Secchi Depth estimation for optically-complex waters based on spectral angle mapping - derived water classification using Sentinel-2 data

ABSTRACT

Classification-based methods for estimating water quality parameter (WQP) using remote sensing have shown great application potential in inland waters. Water classification algorithms have seen progress in water remote sensing. In this paper, we conducted the Secchi Depth value (Z_SD) estimation based on a global water typology for the Wuhan area. Firstly, we classified the water into seven types using the Sentinel-2 data. The procedure was based on spectral angle mapping (SAM) of the 13 inland water optical types (IWOTs). Afterwards, seven IWOTs were summarized into four categories for Wuhan area. We then developed empirical models for each water category by stepwise multiple linear regression, generalized regression neural network (GRNN), and sparse spectrum Gaussian process regression (SSGPR), and applied the better approaches (GRNN and SSGPR) to three full satellite images (27 October 2018, 10 May 2019, and 29 July 2019). Finally, the retrieved results were validated using in situ-satellite match-ups and compared with the results based on unclassified imagery. With root-mean-square error (RMSE) of three satellite-derived results reduced from 0.32 m (without classification) to 0.16 m (with classification), and mean absolute percentage error (MAPE) reduced from 52% to 18%, from 0.51 m (MAPE = 57%) to 0.19 m (MAPE = 21%), and from 0.18 m (MAPE = 35%) to 0.09 m (MAPE = 17%), Z_SD estimations over optically complex waters were improved based on this water classification. Due to its low cost and ease of operation, the SAM – derived classification applied in this paper provides a possibility for dynamic and high-precision monitoring for water management.

Acknowledgements

The authors are grateful to the support of the workers from the Wuhan Environmental Monitoring Center during the water sample collection and laboratory analysis. The authors would like to thank Miguel Lazaro-Gredilla for the SSGPR algorithm. Specially, the authors would like to thank Claudia Giardino and Mariano Bresciani, the researchers in the Institute for Electromagnetic Sensing of the Environment, National Research Council in Italy, for their valuable comments and suggestions to improve the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was funded by the key research project of Water Conservancy in Hubei Province under Grant No. HBSLKY201910; this work was supported in part by Hubei Provincial Natural Science Foundation for Innovation Groups (No. 2019CFA019); this work was funded by the and the government procurement project of Wuhan Environmental Protection Bureau under Grant No.10020200916WHSHBJ and the Technological Innovation Special Major Project of Hubei Province under Grant No. 2016ACA168; Hubei Provincial Department of Water Resources [HBSLKY201910]; Wuhan Environmental Protection Bureau [10020200916WHSHBJ]; Technological Innovation Special Major Project of Hubei Province, Science and Technology Department of Hubei Province [2016ACA168].