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

Remote Sensing 2020 26 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 30 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Yao Liu, Chenchao Xiao, Junsheng Li, Fangfang Zhang, Shenglei Wang

Summary

Researchers validated a satellite-based method for estimating water clarity (Secchi disk depth) in Chinese lakes using hyperspectral imagery. While focused on remote sensing of water quality, such tools can be applied to monitoring microplastic distribution in aquatic environments by proxy.

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.

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