| Summary: | Water clarity, commonly measured as the Secchi disk depth (<inline-formula> <math display="inline"> <semantics> <msub> <mi>Z</mi> <mrow> <mi>s</mi> <mi>d</mi> </mrow> </msub> </semantics> </math> </inline-formula>), 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 <inline-formula> <math display="inline"> <semantics> <mrow> <mi>Q</mi> <mi>A</mi> <msub> <mi>A</mi> <mrow> <mi>V</mi> <mn>5</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>, <inline-formula> <math display="inline"> <semantics> <mrow> <mi>Q</mi> <mi>A</mi> <msub> <mi>A</mi> <mrow> <mi>V</mi> <mn>6</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>, <inline-formula> <math display="inline"> <semantics> <mrow> <mi>Q</mi> <mi>A</mi> <msub> <mi>A</mi> <mrow> <mi>L</mi> <mn>09</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mrow> <mi>Q</mi> <mi>A</mi> <msub> <mi>A</mi> <mrow> <mi>M</mi> <mn>14</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>, for the AHSI images with concurrent in situ measurements in four inland water bodies with a <inline-formula> <math display="inline"> <semantics> <msub> <mi>Z</mi> <mrow> <mi>s</mi> <mi>d</mi> </mrow> </msub> </semantics> </math> </inline-formula> range of 0.3–4.5 m. The semi-analytical method with <inline-formula> <math display="inline"> <semantics> <mrow> <mi>Q</mi> <mi>A</mi> <msub> <mi>A</mi> <mrow> <mi>V</mi> <mn>5</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> can yield the most accurate <inline-formula> <math display="inline"> <semantics> <msub> <mi>Z</mi> <mrow> <mi>s</mi> <mi>d</mi> </mrow> </msub> </semantics> </math> </inline-formula> predictions with approximated atmospheric-corrected remote sensing reflectance. For 84 concurrent sampling sites, the estimated <inline-formula> <math display="inline"> <semantics> <msub> <mi>Z</mi> <mrow> <mi>s</mi> <mi>d</mi> </mrow> </msub> </semantics> </math> </inline-formula> had a mean absolute error (MAE) of 0.35 m, while the mean relative error (MRE) was 25.3%. Specifically, the MAEs of estimated <inline-formula> <math display="inline"> <semantics> <msub> <mi>Z</mi> <mrow> <mi>s</mi> <mi>d</mi> </mrow> </msub> </semantics> </math> </inline-formula> were 0.22, 0.46, and 0.24 m for <inline-formula> <math display="inline"> <semantics> <msub> <mi>Z</mi> <mrow> <mi>s</mi> <mi>d</mi> </mrow> </msub> </semantics> </math> </inline-formula> 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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