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Retrieval of Secchi Disk Depth in Turbid Lakes from GOCI Based on a New Semi-Analytical Algorithm
Open AccessArticle

Secchi Disk Depth Estimation from China’s New Generation of GF-5 Hyperspectral Observations Using a Semi-Analytical Scheme

1
Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of China, Beijing 100048, China
2
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
3
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
4
Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(11), 1849; https://doi.org/10.3390/rs12111849
Received: 22 April 2020 / Revised: 27 May 2020 / Accepted: 4 June 2020 / Published: 8 June 2020
(This article belongs to the Special Issue Remote Sensing of Aquatic Ecosystem Health and Processes)
Water clarity, commonly measured as the Secchi disk depth ( Z s d ), is an important parameter that depicts water quality in aquatic ecosystems. China’s new generation Advanced HyperSpectral Imager (AHSI) on board the GF-5 satellite has significant potential for applications of more accurate water clarity estimation compared with existing multispectral satellite imagery, considering its high spectral resolution with a 30-m spatial resolution. In this study, we validate the semi-analytical model with various Quasi-Analytical Algorithms (QAA), including Q A A V 5 , Q A A V 6 , Q A A L 09 and Q A A M 14 , for the AHSI images with concurrent in situ measurements in four inland water bodies with a Z s d range of 0.3–4.5 m. The semi-analytical method with Q A A V 5 can yield the most accurate Z s d predictions with approximated atmospheric-corrected remote sensing reflectance. For 84 concurrent sampling sites, the estimated Z s d had a mean absolute error (MAE) of 0.35 m, while the mean relative error (MRE) was 25.3%. Specifically, the MAEs of estimated Z s d were 0.22, 0.46, and 0.24 m for Z s d of 0.3–1, 1–3, and 3–4.5 m, respectively. The corresponding MREs were 33.1%, 29.1% and 6.3%, respectively. Although further validation is still required, especially in terms of highly turbid waters, this study indicates that AHSI is effective for water clarity monitoring. View Full-Text
Keywords: Secchi-disk depth; hyperspectral imagery; GF-5 satellite; semi-analytical model; Quasi-Analytical Algorithm Secchi-disk depth; hyperspectral imagery; GF-5 satellite; semi-analytical model; Quasi-Analytical Algorithm
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MDPI and ACS Style

Liu, Y.; Xiao, C.; Li, J.; Zhang, F.; Wang, S. Secchi Disk Depth Estimation from China’s New Generation of GF-5 Hyperspectral Observations Using a Semi-Analytical Scheme. Remote Sens. 2020, 12, 1849.

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