## 1. Introduction

Water transparency, which has been widely and regularly measured with Secchi disk in both limnology and oceanography, is key information for assessing the water quality and trophic state. Secchi disk depth (Z

_{SD}, m) is the depth at which the disk, lowered from the surface, is no longer visible to the observer, and is a measure of transparency or vertical visibility in the water body. The values of Z

_{SD} depend on the intensity of light, water molecules, and optical components (e.g., chlorophyll-a and suspended matter), and they play a key role in understanding aquatic environment variations and biogeochemical processes [

1]. Traditional methods of estimating Z

_{SD} largely depend on cruise surveys, which are laborious and time-consuming. The large coverage characteristics of satellite data makes it a perfect tool for describing the spatial and temporal variability of Z

_{SD} [

2].

Substantial efforts, which are based on remote sensing methods, have been made to obtain Z

_{SD} for describing the spatiotemporal variability of water properties in marine, coastal, and inland waters [

3,

4,

5,

6,

7]. They can be mainly divided into two strategies: empirical and semi-analytical approaches. (1) empirical methods: Binding et al. [

8] used remote-sensing reflectance at 550 nm to estimate Z

_{SD} in Great Lakes based on multi-source satellite data. Shi et al. [

5] also estimated Z

_{SD} in Lake Taihu by constructing a linear regression model between Z

_{SD} and MODIS-Aqua reflectance at 645 nm. However, the empirical methods have the characteristic of location-dependency and may not be portable to other waters. (2) semi-analytical methods: Preisendorfer et al. proposed a semi-analytical approach for estimating inherent optical properties (IOPs) [

9] based on classical underwater visibility theory; this approach has been applied to estimate Z

_{SD} [

7]; however, the results from this approach did not agree well with field observations and human experiences, and a new semi-analytical model (denoted as Z

_{SDV6}) based on the radiative transfer theory was proposed by Lee et al. [

10] to retrieve Z

_{SD}. Shang et al. subsequently verified the encouraging performance of the new method [

11].

This new model was developed and validated with a large data covering different IOPs conditions, including marine, coastal, and inland waters (Z

_{SD} range of 0.1–30 m), and received excellent results (~18% average absolute difference, R

^{2} = 0.96) [

12]. By this method, the inherent optical properties (IOPs) were first estimated while using the quasi-analytical algorithm (QAA), and then positing the reference wavelength at 550 or 670 nm further derived the Z

_{SD}. However, several obvious limitations of the original QAA become evident for applications in inland turbid waters [

6,

13,

14,

15,

16]. Firstly, the estimation method of the total absorption coefficient at the reference wavelength (550 or 670 nm) does not work in inland turbid waters with lower absorption, but high scattering coefficients; secondly, the original coefficients of the IOPs were calibrated while using the data from marine and coastal waters and inland waters. The Z

_{SD} in turbid water is much lower than in oceanic water or coastal waters, and so the Z

_{SD} model must be reevaluated for these turbid conditions. Moreover, vertical heterogeneous distribution of water constituents and complex optical properties in turbid lakes challenge the validation of parameters in the semi-analytical model of Z

_{SD} that was proposed by Lee et al. [

10]. Therefore, an improved semi-analytical algorithm is needed to retrieve Z

_{SD} in turbid waters.

The Geostationary Ocean Color Imager (GOCI), an ocean color satellite imager that has been set in geostationary orbit, provides eight images during the daytime from 8:30 to 16:30 local time. With its eight bands from visible to near-infrared spectral region (412~865 nm) and one hourly short temporal resolutions, GOCI has been confirmed as an ideal satellite sensor with high signal-to-noise ratios (SNR) for mapping suspended particulate matter, colored dissolved organic matter (CDOM), and harmful algal blooms [

13,

17,

18,

19]. Although the application of GOCI data for Z

_{SD} estimation has seldom been reported in inland turbid waters, it could be of great significance for the observation of Z

_{SD} in inland turbid lakes.

The Z_{SD} of Lake Taihu and Lake Hongze were estimated based on in-situ data and GOCI images to improve the algorithm of retrieving Z_{SD} in inland turbid water. The main objectives of this study were: (1) to develop a new semi-analytical algorithm of Z_{SD} for inland turbid waters, (2) compare the performance of the new scheme with the existing semi-analytical algorithm, and (3) to obtain the spatiotemporal dynamic characteristics of Z_{SD} from GOCI observations.