期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 51, 期 7, 页码 3871-3884出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2012.2227976
关键词
Optically complex turbid waters; pigment C-phycocyanin (C-PC); type-specific algorithm; water classification
类别
资金
- National Natural Science Foundation for Young Scholars of China [41101340, 40801145]
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (Wuhan University) [(11) key 04]
- Key Laboratory of Digital Earth, Center for Earth Observation and Digital Earth, Chinese Academy of Sciences [2011LDE009]
- University Natural Science [11KJA170003]
- Ministry of Education, Province Jiangsu
- Priority Academic Program Development of Jiangsu Higher Education Institutions
Pigment C-phycocyanin (C-PC) is a useful indicator for the presence of cyanobacteria in inland waters, which has been well known as a phytoplankton group with many negative effects on human, animal, and aquatic ecosystem health. In recent years, the remote detection of the C-PC concentrations for inland waters has received much attention. However, their accurate quantification by means of remote sensing is still a challenge due to the significant bio-optical complexity of turbid inland waters. In this paper, three typical turbid inland lakes in China were investigated through in situ observed data sets containing optical and water quality parameters. By using a recently proposed TD 680 optical classification method, all collected samples were first classified into three types. For each type of water, we determined specific spectral sensitive regions for the pigment C-PC. Then, we developed three type-specific support vector regression (SVR) algorithms and an aggregated SVR algorithm. The performances of these algorithms were evaluated through the validation data sets. The results show that the type-specific algorithms generally have significantly improved performance over the aggregated SVR algorithm. Their assessment errors [mean absolute percentage error (MAPE) and root-mean-square error (rmse)] were as follows: 1) MAPE = 15.6% and rmse = 30.6 mg.m(-3) for Type 1 water; 2) MAPE = 47.1% and rmse = 61.5 mg.m(-3) for Type 2 water; and 3) MAPE = 26.4% and rmse = 19.1 mg.m(-3) for Type 3 water. The findings in this paper demonstrate that a prior water classification is needed for the development of accurate C-PC retrieval algorithms. This paper provides a valid strategy for improving C-PC estimation accuracy and enhancing algorithm commonality for optically complex turbid waters.
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