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

^{1}

^{2}

^{*}

^{†}

## Abstract

**:**

_{SD}) in turbid waters is essential in the monitoring the ecological environment of lakes. Using the field measured Z

_{SD}and the remote sensing reflectance (Rrs(λ)) data, a new semi-analytical algorithm (denoted as Z

_{SDZ}) for retrieving Z

_{SD}was developed from Rrs(λ), and it was applied to Geostationary Ocean Color Imager (GOCI) images in extremely turbid waters. Our results are as follows: (1) the Z

_{SDZ}performs well in estimating Z

_{SD}in turbid water bodies (0.15 m < Z

_{SD}< 2.5 m). By validating with the field measured data that were collected in four turbid inland lakes, the determination coefficient (R

^{2}) is determined to be 0.89, with a mean absolute square percentage error (MAPE) of 22.39%, and root mean square error (RMSE) of 0.24 m. (2) The Z

_{SDZ}improved the retrieval accuracy of Z

_{SD}in turbid waters and outperformed the existing semi-analytical schemes. (3) The developed algorithm and GOCI data are in order to map the hourly variation of Z

_{SD}in turbid inland waters, the GOCI-derived results reveal a significant spatiotemporal variation in our study region, which are significantly driven by wind forcing. This study can provide a new approach for estimating water transparency in turbid waters, offering important support for the management of inland waters.

## 1. Introduction

_{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].

_{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].

_{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.

_{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.

_{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.

## 2. Materials and Methods

#### 2.1. Study Area

^{2}, ~2338 km

^{2}, and ~1577 km

^{2}, respectively (Figure 1). Lake Erhai is the second largest freshwater lake in Yunnan Province. Lake Dongting has a mean water depth of 1.9 m and a maximum depth of ~5.5 m. Lake Taihu has a mean water depth of 1.9 m and a maximum depth of ~2.6 m. Lake Hongze has a mean water depth of 1.8 m and a maximum depth of ~4.5 m. Lake Erhai has a mean water depth of 10 m and maximum depth of ~20 m. Over the past few decades, because of environmental overload from catchments, the aquatic ecosystems of lakes have faced water quality deterioration and reduced biodiversity. Blue-green algae outbreaks in these lakes seriously affect aquatic biodiversity, tourism, and the health of nearby residents. Lake Taihu is characterized by hypereutrophic and high turbidity due to high levels of primary production and high concentrations of suspended sediments; Lake Hongze is characterized by mesotrophic and high turbidity due to a large amount of sediment and terrestrial debris being carried from the Huai River and human activities [20]. Similar to the above two lakes, Lake Dongting has also experienced eutrophication in recent years due to anthropogenic activities and environmental change [21].

#### 2.2. In-Situ Water Quality Data and Spectra Data Collection

^{2}of non-linear fit regressions was greater than 0.95 and the number of depths must be no less than 3 [24].

_{SD}) measurements and water samples were simultaneously collected at each sampling site. The Secchi disk is lowered into a body of water by an observer until it disappears from view; the depth at which it is no longer visible is recorded as the Z

_{SD}value. One skilled person carried out all of the measurements in order to decrease artificial error. At the same time, in situ water samples were collected using Niskin bottles at a depth of 50 cm under the surface and were kept in containers at −20 °C for laboratory analysis. Whthin the next five days, the water samples were filtered and measured in the laboratory. The measured parameters of water samples in laboratory analysis included chlorophyll-a (Chla), total suspended matter (TSM), and organic/ inorganic suspended matter (OSM/ISM) concentrations, which were determined using the method that was described by Chen et al. [25]. Note that Lake Erhai has not field measured ${K}_{d}\left(\lambda \right)$ data used in this study. Finally, the total datasets of 232 samples were divided into two groups, one of which consisted of 137 samples, approximately 58% of the total number of samples, was used to calibrate the model; the remaining group was used to validate the accuracy of the model.

#### 2.3. Satellite Data and Preprocessing

_{SD}in Lake Hongze and Lake Taihu. During the cruise campaigns that were conducted from 2014 to 2018, the measurement time of a total of 125 points (69 points in Lake Hongze and 56 in Lake Taihu) was close to GOCI imaging time (± 1 h). These sampling points were selected for further validation of the accuracy of 6S atmospheric correction.

#### 2.4. Wind Speed Data Collection

#### 2.5. Data analyses and Accuracy Assessment

^{2}), the mean absolute square percentage error (MAPE), and root mean square error (RMSE), were selected to characterize the performance of the model:

#### 2.6. Z_{SDZ} Algorithm

_{SDZ}) that is suitable for estimating Z

_{SD}in inland turbid waters was developed based on the original Z

_{SDV6}by Lee et al. [10] and modified based on the in-situ data. Table 1 lists the derivation flowchart of Z

_{SDZ}algorithm and the corresponding expressions. Two parts (part I and part II) are contained in the new algorithm.

#### 2.6.1. Part I

_{SDZ}algorithm, which were suggested for high scattering waters by [15].

_{SDV6}algorithm that was proposed by Lee et al. [10], $a\left({\lambda}_{0}\right)$ is the absorption of dissolved components and detritus at original reference wavelength (550 nm or 670 nm). However, poor accuracy can be obtained by deriving $\u25b3a\left(550\right)$ or $\u25b3a\left(670\right)$, and further leading to poorer performance of $a\left({\lambda}_{0}\right)$ in turbid waterbodies. Moreover, an ambiguous extrapolation index of the aerosol model usually leads to the poor estimation of ${R}_{rs}$ from satellite images at short wavelengths in highly turbid inland waters [27]. These defects will propagate to the next steps and affect the accuracy of the model. One way to overcome this shortcoming is to first shift the reference wavelength to longer bands [6,28]. There are two longer NIR (745 nm and 845 nm) that meet the above conditions given the band setting of the GOCI image. However, poor performance of atmospheric correction existed at 865nm. Therefore, in the modified algorithm of Z

_{SDZ}, the reference wavelength of $a\left({\lambda}_{0}\right)$ will be shifted to 745 nm, and the $a\left({\lambda}_{0}\right)$ is considered to be equal to the absorption coefficient of pure water at 745 nm, ${a}_{\mathrm{w}}\left(745\right)$. Samplings of 745 nm of GOCI are determined in this step, other sensors with longer NIR band, such as the 740 nm of Sentinel-2, can also be selected as reference wavelength when applying the Z

_{SDZ}to other study areas.

