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Validation of MERIS Case-2 Water Products in Lake Taihu, China
1 State Key Laboratory ofLake Science and Environment, Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences, Nanjing, 210008, People’s Republic of China
2 Finnish EnvironmentInstitute SYKE, Marine Research Centre,Erik Palménin Aukio 1, 00560 Helsinki,Finland
3 Instituteof Space and Earth Information Science, The Chinese University of Hong Kong, HongKong, People’s Republic of China
GIScience & Remote Sensing 49 (6), 873-894
MERIS遥感影像由于具有620、709、753等对藻蓝素和叶绿素比较敏感的波段,是目前最适合内陆湖泊等二类水体水色遥感的传感器。欧空局开发了针对MERIS等影像的BEAM软件,其中含有三个针对二类水体的算法。这些算法都是建立在神经网络基础上,而其训练集多是采用欧洲水体的数据,因此能否在我国太湖使用就存在较大疑问。本文通过MERIS过境时的同步数据,对MERIS的三个水色产品分别进行了验证。主要结果如下:
Threeartificial neural network (ANN) processors available as plug-in modules for theBasic ERS & ENVISAT (A)ATSR and MERIS Toolbox (BEAM) were validated in LakeTaihu, China. Mean deviations of reflectance derived from LakesBoreal and LakesEutrophic were 10 - 90%, while reflectance from the FUB-WeW processor showed larger errors. All processors showed underestimates of chlorophylla (Chl-a), total suspended matter (TSM), and phytoplankton pigment absorption,while particulate scattering values were severely overestimated. None of thereadily available MERIS processors is currently able to separate atmosphericand water-leaving radiance over Lake Taihu, while the retrieval ofphytoplankton biomass through ANN processors shows promise.
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