An approach for retrieval of horizontal and vertical distribution of total suspended matter concentration from GOCI data over Lake Hongze

• Published in: The Science of the total environment 2020-01, Vol.700, p.134524-134524, Article 134524
• Main Authors: Lei, Shaohua, Xu, Jie, Li, Yunmei, Du, Chenggong, Liu, Ge, Zheng, Zhubin, Xu, Yifan, Lyu, Heng, Mu, Meng, Miao, Song, Zeng, Shuai, Xu, Jiafeng, Li, Lingling
• Format: Article
• Language: English
• Subjects: Lake Hongze; Remote sensing; Suspended particulate matter; Vertical distribution
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2019.134524

[Display omitted] •A robust algorithm to estimate the horizontal and vertical of TSM was developed.•The spatiotemporal and vertical variation of TSM were mapped in Lake Hongze.•The difference between the two hypotheses for estimating CMSM were discussed.•The response of spatiotemporal TSM and CMSM on climate data were discussed. There are a few studies working on the vertical distribution of TSM, however, understanding the underwater profile of TSM is of great benefit to the study of biogeochemical processes in the water column that still require further research. In this study, three data-gathering expeditions were conducted in Lake Hongze (HZL), China, between 2016 and 2018. Based on the in situ optical and biological data, a multivariate linear stepwise regression method was applied for retrieval of the surface horizontal distribution of TSM (TSM0.2) using GOCI (Geostationary Ocean Color Imager) data. Then, the estimation model of vertical structure of underwater TSM was constructed using layer-by-layer recursion. This study drew several crucial findings: (1) the approach proposed in this paper generated very high goodness of fit results, with determination coefficients (R2) of 0.83 (p