_{SDV6}algorithm, the original values of parameters (m0~3) were determined while using Hydrolight simulations on oligotrophic waters and Case-1 model of Morel et al. [29] for optical properties, where the input inherent optical properties (IOPs) were kept vertically constant [30]. Therefore, parameters (m0~3) should be retuned for turbid waters. Parameters (m0~3) were retuned using our calibration data (137 samples) in order to meet the application of turbid waters, similar to Lee et al. [30]. In detail, we fixed the same values for m1, m2, and m3, but varied m0, and the optimal value of m0 was further confirmed based on nonlinear best fit. The values of m1~m3 were obtained in the same way. The final values of the four model parameters (m0~3) were 0.0124, 3.16, 0.52, and 10.8, respectively. After retuning, Figure 2A presents the match up points between the field measured and the retrieved ${K}_{d}\left(745\right)$. All of the points were closely distributed along the 1:1 line with low MAPE (10.24%) and RMSE (0.81 m

^{−1}), which suggests that the retuned values of parameters (m0~3) are acceptable for estimation of ${K}_{d}\left(745\right)$.

#### 2.5.2. Part II

_{SD}is derived based on part I:

^{2}= 0.89). After that, the values of $b$ and $c$ in Equations (2)–(10) are 0.99 and −1.96, respectively. The ${R}_{rs}^{tr}$ is the remote-sensing reflectance (sr−1) of the maximum transparency at the transparent window of the water body, and ${R}_{rs}^{tr}$ was taken as the ${R}_{rs}$ value that corresponds to the wavelength with minimum ${K}_{d}$ [10].

#### 2.7. Noise-Equivalent Z_{SD}

_{SD}can be estimated from $N{E}_{\rho}$ while using our developed algorithm.

## 3. Results

#### 3.1. Biogeochemical and Optical Characterization

_{SD}were more than 15 times larger than the minimum values. The Chla, TSM, and Z

_{SD}were 1.39–149.32 μg/L, 7.18–193.33 mg/L, and 0.1–0.8 m, respectively. In Lake Hongze, the share of OSM in TSM is smaller than that of ISM, which demonstrated that ISM dominated the TSM in Lake Hongze. In Lake Taihu, the Chla, TSM, and Z

_{SD}were 9.23–301.93 μg/L, 8.66–96.47 mg/L, and 0.14–1.1 mg/L, respectively. The maximum values of Chla, TSM and Z

_{SD}were 32, 11, and 7 fold larger than the minimum values. The ratio of OSM to TSM (OSM / TSM > 0.5) indicates that suspended particulates in Lake Taihu are mainly dominated by organic suspended particulates. In Lake Dongting, the Chla, TSM and Z

_{SD}were 2.79–52.08 μg/L, 3.75–200.53 mg/L and 0.15–1.05 mg/L, respectively. In Lake Erhai, the Chla, TSM and Z

_{SD}were 7.27–34.34 μg/L, 1.96–7.5 mg/L, and 0.15–4 mg/L, respectively. Overall, Lake Hongze, Lake Taihu, Lake Dongting, and Lake Erhai showed high levels of suspended matter and Chla, but low Z

_{SD}, which showed huge concentration differences with other inland waterbodies [4,33,34,35], indicating that Lake Hongze and Lake Taihu have the characteristics of highly turbid waters.

#### 3.2. Algorithm Validation and Noise-Equivalent Z_{SD}

_{SD}. Note that only 48 validated samples have field measured ${K}_{d}\left(\lambda \right)$ data. The in-situ measured and corresponding retrieved values of ${K}_{d}\left(555\right)$ and Z

_{SD}were distributed well along the 1:1 line (Figure 4). The MAPE and RMSE of match-up points between the measured and retrieved ${K}_{d}\left(555\right)$ were 11.31% and 0.63 m

^{−1}, respectively (Figure 4A). This suggests that the modified steps in the new algorithm (step 3, 5, and 6) performed well with satisfactory accuracy and had great potential to further estimate the Z

_{SD}in turbid lakes. The MAPE and RMSE of match-up points between the measured and retrieved Z

_{SD}were 22.39% and 0.24 m, respectively (Figure 4B), which demonstrates that the performance of the semi-analytical algorithm of Z

_{SD}had great potential for estimating Z

_{SD}in satellite images.

_{SD}, but also on the quality of remotely sensed imagery. Some previous studies [31,36] showed significant differences of imagery quality in the difference sensors between open sea and inland turbid waters, raising the question of whether GOCI imagery are suitable for the algorithm that we proposed for mapping Z

_{SD}. Finally, the noise-equivalent Z

_{SD}of 0.084 m was calculated based on our developed algorithm. When considering its well noise-equivalent Z

_{SD}, GOCI imagery can be used for Z

_{SD}estimation while using our developed algorithm in extremely turbid inland waters, such as Lake Taihu and Lake Hongze.

#### 3.3. Atmospheric Correction Assessment by Synchronized Images

_{SD}deriving algorithm were evaluated by comparing the GOCI-derived and the synchronized field measured Rrs at 443, 560, 665, 674, 709, and 754 nm (Figure 5), while the field measured reflectance obtained by ASD measurement were integrated while using the GOCI spectral response functions (SRF) to simulate the Rrs at the ground level. There are 42 synchronized in-situ samples in Lake Hongze and 30 samples in Lake Taihu.

_{SDZ}algorithm, the in situ measured Z

_{SD}and the corresponding retrieved values were close to the 1:1 line (Figure 5C). The MAPE and RMSE for the validation of the developed models were 29.07% and 0.12 m, respectively. This suggests that our proposed retrieval models that were based on GOCI data performed well.

#### 3.4. Mapping Z_{SD} from GOCI Based on Developed Algorithm

_{SD}based on our developed algorithm. The newly developed semi-analytical algorithm was applied to GOCI atmosphere corrected images in order to derive the hourly scale of Z

_{SD}in Lake Taihu and Lake Hongze (Figure 6). The Z

_{SD}retrieval results in the eastern part of Lake Taihu are not shown due to the impact of submerged vegetation and bottom reflectance. The hourly scale maps show the consecutive spatiotemporal dynamic characteristics of Z

_{SD}in both Lake Taihu and Hongze with low Z

_{SD}values within one day. Lake Taihu and Lake Hongze are shallow inland turbid waters, which are susceptible to environmental and anthropogenic factors, and they have significantly different characteristics from other inland waterbodies.

