###### Abstract  
Water clarity, commonly measured as the Secchi disk depth (\(Z_{sd}\)), 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 \(QAA_{V5}\), \(QAA_{V6}\), \(QAA_{L09}\) and \(QAA_{M14}\), for the AHSI images with concurrent in situ measurements in four inland water bodies with a \(Z_{sd}\) range of 0.3-4.5 m. The semi-analytical method with \(QAA_{V5}\) can yield the most accurate \(Z_{sd}\) predictions with approximated atmospheric-corrected remote sensing reflectance. For 84 concurrent sampling sites, the estimated \(Z_{sd}\) had a mean absolute error (MAE) of 0.35 m, while the mean relative error (MRE) was 25.3%. Specifically, the MAEs of estimated \(Z_{sd}\) were 0.22, 0.46, and 0.24 m for \(Z_{sd}\) 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. Secchi-disk depth; hyperspectral imagery; GF-5 satellite; semi-analytical model; Quasi-Analytical Algorithm 2020 12 1849 10.3390/rs12111849 10.  
determined as the Secchi disk depth (\(Z_{sd}\), in meters), that is, the depth at which the disk can no longer be seen from above the water. Although increasingly sophisticated optical-electro sensors are available for water clarity measurements, the Secchi disk is still extensively used due to its low cost and convenience [8; 9; 10]. However, implementing large-scale continuous monitoring of water clarity from ground stations or ship surveys is difficult. Remote sensing observations from satellites are likely the only feasible technique for the acquisition of large-scale and long-term water transparency data. Numerous empirical models have been developed to retrieve \(Z_{sd}\), and are mostly established on linear regressions of a single band or band ratios [11; 12; 13; 14; 15]. Although empirical models are advantageous in terms of simple model-building and rapid data processing, they tend to fail if applied to other water bodies that have different concentrations or types of optically active components (OACs). Semi-analytical methods (SAM) can be applied to various water types with improved accuracy. [PERSON] et al. [16] developed a new theoretical model that interprets \(Z_{sd}\) as inversely proportional to the diffuse attenuation coefficient at the wavelength of maximum light penetration. They further established a semi-analytical \(Z_{sd}\) estimation method based on the Quasi-Analytical Algorithms (QAA) [17]. This method can be applied to a wide range of water clarity retrieval from multiple sensors, such as MODIS and the Landsat-8 OLI, as it has been validated based on more than 300 in situ measurements from inland, coastal and oceanic waters, with a \(Z_{sd}\) range of 0-30 m [18; 19; 16]. For inland water quality parameter retrieval, hyperspectral imagery is capable of providing numerous narrow bands for optimal spectral combinations, which is an important advantage over multi-spectral sensors that have a limited number of broadbands. However, the application of SAM is seldom validated on the satellite hyperspectral imagery due to a lack of data. On 9 May 2018, China successfully launched the GF-5 satellite, which carries six payloads, including a new generation Advanced Hyperspectral Imager (AHSI) [20]. AHSI contains 330 bands covering a spectral range from 400-2500 nm. AHSI's spectral resolution is 5 nm for the 150 visible to near-infrared (VNIR) bands and 10 nm for the 180 short-wave infrared (SWIR) bands.