Knowledge Management System Of South China Sea Institute of Oceanology,CAS
| Application of a generalized additive model (GAM) for estimating chlorophyll-a concentration from MODIS data in the Bohai and Yellow Seas, China | |
| Wang, YQ; Liu, DY; Tang, DL; dyliu@yic.ac.cn | |
| 2017 | |
| Source Publication | INTERNATIONAL JOURNAL OF REMOTE SENSING
 |
| Volume | 38Issue:3Pages:639-661 |
| Abstract | In optically complex waters, it is important to evaluate the accuracy of the standard satellite chlorophyll-a (chl-a) concentration algorithms, and to develop accurate algorithms for monitoring the dynamics of chl-a concentration. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote-sensing reflectance and concurrent in situ measured chl-a (20102013) were used to evaluate the standard OC3M algorithm (ocean chlorophyll-a three-band algorithm for MODIS) and Graver-Siegel-Maritorena model version 1 (GSM01) algorithm for estimating chl-a concentration in the Bohai and Yellow Seas (BYS). The results showed that the chl-a algorithms of OC3M and GSM01 with global default parameters presented poor performance in the BYS (the mean absolute percentage difference (MAPD) and coefficient of determination (R-2) of OC3M are 222.27% and 0.25, respectively; the MAPD and R-2 of GSM01 are 118.08% and 0.07, respectively). A novel statistical algorithm based on the generalized additive model (GAM) was developed, with the aim of improving the satellite-derived chl-a accuracy. The GAM algorithm was established using the in situ measured chl-a concentration as the output variable, and the MODIS above water remote-sensing reflectance (visible bands at 412, 443, 469, 488, 531, 547, 555, 645, 667, and 678 nm) and bathymetry (water depth) as input variables. The MAPD and R-2 calculated between the GAM and the in situ chl-a concentration are 39.96% and 0.67, respectively. The results suggest that the GAM algorithm can yield a superior performance in deriving chl-a concentrations relative to the standard OC3M and GSM01 algorithms in the BYS. |
| Department | [Wang, Yueqi; Liu, Dongyan] Chinese Acad Sci, Yantai Inst Coastal Zone Res, Key Lab Coastal Zone Environm Proc & Ecol Remedia, Yantai, Shandong, Peoples R China; [Liu, Dongyan] East China Normal Univ, State Key Lab Estuarine & Coastal Res, Shanghai, Peoples R China; [Tang, DanLing] Chinese Acad Sci, South China Sea Inst Oceanol, State Key Lab Trop Oceanog, Res Ctr Remote Sensing Marine Ecol & Environm, Guangzhou, Guangdong, Peoples R China |
| Funding Project | LTO |
| Document Type | 期刊论文 |
| Identifier | http://ir.scsio.ac.cn/handle/344004/16420 |
| Collection | 热带海洋环境国家重点实验室(LTO) |
| Corresponding Author | dyliu@yic.ac.cn |
| Recommended Citation GB/T 7714 | Wang, YQ,Liu, DY,Tang, DL,et al. Application of a generalized additive model (GAM) for estimating chlorophyll-a concentration from MODIS data in the Bohai and Yellow Seas, China[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2017,38(3):639-661. |
| APA | Wang, YQ,Liu, DY,Tang, DL,&dyliu@yic.ac.cn.(2017).Application of a generalized additive model (GAM) for estimating chlorophyll-a concentration from MODIS data in the Bohai and Yellow Seas, China.INTERNATIONAL JOURNAL OF REMOTE SENSING,38(3),639-661. |
| MLA | Wang, YQ,et al."Application of a generalized additive model (GAM) for estimating chlorophyll-a concentration from MODIS data in the Bohai and Yellow Seas, China".INTERNATIONAL JOURNAL OF REMOTE SENSING 38.3(2017):639-661. |
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| Application of a gen（3822KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | Application Full Text | |
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