_{SD}was present from 8:30 to 15:30 in Lake Taihu. The spatiotemporal distribution of Z

_{SD}was significantly regulated by particulate suspended matter in Lake Taihu as a result of wind speed and wind direction [5,39]. The distribution of low Z

_{SD}is mostly concentrated in the southwest areas of Taihu Lake, whereas high values were observed in central open zones and lake bays (such as Meiliang Bay, as shown in Figure 1). Sheltered conditions and large amounts of submerged aquatic vegetation, which maintain relatively high transparency due to the water filtration, wind-induced wave attenuation, and the inhibition of sediment resupension by aquatic vegetation characterize these areas [5,39]. In conclusion, wind conditions and submerged aquatic vegetation can both regulate the distribution of Z

_{SD}in Lake Taihu. In addition, a key finding was that the Z

_{SD}value of Lake Taihu shows a distinct increasing trend from 8:30 in the morning to 15:30 in the afternoon, which is probably due to the continued weakening of wind speed from morning to afternoon.

_{SD}was observed in Lake Hongze than in Lake Taihu, which is, the transparency value showed a significant downward trend from 8:30 a.m. to 15:30 p.m. Temporally, the Z

_{SD}in the eastern and northern part of Lake Hongze go through a process from high to low, whereas the Z

_{SD}in the southwestern Lake remains low from 8:30 to 15:30. Spatially, the distribution of high Z

_{SD}is concentrated in the north and west of Lake Hongze, whereas the low values were observed in the southern region, especially in the upstream riverine region (Figure 6B). The headwater of Huai River accounts for more than 70% of the annual mean incoming runoff of Lake Hongze; the vigorous movement of water is one of main factors for the low transparency in these areas [19].

## 4. Discussion

#### 4.1. The Relationship between In-Situ Z_{SD} and Water Constituent Concentrations

_{SD}values were low, with 0.15–0.8 m in Lake Hongze and 0.15 m–1.0 m in Lake Taihu, indicating that extreme turbidity characterizes these two lakes. These findings were similar to those found in other turbid inland lakes [40,41], as well as previous studies in Lake Taihu [42]. However, the Z

_{SD}values in Lake Hongze and Lake Taihu are obviously lower than those for oceanic and coastal waters [10,11], and slightly turbid waters [4,6]. For example, the mean value of the field measured Z

_{SD}in Lake Hongze is six times lower than that for Lake Qiandao [4].

_{SD}[5,43]. Among these, the concentrations of main optical components (such as Chl-a and TSM) and their relative contributions to Z

_{SD}vary for different waters [4,5,8]. For example, in open marine waters, the optical properties are mainly dominated by phytoplankton and its attachments substantially contribute to the variability in Z

_{SD}. In contrast, particulate sediment and debris may have a significant effect on water clarity in turbid lakes [5].

_{SD}and TSM were found in both Lake Hongze and Lake Taihu (Figure 7A), indicating that Z

_{SD}has a tight relationship with TSM; more than 60% of the Z

_{SD}dynamic can be explained by the TSM in these two lakes. Relative higher correlation coefficients between Z

_{SD}and ISM were also found in Lake Hongze (R

^{2}= 0.60, p < 0.01) and Lake Taihu (R

^{2}= 0.59, p < 0.01), whereas low values between Z

_{SD}and OSM were in both lakes. Note that the coefficient in Lake Hongze (R

^{2}= 0.31, p < 0.01) is higher than that in Lake Taihu (R

^{2}= 0.08, p < 0.01), indicating that there are different water compositions between these two lakes. These results conclude that inorganic suspended matter (ISM), rather than organic suspended matter (OSM), plays an important role in water quality parameters that affects transparency. In addition, the relationships between field measured Z

_{SD}and field measured Chla in both lakes were poor (Figure 7C,D).

_{SD}values are characterized by high turbidity and high TSM. High TSM caused by sediment resuspension significantly decreased the water clarity [46]. Moreover, sediment resuspension can be induced by meteorological and hydrological events, such as rainstorms, strong winds, and human activities, such as sailing and trawling [47]. The intense water currents and sand mining activities in Lake Hongze cause sediment resuspension and reduce water transparency, especially in the estuarine part of the lake, according to the study by Lei et al. [20].

#### 4.2. Comparison with the Exist Z_{SD} Algorithms for GOCI

_{SD}in slightly turbid or turbid waters have been proposed [6,11,12]. These algorithms were selected for comparison with our developed model in this study (Table 3 and Figure 8). Model 4 (Z

_{SDV6}) was the original semi-analytical algorithm of Z

_{SD}, and this model was developed and validated with large data covering inland, marine, and coastal waters [10]. Model 1 (denoted as Z

_{SDM14}) and 2 (denoted as Z

_{SDR17}) were developed by parameterizing and calibrating for application in highly turbid coastal and slightly turbid waters [6,48].

^{2}= 0.89). Model 3 performed well for low Z

_{SD}, but it had an overestimation effect for high Z

_{SD}, which was consistent with a previous finding in slightly turbid waters [6]. Among the existing models, model 1 and 2 significantly underestimated Z

_{SD}, which suggested that the original Z

_{SD}algorithm that was proposed by Lee et al. [10] was more suitable than others in turbid inland waters. The results indicate that, although the three existing models can be used to estimate Z

_{SD}in turbid inland waters, their performance was highly impacted by the local parameters and parameterization of the inherent optical properties, the new proposed model showed the best estimation effect.

#### 4.3. Factors in the Z_{SD} Inversions

#### 4.3.1. Model Parameterization

_{SD}) in turbid lakes. The values of Z

_{SD}were underestimated or overestimated based on existing algorithms (Figure 6), which was probably due to these algorithms introducing larger uncertainty into the derived IOPs and further leading to the poorer performance of Z

_{SD}in turbid inland waters. Lee et al. [12] noticed the underestimated IOPs using original QAA for turbid waters and concluded that the larger absorption effect of particulates at short wavelengths results in the weak accuracy of the model. Although several modified versions have been proposed for slightly turbid and coastal waters [6,48], the uncertainty analysis of semi-analytical algorithms for deriving Z

_{SD}in extremely turbid lakes is seldom reported.

_{SD}in step 7.

