atmospheric correction(AC)is a critical step in ocean color remote sensing,particularly for coastal waters that still face challenges from high concentrations of suspended materials and absorbing aerosols.To address these limitations,this study presents a novel AC algorithm,termed ACA-SIM(atmospheric correction based on satellite-in situ matchup data),based on extensive matchups between satellite measurements(ρ_(t))and in situ remote sensing reflectance(R_(rs))from Aerosol Robotic Network-Ocean Color(AERONET-OC),with neural networks as the processing tool.Unlike the Ocean Color-Simultaneous Marine and Aerosol Retrieval Tool(OC-SMART) algorithm,which employs a similar strategy but relies on simulated data,ACA-SIM uses real-world matchups betweenρ_(t) and in situ R_(rs),capturing sensor-specific effects such as striping and encompassing a wide range of water and aerosol properties.Validations with independent AERONET-OC measurements demonstrated that ACA-SIM outperformed both the NASA Standard and OC-SMART,achieving higher accuracy of R_(rs) across all spectral bands.In particular,for the blue bands,ACA-SIM reduced the mean absolute percentage difference(MAPD)of derived R_(rs) to~15%,compared to an MAPD of~32%by OC-SMART,and maintained robust performance even under challenging conditions.Moreover,when applied to Moderate-Resolution Imaging Spectroradiometer-Aqua images in highly turbid and dust-or smoke-affected regions,such as the Bohai and Yellow Seas,the West Coast of North Africa,and areas impacted by Australian bushfires,ACA-SIM demonstrated exceptional capability in minimizing striping effects and generating reliable R_(rs) products.This study advances AC techniques for coastal waters and reinforces the importance of the AERONET-OC network.Furthermore,it lays a foundation for extending ACA-SIM to other satellite sensors,enabling the generation of consistent and accurate ocean color products across multiple satellite platforms.
