气候变化领域集成服务门户(CCPortal): A soft-classification-based chlorophyll-a estimation method using MERIS data in the highly turbid and eutrophic Taihu Lake

CCPortal

DOI	10.1016/j.jag.2018.07.018
	A soft-classification-based chlorophyll-a estimation method using MERIS data in the highly turbid and eutrophic Taihu Lake
	Zhang F.; Li J.; Shen Q.; Zhang B.; Tian L.; Ye H.; Wang S.; Lu Z.
发表日期	2019
ISSN	15698432
起始页码	138
结束页码	149
卷号	74
英文摘要	Soft-classification-based methods for estimating chlorophyll-a concentration (C chla ) by satellite remote sensing have shown great potential in turbid coastal and inland waters. However, one of the most important water color sensors, the MEdium Resolution Imaging Spectrometer (MERIS), has not been applied to the study of turbid or eutrophic lakes. In this study, we developed a new soft-classification-based C chla estimation method using MERIS data for the highly turbid and eutrophic Taihu Lake. We first developed a decision tree to classify Taihu Lake into three optical water types (OWTs) using MERIS reflectance data, which were quasi-synchronous (±3 h) with in situ measured C chla data from 91 sample stations. Secondly, we used MERIS reflectance and in situ measured C chla data in each OWT to calibrate the optimal C chla estimation model for each OWT. We then developed a soft-classification-based C chla estimation method, which blends the C chla estimation results in each OWT by a weighted average, where the weight for each MERIS spectra in each OWT is the reciprocal value of the spectral angle distance between the MERIS spectra and the centroid spectra of the OWT. Finally, the soft-classification based C chla estimation algorithm was validated and compared with no-classification and hard-classification-based methods by the leave-one-out cross-validation (LOOCV) method. The soft-classification-based method exhibited the best performance, with a correlation coefficient (R 2 ), average relative error (ARE), and root-mean-square error (RMSE) of 0.81, 33.8%, and 7.0 μg/L, respectively. Furthermore, the soft-classification-based method displayed smooth values at the edges of OWT boundaries, which resolved the main problem with the hard-classification-based method. The seasonal and annual variations of C chla were computed in Taihu Lake from 2003 to 2011, and agreed with the results of previous studies, further indicating the stability of the algorithm. We therefore propose that the soft-classification-based method can be effectively used in Taihu Lake, and that it has the potential for use in other optically-similar turbid and eutrophic lakes, and using spectrally-similar satellite sensors. © 2018 Elsevier B.V.
英文关键词	Chlorophyll-a estimation; MEdium Resolution Imaging Spectrometer (MERIS); Optical water types (OWTs); Soft-classification-based method
语种	英语
scopus关键词	annual variation; chlorophyll a; classification; concentration (composition); estimation method; eutrophic environment; MERIS; remote sensing; seasonal variation; turbidity; China; Taihu Lake
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156549
作者单位	Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China; University of Chinese Academy of Sciences, Beijing, 100049, China; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China
推荐引用方式 GB/T 7714	Zhang F.,Li J.,Shen Q.,et al. A soft-classification-based chlorophyll-a estimation method using MERIS data in the highly turbid and eutrophic Taihu Lake[J],2019,74.
APA	Zhang F..,Li J..,Shen Q..,Zhang B..,Tian L..,...&Lu Z..(2019).A soft-classification-based chlorophyll-a estimation method using MERIS data in the highly turbid and eutrophic Taihu Lake.International Journal of Applied Earth Observation and Geoinformation,74.
MLA	Zhang F.,et al."A soft-classification-based chlorophyll-a estimation method using MERIS data in the highly turbid and eutrophic Taihu Lake".International Journal of Applied Earth Observation and Geoinformation 74(2019).