#### 4.3.2. Measurement Uncertainties

_{SD}. The measurement of spectral data followed the above-water measurement method in this study. It is worth noting that the water-leaving radiance was obtained by correcting the total radiance of the water surface (${L}_{t}$) while using the coefficient of skylight reflectance (${r}_{aw}$), which depended on the water surface conditions that are caused by wind conditions [21]. Zheng et al. [21] suggested that ${r}_{aw}$= 0.022 for calm conditions, 0.025 for < 5 m/s wind speed, and 0.026–0.028 for 10 m/s wind speed. According to field measured speed data that we collected, low wind speeds are dominant in Lake Hongze and Lake Taihu, and ${r}_{aw}$= 0.022 was applied in this study. However, the variation of ${r}_{aw}$ would result in a high variation of mean coefficient variance value (CV = ~50%) of Rrs at the spectral range of 400 to 800 nm [15], which definitely has a negative impact on the performance of Z

_{SD}models. Moreover, although a reliable method was used to reduce sun glint and ensure the accurate measurement of reflectance, it is still possible for glint to remain. In addition, the inherent noise that is generated by the sensor is also an influencing factor.

_{SD}. The sensibility of using improved semi-analytical model of Z

_{SD}for the input parameters (Rrs(745) and Rrs(555)) were analyzed, the result indicated that this model is highly sensitive to the atmospheric corrected GOCI-derived Rrs (Figure 9). Therefore, the retrieved IOPs rely on the accuracy of corrected Rrs directly, indicating that an excellent atmospheric correction model with high accuracy is necessary.

#### 4.3.3. Limitation of Model in Retrieving Z_{SD}

_{SDZ}algorithm, one of the key steps (step 3 in Table 1) is to shift the reference wavelength to longer NIR band in order to satisfy an assumed condition that the ${a}_{\mathrm{w}}\left({\lambda}_{0}\right)$ is dominant in NIR wavelength. Additionally, the relatively high ${R}_{rs}\left({\lambda}_{0}\right)$ value in extremely turbid waters in NIR wavelength provides a higher signal-to-noise ratio to ensure the algorithm development. Conversely, for some slightly turbid waters or clear waters, a relatively low ${R}_{rs}\left({\lambda}_{0}\right)$ magnitude at longer NIR wavelength implies a low SNR, which might introduce large uncertainty to estimate $a\left({\lambda}_{0}\right)$. Although we do not have enough datasets to validate whether the developed algorithm can effectively estimate Z

_{SD}in slightly turbid or clear waters, caution should be exercised in applying the newly developed algorithm to the above waters. One empirical step (step 5 in Table 1) was constructed based on field measured data that we collected, which is location dependent and may not be suitable to other waters. Therefore, when our algorithm is applied to other water bodies, it might be necessary to reconstruct similar empirical equations based on the optical properties of water.

_{SD}model. Therefore, for waterbodies with a high spatial dynamic of bio-optical properties, the retrieval of IOPs and the estimation of Z

_{SD}may be compromised based on Z

_{SDZ}algorithm.

#### 4.4. The Response of GOCI-Derived Z_{SD} to Wind Speed

_{SD}and the corresponding wind speed. Strong relationships between GOCI-derived Z

_{SD}and wind speed are found in both Lake Hongze (R

^{2}= 0.57, p < 0.01) and Lake Taihu (R

^{2}= 0.63, p < 0.01), and the Z

_{SD}decreases with a reduction in wind speed, indicating that wind greatly affects the Z

_{SD}.

_{SD}and wind speed, wind speed was divided into three levels: low (0–3 m/s), medium (3–4 m/s), and high (> 4 m/s), according to the intensity of the wind [53]. When the wind speed was low, the Z

_{SD}varied from 0.08 m to 0.9 m with a mean value of 0.29 ± 0.21 m, and 84% of the variation of Z

_{SD}can be explained by wind speed. However, when the wind condition was medium or high, the Z

_{SD}ranged from 0.05 to 0.24 m with a mean value of 0.13 ± 0.05 m, and a weak relationship is observed between the wind speed and Z

_{SD}(Figure 10B). The results indicate that low wind speeds are dominant in Lake Taihu and Lake Hongze, which is good for the growth of submerged aquatic vegetation. However, an increase in Z

_{SD}is observed in both Lake Hongze and Lake Taihu, which might be related to anthropogenic activities [20,42].

## 5. Conclusions

_{SDZ}) was developed to estimate water clarity in turbid waters. When compared with original Z

_{SDV6}, the new method has three main extensions. Firstly, the reference wavelength for estimating $a\left({\lambda}_{0}\right)$ was relocated at 745 nm to reduce the adverse contribution from the optical components in extremely turbid waters. Second, four coefficients (m0 - m3) were modified based on field measured datasets to better estimate ${K}_{d}\left({\lambda}_{0}\right)$. Third, target ${K}_{d}\left(\lambda \right)$ was derived based on the good empirical relationship between ${K}_{d}\left(\lambda \right)$ and ${K}_{d}\left({\lambda}_{0}\right)$. The validation on a comprehensive field measured Z

_{SD}dataset (69 samples) demonstrated that Z

_{SDZ}improved the retrieval accuracy of Z

_{SD}in turbid waters and outperformed the three existing semi-analytical schemes. The hourly variation patterns of Z

_{SD}in Lake Hongze and Lake Taihu were also discovered after applying the Z

_{SDZ}to GOCI data. A distinct difference was presented in the spatiotemporal distribution pattern of Z

_{SD}between the two lakes. The transparency value showed a significant downward trend from 8:30 a.m. to 15:30 p.m. in Lake Hongze; conversely, a positive change in transparency was observed during the same time in Lake Taihu. Moreover, it has been found that wind is an important regulating factor in the spatiotemporal variation of Z

_{SD}. Our findings suggest that our proposed algorithm that is based on GOCI imagery can be suitable for capturing and analyzing the hourly variation and the dynamic characteristics of transparency of turbid waterbodies in a good manner, providing an important source of information on monitoring and mitigating the adverse effects on ecosystems.

