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

Abstract

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.