Validation of MERIS Case-2 Water Products in Lake Taihu, China

Hongtao Duan¹, Ronghua Ma¹, Stefan G.H.Simis², and Yuanzhi Zhang³

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

http://www.rslakes.com/up/document/2012/Validation%20of%20MERIS%20Case-2%20Water%20Products%20in%20Lake%20Taihu,%20China.pdf

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.