Satellite ocean color remote sensing plays a crucial role in monitoring marine environment at both regional and global scales.However,due to the reduced accuracy of atmospheric correction models under large solar zeni...Satellite ocean color remote sensing plays a crucial role in monitoring marine environment at both regional and global scales.However,due to the reduced accuracy of atmospheric correction models under large solar zenith angles(≥70°),publicly available satellite ocean color pro-ducts lack valid datasets for high-latitude oceans(≥50°S or≥50°N)during winter.Based on a neural network atmospheric correction model designed for high solar zenith angle observation environments(which used a Rayleigh scattering lookup table generated by PCOART-SA to compute Rayleigh scattering radiance and a neural network model to invert remote sensing reflectance from Rayleigh-corrected radiance),this study has established a monthly ocean color product dataset for high-latitude oceans,named NN-LAT50,covering the per-iod from 2003 to 2020.We validated the accuracy of the ocean color products in NN-LAT50 dataset using multiple in situ datasets,and the results indicated that NN-LAT50 had more reliable and accurate retrie-vals compared to the NASA released ocean color products in high latitude oceans.Furthermore,during autumn and winter,coverage of the NN-LAT50 dataset far exceeds that of products released by NASA.For instance,during the winter in the Southern Hemisphere,the cover-age rates are 3.02%for MODIS/Aqua,21.59%for VIIRS,and 1.74%for OLCI,while the NN-LAT50 dataset maintains a coverage rate of 38.64%.This study is the frst to establish a long-term(2003-2020)ocean color product dataset covering high-latitude oceans during winter,which can significantly enhance the observation of ecological changes in polar and subpolar oceans.展开更多
基金funded by the National Key Research and Development Program of China(Grant#2023YFC3108101)the National Natural Science Foundation of China(Grants#42176177,#42206183,and#U22B2012)+3 种基金the Zhejiang Provincial Natural Science Foundation of China(Grant#LDT23D06021D06)the"Pioneer"R&D Program of Zhejiang(Grant#2023C03011)the Zhejiang Provincial Natural Science Foundation of China(Grant#LY24D060005)the Science Foundation of Donghai Laboratory(Grant#DH-2023QH0002).
文摘Satellite ocean color remote sensing plays a crucial role in monitoring marine environment at both regional and global scales.However,due to the reduced accuracy of atmospheric correction models under large solar zenith angles(≥70°),publicly available satellite ocean color pro-ducts lack valid datasets for high-latitude oceans(≥50°S or≥50°N)during winter.Based on a neural network atmospheric correction model designed for high solar zenith angle observation environments(which used a Rayleigh scattering lookup table generated by PCOART-SA to compute Rayleigh scattering radiance and a neural network model to invert remote sensing reflectance from Rayleigh-corrected radiance),this study has established a monthly ocean color product dataset for high-latitude oceans,named NN-LAT50,covering the per-iod from 2003 to 2020.We validated the accuracy of the ocean color products in NN-LAT50 dataset using multiple in situ datasets,and the results indicated that NN-LAT50 had more reliable and accurate retrie-vals compared to the NASA released ocean color products in high latitude oceans.Furthermore,during autumn and winter,coverage of the NN-LAT50 dataset far exceeds that of products released by NASA.For instance,during the winter in the Southern Hemisphere,the cover-age rates are 3.02%for MODIS/Aqua,21.59%for VIIRS,and 1.74%for OLCI,while the NN-LAT50 dataset maintains a coverage rate of 38.64%.This study is the frst to establish a long-term(2003-2020)ocean color product dataset covering high-latitude oceans during winter,which can significantly enhance the observation of ecological changes in polar and subpolar oceans.
