A Portable Algorithm to Retrieve Bottom Depth of Optically Shallow Waters from Top-Of-Atmosphere Measurements
作者机构：State Key Laboratory of Marine Environmental ScienceCollege of Ocean and Earth SciencesXiamen UniversityXiamen 361102China School for the EnvironmentUniversity of Massachusetts BostonBostonMA 02125USA School of Earth SciencesThe University of Western AustraliaCrawleyWA 6009Australia State Key Laboratory of Satellite Ocean Environment DynamicsSecond Institute of OceanographyMinistry of Natural ResourcesHangzhouChina
出 版 物：《Journal of Remote Sensing》 (国际遥感学报（英文）)
年 卷 期：2022年第2022卷第1期
页 面：268-283页
核心收录：
学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1002[医学-临床医学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术]
基 金：Financial supports from the Chinese Ministry of Science and Technology through the National Key Research and Development Program of China(#2016YFC1400905) the National Natural Science Foundation of China(#41941008,#41890803,and#41830102) the Joint Polar Satellite System(JPSS)funding for the NOAA ocean color calibration and validation(Cal/Val)project,and the University of Massachusetts Boston are greatly appreciated
主 题：Optically reflectance shallow
摘 要：Bottom depth(H)of optically shallow waters can be retrieved from multiband imagery,where remote sensing reflectance(R_(rs))are commonly used as the *** of the difficulties of removing the atmospheric effects in coastal areas,quite often,there are no valid R_(rs) from satellites for the retrieval of *** importantly,the empirical algorithms for H are hardly portable to new *** this study,using data from Landsat-8 and ICESat-2 as examples,we present an approach to retrieve H directly from the top-of-atmosphere(TOA)*** not only bypasses the requirement to correct the effects of aerosols but also shows promising portability to areas not included in algorithm ***,we use Rayleigh-corrected TOA reflectance(ρ_(rc))in the 443–2300nm range as input,along with a multilayer perceptron(MLP^(ρ_(rc))_(H)),for the retrieval of *** than 78,000 matchup points ofρ_(rc)and H(0–25 m)were used to train MLP^(ρ_(rc))_(H),which resulted in a Mean Absolute Percentage Difference(MARD)of 8.8%and a coefficient of determination(R^(2))of *** MLP^(ρ_(rc))_(H)was further applied to Landsat-8 data of six regions not included in the training phase,generating MARD and R^(2)values of 8.3%and 0.98,*** contrast,a conventional two-band ratio algorithm with R_(rs) as the input generated MARD and R^(2)values of 31.6%and 0.68 and significantly fewer H retrievals due to failures in atmospheric *** results indicate a breakthrough of algorithm portability of MLP^(ρ_(rc))_(H)in sensing H of optically shallow waters.
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