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Bai, S.; Gao, J.; Sun, D.; Tian, M. Monitoring Water Transparency in Shallow and Eutrophic Lake Waters Based on GOCI Observations. Remote Sens.
**2020**, 12, 163. [Google Scholar] [CrossRef][Green Version] - Jiang, D.; Matsushita, B.; Setiawan, F.; Vundo, A. An improved algorithm for estimating the Secchi disk depth from remote sensing data based on the new underwater visibility theory. ISPRS J. Photogramm. Remote Sens.
**2019**, 152, 13–23. [Google Scholar] [CrossRef] - Al Kaabi, M.; Zhao, J.; Ghedira, H. MODIS-Based Mapping of Secchi Disk Depth Using a Qualitative Algorithm in the Shallow Arabian Gulf. Remote Sens.
**2016**, 8, 423. [Google Scholar] [CrossRef][Green Version] - Wu, Z.; Zhang, Y.; Zhou, Y.; Liu, M.; Shi, K.; Yu, Z. Seasonal-Spatial Distribution and Long-Term Variation of Transparency in Xin’anjiang Reservoir: Implications for Reservoir Management. Int. J. Environ. Res. Public Health
**2015**, 12, 9492–9507. [Google Scholar] [CrossRef] [PubMed] - Shi, K.; Zhang, Y.; Zhu, G.; Qin, B.; Pan, D. Deteriorating water clarity in shallow waters: Evidence from long term MODIS and in-situ observations. Int. J. Appl. Earth Obs. Geoinf.
**2018**, 68, 287–297. [Google Scholar] [CrossRef] - Rodrigues, T.; Alcântara, E.; Watanabe, F.; Imai, N. Retrieval of Secchi disk depth from a reservoir using a semi-analytical scheme. Remote Sens. Environ.
**2017**, 198, 213–228. [Google Scholar] [CrossRef][Green Version] - Fukushima, T.; Matsushita, B.; Yang, W.; Jaelani, L.M. Semi-analytical prediction of Secchi depth transparency in Lake Kasumigaura using MERIS data. Limnology
**2017**, 19, 89–100. [Google Scholar] [CrossRef] - Binding, C.E.; Greenberg, T.A.; Watson, S.B.; Rastin, S.; Gould, J. Long term water clarity changes in North America’s Great Lakes from multi-sensor satellite observations. Limnol. Oceanogr.
**2015**, 60, 1976–1995. [Google Scholar] [CrossRef] - Preisendorfer, R.W. Secchi disk science: Visual optics of natural waters. Limnol. Oceanogr.
**1986**, 31, 909. [Google Scholar] [CrossRef][Green Version] - Lee, Z.; Shang, S.; Hu, C.; Du, K.; Weidemann, A.; Hou, W.; Lin, J.; Lin, G. Secchi disk depth: A new theory and mechanistic model for underwater visibility. Remote Sens. Environ.
**2015**, 169, 139–149. [Google Scholar] [CrossRef][Green Version] - Shang, S.; Lee, Z.; Shi, L.; Lin, G.; Wei, G.; Li, X. Changes in water clarity of the Bohai Sea: Observations from MODIS. Remote Sens. Environ.
**2016**, 186, 22–31. [Google Scholar] [CrossRef][Green Version] - Lee, Z.; Shang, S.; Qi, L.; Yan, J.; Lin, G. A semi-analytical scheme to estimate Secchi-disk depth from Landsat-8 measurements. Remote Sens. Environ.
**2016**, 177, 101–106. [Google Scholar] [CrossRef] - Wang, Y.; Shen, F.; Sokoletsky, L.; Sun, X. Validation and Calibration of QAA Algorithm for CDOM Absorption Retrieval in the Changjiang (Yangtze) Estuarine and Coastal Waters. Remote Sens.
**2017**, 9, 1192. [Google Scholar] [CrossRef][Green Version] - Grunert, B.K.; Mouw, C.B.; Ciochetto, A.B. Deriving inherent optical properties from decomposition of hyperspectral non-water absorption. Remote Sens. Environ.
**2019**, 225, 193–206. [Google Scholar] [CrossRef][Green Version] - Xue, K.; Ma, R.; Duan, H.; Shen, M.; Boss, E.; Cao, Z. Inversion of inherent optical properties in optically complex waters using sentinel-3A/OLCI images: A case study using China’s three largest freshwater lakes. Remote Sens. Environ.
**2019**, 225, 328–346. [Google Scholar] [CrossRef] - Watanabe, F.; Mishra, D.R.; Astuti, I.; Rodrigues, T.; Alcântara, E.; Imai, N.N.; Barbosa, C. Parametrization and calibration of a quasi-analytical algorithm for tropical eutrophic waters. ISPRS J. Photogramm. Remote Sens.
**2016**, 121, 28–47. [Google Scholar] [CrossRef][Green Version] - Shen, F.; Zhou, Y.; Peng, X.; Chen, Y. Satellite multi-sensor mapping of suspended particulate matter in turbid estuarine and coastal ocean, China. Int. J. Remote Sens.
**2014**, 35, 4173–4192. [Google Scholar] [CrossRef] - Lou, X.; Hu, C. Diurnal changes of a harmful algal bloom in the East China Sea: Observations from GOCI. Remote Sens. Environ.
**2014**, 140, 562–572. [Google Scholar] [CrossRef] - Lei, S.; Xu, J.; Li, Y.; Du, C.; Liu, G.; Zheng, Z.; Xu, Y.; Lyu, H.; Mu, M.; Miao, S.; et al. An approach for retrieval of horizontal and vertical distribution of total suspended matter concentration from GOCI data over Lake Hongze. Sci. Total Environ.
**2019**, 700, 134524. [Google Scholar] [CrossRef] - Lei, S.; Xu, J.; Li, Y.; Lyu, H.; Liu, G.; Zheng, Z.; Xu, Y.; Du, C.; Zeng, S.; Wang, H.; et al. Temporal and spatial distribution of Kd(490) and its response to precipitation and wind in lake Hongze based on MODIS data. Ecol. Indic.
**2020**, 108, 105684. [Google Scholar] [CrossRef] - Zheng, Z.; Ren, J.; Li, Y.; Huang, C.; Liu, G.; Du, C.; Lyu, H. Remote sensing of diffuse attenuation coefficient patterns from Landsat 8 OLI imagery of turbid inland waters: A case study of Dongting Lake. Sci. Total Environ.
**2016**, 573, 39–54. [Google Scholar] [CrossRef] [PubMed] - Xu, J.; Lei, S.; Bi, S.; Li, Y.; Lyu, H.; Xu, J.; Xu, X.; Mu, M.; Miao, S.; Zeng, S.; et al. Tracking spatio-temporal dynamics of POC sources in eutrophic lakes by remote sensing. Water Res.
**2020**, 168, 115162. [Google Scholar] [CrossRef] [PubMed] - Lei, S.; Wu, D.; Li, Y.; Wang, Q.; Huang, C.; Liu, G.; Zheng, Z.; Du, C.; Mu, M.; Xu, J.; et al. Remote sensing monitoring of the suspended particle size in Hongze Lake based on GF-1 data. Int. J. Remote Sens.
**2018**, 40, 3179–3203. [Google Scholar] [CrossRef] - Zhang, Y.; Liu, X.; Yin, Y.; Wang, M.; Qin, B. A simple optical model to estimate diffuse attenuation coefficient of photosynthetically active radiation in an extremely turbid lake from surface reflectance. Opt. Express
**2012**, 20, 20482–20493. [Google Scholar] [CrossRef] - Chen, S.-L.; Zhang, G.-A.; Yang, S.-L.; Shi, J.Z. Temporal variations of fine suspended sediment concentration in the Changjiang River estuary and adjacent coastal waters, China. J. Hydrol.
**2006**, 331, 137–145. [Google Scholar] [CrossRef] - Lee, Z.; Lubac, B.; Werdell, J.; Arnone, R. An update of the quasi-analytical algorithm (QAA_v5). Int. Ocean Color Group Softw. Rep.
**2009**, 1–9. [Google Scholar] - Budhiman, S.; Suhyb Salama, M.; Vekerdy, Z.; Verhoef, W. Deriving optical properties of Mahakam Delta coastal waters, Indonesia using in situ measurements and ocean color model inversion. ISPRS J. Photogramm. Remote Sens.
**2012**, 68, 157–169. [Google Scholar] [CrossRef] - Lee, Z.; Ahn, Y.H.; Mobley, C.; Arnone, R. Removal of surface-reflected light for the measurement of remote-sensing reflectance from an above-surface platform. Opt. Express
**2010**, 18, 26313–26324. [Google Scholar] [CrossRef] - Morel, A.; Maritorena, S. Bio-optical properties of oceanic waters: A reappraisal. J. Geophys. Res. Ocean.
**2001**, 106, 7163–7180. [Google Scholar] [CrossRef][Green Version] - Lee, Z.; Hu, C.; Shang, S.; Du, K.; Lewis, M.; Arnone, R.; Brewin, R. Penetration of UV-visible solar radiation in the global oceans: Insights from ocean color remote sensing. J. Geophys. Res. Ocean.
**2013**, 118, 4241–4255. [Google Scholar] [CrossRef][Green Version] - Vanhellemont, Q.; Ruddick, K. Turbid wakes associated with offshore wind turbines observed with Landsat 8. Remote Sens. Environ.
**2014**, 145, 105–115. [Google Scholar] [CrossRef][Green Version] - Ryu, J.-H.; Han, H.-J.; Cho, S.; Park, Y.-J.; Ahn, Y.-H. Overview of geostationary ocean color imager (GOCI) and GOCI data processing system (GDPS). Ocean. Sci. J.
**2012**, 47, 223–233. [Google Scholar] [CrossRef] - Zhang, Y.; Zhang, Y.; Shi, K.; Zha, Y.; Zhou, Y.; Liu, M. A Landsat 8 OLI-Based, Semianalytical Model for Estimating the Total Suspended Matter Concentration in the Slightly Turbid Xin’anjiang Reservoir (China). IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2016**, 9, 398–413. [Google Scholar] [CrossRef] - Tang, X.; Wu, M.; Li, R. Phosphorus distribution and bioavailability dynamics in the mainstream water and surface sediment of the Three Gorges Reservoir between 2003 and 2010. Water Res.
**2018**, 145, 321–331. [Google Scholar] [CrossRef] [PubMed] - Volpe, V.; Silvestri, S.; Marani, M. Remote sensing retrieval of suspended sediment concentration in shallow waters. Remote Sens. Environ.
**2011**, 115, 44–54. [Google Scholar] [CrossRef] - Ren, J.; Zheng, Z.; Li, Y.; Lv, G.; Wang, Q.; Lyu, H.; Huang, C.; Liu, G.; Du, C.; Mu, M.; et al. Remote observation of water clarity patterns in Three Gorges Reservoir and Dongting Lake of China and their probable linkage to the Three Gorges Dam based on Landsat 8 imagery. Sci. Total Environ.
**2018**, 625, 1554–1566. [Google Scholar] [CrossRef] [PubMed] - Wang, M.H.; Shi, W.; Jiang, L.D. Atmospheric correction using near-infrared bands for satellite ocean color data processing in the turbid western Pacific region. Opt. Express
**2012**, 20, 741–753. [Google Scholar] [CrossRef] - Le, C.; Li, Y.; Zha, Y.; Sun, D.; Huang, C.; Lu, H. A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: The case of Taihu Lake, China. Remote Sens. Environ.
**2009**, 113, 1175–1182. [Google Scholar] [CrossRef] - Zhang, Y.L.; Shi, K.; Liu, J.J.; Deng, J.M.; Qin, B.Q.; Zhu, G.W.; Zhou, Y.Q. Meteorological and hydrological conditions driving the formation and disappearance of black blooms, an ecological disaster phenomena of eutrophication and algal blooms. Sci. Total Environ.
**2016**, 569, 1517–1529. [Google Scholar] [CrossRef] - Arst, H.; Nõges, T.; Nõges, P.; Paavel, B. Relations of phytoplankton in situ primary production, chlorophyll concentration and underwater irradiance in turbid lakes. Hydrobiologia
**2008**, 599, 169–176. [Google Scholar] [CrossRef] - Liu, J.; Sun, D.; Zhang, Y.; Li, Y. Pre-classification improves relationships between water clarity, light attenuation, and suspended particulates in turbid inland waters. Hydrobiologia
**2013**, 711, 71–86. [Google Scholar] [CrossRef] - Shi, K.; Zhang, Y.; Liu, X.; Wang, M.; Qin, B. Remote sensing of diffuse attenuation coefficient of photosynthetically active radiation in Lake Taihu using MERIS data. Remote Sens. Environ.
**2014**, 140, 365–377. [Google Scholar] [CrossRef] - Feng, L.; Hou, X.; Zheng, Y. Monitoring and understanding the water transparency changes of fifty large lakes on the Yangtze Plain based on long-term MODIS observations. Remote Sens. Environ.
**2019**, 221, 675–686. [Google Scholar] [CrossRef] - Miao, S.; Lyu, H.; Wang, Q.; Li, Y.; Wu, Z.; Du, C.; Xu, J.; Bi, S.; Mu, M.; Lei, S. Estimation of terrestrial humic-like substances in inland lakes based on the optical and fluorescence characteristics of chromophoric dissolved organic matter (CDOM) using OLCI images. Ecol. Indic.
**2019**, 101, 399–409. [Google Scholar] [CrossRef] - Wu, Y.; Li, L.; Gan, N.; Zheng, L.; Ma, H.; Shan, K.; Liu, J.; Xiao, B.; Song, L. Seasonal dynamics of water bloom-forming Microcystis morphospecies and the associated extracellular microcystin concentrations in large, shallow, eutrophic Dianchi Lake. J. Environ. Sci. (China)
**2014**, 26, 1921–1929. [Google Scholar] [CrossRef] - Shi, K.; Zhang, Y.; Zhu, G.; Liu, X.; Zhou, Y.; Xu, H.; Qin, B.; Liu, G.; Li, Y. Long-term remote monitoring of total suspended matter concentration in Lake Taihu using 250m MODIS-Aqua data. Remote Sens. Environ.
**2015**, 164, 43–56. [Google Scholar] [CrossRef] - Liu, X.; Zhang, Y.; Yin, Y.; Wang, M.; Qin, B. Wind and submerged aquatic vegetation influence bio-optical properties in large shallow Lake Taihu, China. J. Geophys. Res. Biogeosciences
**2013**, 118, 713–727. [Google Scholar] [CrossRef] - Mishra, S.; Mishra, D.R.; Lee, Z. Bio-Optical Inversion in Highly Turbid and Cyanobacteria-Dominated Waters. IEEE Trans. Geosci. Remote Sens.
**2014**, 52, 375–388. [Google Scholar] [CrossRef] - Röttgers, R.; Dupouy, C.; Taylor, B.B.; Bracher, A.; Woźniak, S.B. Mass-specific light absorption coefficients of natural aquatic particles in the near-infrared spectral region. Limnol. Oceanogr.
**2014**, 59, 1449–1460. Available online: https://aslopubs.onlinelibrary.wiley.com/doi/abs/10.4319/lo.2014.59.5.1449 (accessed on 18 March 2020). [CrossRef][Green Version] - Liu, G.; Li, L.; Song, K.; Li, Y.; Lyu, H.; Wen, Z.; Fang, C.; Bi, S.; Sun, X.; Wang, Z.; et al. An OLCI-based algorithm for semi-empirically partitioning absorption coefficient and estimating chlorophyll a concentration in various turbid case-2 waters. Remote Sens. Environ.
**2020**, 239, 111648. [Google Scholar] [CrossRef] - Ogashawara, I.; Mishra, D.R.; Nascimento, R.F.F.; Alcântara, E.H.; Kampel, M.; Stech, J.L. Re-parameterization of a quasi-analytical algorithm for colored dissolved organic matter dominant inland waters. Int. J. Appl. Earth Obs. Geoinf.
**2016**, 53, 128–145. [Google Scholar] [CrossRef][Green Version] - Lee, Z.; Carder, K.L.; Mobley, C.D.; Steward, R.G.; Patch, J.S. Hyperspectral remote sensing for shallow waters. 2. Deriving bottom depths and water properties by optimization. Appl. Opt.
**1999**, 38, 3831–3843. [Google Scholar] [CrossRef] [PubMed][Green Version] - Huang, C.; Shi, K.; Yang, H.; Li, Y.; Zhu, A.X.; Sun, D.; Xu, L.; Zou, J.; Chen, X. Satellite observation of hourly dynamic characteristics of algae with Geostationary Ocean Color Imager (GOCI) data in Lake Taihu. Remote Sens. Environ.
**2015**, 159, 278–287. [Google Scholar] [CrossRef]

**Figure 1.**The location of Lake Hongze (

**B**), Lake Dongting (

**C**), Lake Taihu (

**D**), and Lake Erhai (

**E**) in China (

**A**). The symbols represent the sampling date in year-month forma.

**Figure 2.**Comparison between measured and the model derived values of K

_{d}(745) (

**A**). The in-situ relationships between K

_{d}(745) and K

_{d}(555) (

**B**).

**Figure 3.**Average Rrs (

**A**) and K

_{d}(B) in Lake Hongze (HZ), Lake Taihu (TH), Lake Dongting (DT), and Lake Erhai (EH), respectively. The ${K}_{d}\left(555\right)$ is the minimum ${K}_{d}$ value within the visible domain for GOCI (

**B**).

**Figure 4.**Validation of the ${K}_{d}\left(555\right)$ (

**A**) and Z

_{SD}(

**B**) between the measured and predicted values. Applicability verification of the new algorithm in Lake Dongting.

**Figure 5.**The field measured reflectance corresponded to the Geostationary Ocean Color Imager (GOCI) bands for Lake Taihu (

**A**) and Lake Hongze (

**B**). The performance of new algorithm based on synchronized images (

**C**).

**Figure 6.**Hourly variations of GOCI-derived Z

_{SD}based on Z

_{SDZ}model in Lake Taihu (

**A**) and Lake Hongze (

**B**). The retrieval results of Z

_{SD}in the eastern part of Lake Taihu are not shown due to the dense distribution of aquatic vegetation (see Figure 1).

**Figure 7.**The relationship between measured Z

_{SD}and measured water quality parameters in Lake Hongze and Taihu: (

**A**) total suspended matter (TSM), (

**B**) inorganic suspended matter (ISM), (

**C**) organic suspended matter (OSM), and (

**D**) Chla.

**Figure 8.**Comparison of the measured and derived Z

_{SD}of the four Models. (

**A**) Z

_{SDM14}, (

**B**) Z

_{SDR17}, and (

**C**) Z

_{SDV6}were provided by previous studies, (

**D**) Z

_{SDZ}is provided by this study.

**Figure 9.**Variation of mean absolute square percentage error (MAPE) for Z

_{SD}, derived from the errors introduced by atmospheric correction.

**Figure 10.**The relationship between Z

_{SD}and wind speed (

**A**) and scatterplot of different levels of wind speed and Z

_{SD}(

**B**).

Step | Property | Expression | Approach |
---|---|---|---|

1 | ${r}_{rs}\left(\lambda \right)$ | ${r}_{rs}\left(\lambda \right)=\frac{{R}_{rs}\left(\lambda \right)}{0.52+1.7{R}_{rs}\left(\lambda \right)}$ | Semi-analytical |

2 | $u\left(\lambda \right)$ | $u\left(\lambda \right)=\frac{-\mathrm{g}0+\sqrt{{\left(\mathrm{g}0\right)}^{2}+4\mathrm{g}1\ast {r}_{rs}\left(\lambda \right)}}{2{\mathrm{g}}_{1}}$ $\mathrm{g}0=0.084,\mathrm{g}1=0.17$ | Semi-analytical |

3 | $a\left(745\right)$ | $a\left({\lambda}_{0}\right)={a}_{\mathrm{w}}\left({\lambda}_{0}\right)+\u25b3a\left({\lambda}_{0}\right)\approx {a}_{\mathrm{w}}\left({\lambda}_{0}\right)$ ${\lambda}_{0}=745$ | Empirical |

4 | ${b}_{b}\left(745\right)$ | ${b}_{b}\left({\lambda}_{0}\right)=\frac{u\left({\lambda}_{0}\right)a\left({\lambda}_{0}\right)}{1-u\left({\lambda}_{0}\right)}$, ${\lambda}_{0}=745$ | Analytical |

5 | ${K}_{d}\left({\lambda}_{0}\right)$ | ${K}_{d}\left({\lambda}_{0}\right)=\left(1+{\mathrm{m}}_{0}{\ast \mathsf{\theta}}_{\mathrm{s}}\right)a\left({\lambda}_{0}\right)+$ ${\mathrm{m}}_{1}\left(1-{\mathrm{m}}_{2}\mathrm{exp}\left(-{\mathrm{m}}_{3}a\left({\lambda}_{0}\right)\right)\right){b}_{b}\left({\lambda}_{0}\right)$ ${\mathrm{m}}_{0}=0.0124,{\mathrm{m}}_{1}=3.16,{\mathrm{m}}_{2}=0.52,{\mathrm{m}}_{3}=10.8$ | Semi-analytical |

6 | ${K}_{d}\left(555\right)$ | ${K}_{d}\left(555\right)=b\ast {K}_{d}\left(745\right)+c$ $b=0.99,c=-1.96$ | Empirical |

7 | ${\mathrm{Z}}_{\mathrm{SD}}$ | ${Z}_{SD}=\frac{1}{2.5\mathrm{Min}\left({K}_{d}^{tr}\right)}\mathrm{ln}(\frac{\left|0.14-{R}_{rs}^{tr}\right|}{0.013})$ $\mathrm{Min}\left({K}_{d}^{tr}\right)={K}_{d}\left(555\right),{R}_{rs}^{tr}={R}_{rs}\left(555\right)$ | Semi-analytical |

**Table 2.**Descriptive statistics from Lake Hongze (HZ), Lake Taihu (TH), Lake Dongting, and Lake Erhai (EH).

Parameters | Statistics | HZ(N = 87) | TH(N = 58) | DT(N = 40) | EH(N = 47) |
---|---|---|---|---|---|

Chla (μg/L) | Min–Max | 1.39–149 | 9.23–301.93 | 2.79–52.08 | 7.27–34.34 |

Aver ± SD | 13.67 ± 19.5 | 57.44 ± 46.3 | 16.08 ± 12.4 | 14.74 ± 5.51 | |

CV(%) | 142.64 | 80.61 | 77.61 | 37.36 | |

TSM (mg/L) | Min–Max | 7.18–193.33 | 8.66–96.47 | 3.75–200.53 | 1.96–7.5 |

Aver ± SD | 58.22 ± 31.77 | 33.16 ± 22.6 | 52.44 ± 46.5 | 3.91 ± 1.01 | |

CV(%) | 54.57 | 68.15 | 88.62 | 25.82 | |

ISM (mg/L) | Min–Max | 5.45–174.16 | 4.22–79.41 | 1.65–182.93 | 0.15–4 |

Aver ± SD | 50.48 ± 29.28 | 19.68 ± 17.84 | 45.36 ± 44.16 | 2.89 ± 0.82 | |

CV(%) | 58.01 | 90.66 | 97.35 | 66.77 | |

OSM (mg/L) | Min–Max | 1.23–35.07 | 3.44–59.85 | 2.1–19.46 | 1.83–5.3 |

Aver ± SD | 7.73 ± 4.59 | 13.47 ± 9.15 | 7.11 ± 3.76 | 2.89 ± 0.82 | |

CV(%) | 59.37 | 67.91 | 52.93 | 28.61 | |

Z_{SD} (m) | Min-Max | 0.15–0.8 | 0.15–1.0 | 0.15–1.05 | 0.9–2.48 |

Aver ± SD | 0.28 ± 0.13 | 0.43 ± 0.24 | 0.5 ± 0.25 | 1.56 ± 0.34 | |

CV(%) | 72.43 | 56 | 51.3 | 22.37 | |

OSM/TSM | Min–Max | 0.03–0.47 | 0.16–0.81 | 0.05–0.61 | 0.03–0.53 |

Aver ± SD | 0.14 ± 0.07 | 0.46 ± 0.16 | 0.19 ± 0.12 | 0.27 ± 0.13 | |

CV(%) | 49.46 | 36.16 | 63.57 | 47.64 |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Zeng, S.; Lei, S.; Li, Y.; Lyu, H.; Xu, J.; Dong, X.; Wang, R.; Yang, Z.; Li, J.
Retrieval of Secchi Disk Depth in Turbid Lakes from GOCI Based on a New Semi-Analytical Algorithm. *Remote Sens.* **2020**, *12*, 1516.
https://doi.org/10.3390/rs12091516

**AMA Style**

Zeng S, Lei S, Li Y, Lyu H, Xu J, Dong X, Wang R, Yang Z, Li J.
Retrieval of Secchi Disk Depth in Turbid Lakes from GOCI Based on a New Semi-Analytical Algorithm. *Remote Sensing*. 2020; 12(9):1516.
https://doi.org/10.3390/rs12091516

**Chicago/Turabian Style**

Zeng, Shuai, Shaohua Lei, Yunmei Li, Heng Lyu, Jiafeng Xu, Xianzhang Dong, Rui Wang, Ziqian Yang, and Jianchao Li.
2020. "Retrieval of Secchi Disk Depth in Turbid Lakes from GOCI Based on a New Semi-Analytical Algorithm" *Remote Sensing* 12, no. 9: 1516.
https://doi.org/10.3390/rs12091516