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

Abstract

The accurate remote estimation of the Secchi disk depth (ZSD) in turbid waters is essential in the monitoring the ecological environment of lakes. Using the field measured ZSD and the remote sensing reflectance (Rrs(λ)) data, a new semi-analytical algorithm (denoted as ZSDZ) for retrieving ZSD 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 ZSDZ performs well in estimating ZSD in turbid water bodies (0.15 m < ZSD < 2.5 m). By validating with the field measured data that were collected in four turbid inland lakes, the determination coefficient (R2) 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 ZSDZ improved the retrieval accuracy of ZSD 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 ZSD 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.

A Systematic Review of the Application of GOCI to Water Quality Monitoring of Inland and Coastal Waters

“…𝐾 𝑑 (745) = (1 + 0.0124 * 𝜃 𝑠 ) * 𝑎 𝑤 (745) + 3.16 * (1 − 0.52 * exp(−10.8 * 𝑎 𝑤 (745))) * 𝑏 𝑏 (745) (25) where 𝜃 𝑠 represents the SZA. [182] found that the 𝑀𝑖𝑛(𝐾 𝑑 𝑡𝑟 ) is at 555 nm in Lake Taihu and Lake…”

A Systematic Review of the Application of the Geostationary Ocean Color Imager to the Water Quality Monitoring of Inland and Coastal Waters

“…Bai [167] used a linear relationship to correct SDD Lee (denoted as SDD Bai ) and applied this algorithm to the GOCI in Lake Taihu with as Equation (24). Zeng [181] proposed an improved Quasi-Analytical Algorithm (QAA)-estimated SDD algorithm (denoted as SDD Zeng ) for the GOCI using in situ data collected in Lake Taihu, Lake Hongze, Lake Dongting, and Lake Erhai. SDD Zeng firstly estimated b b (745) using QAA with an assumption of the pure water absorption (a w (745)), which is dominated at this band (a(745) ≈ a w (745)).…”

“…where θ s represents the SZA. Zeng [181] found that the Min K tr d is at 555 nm in Lake Taihu and Lake Hongze. Thus, a linear relationship was built to convert K d (745) to K d (555).…”

Long-Term Dynamics of Chlorophyll-a Concentration and Its Response to Human and Natural Factors in Lake Taihu Based on MODIS Data

“…With a modest spatial resolution of 250 m and fine revisit frequency of 1 day, MODIS-Aqua images are widely believed to represent superior satellite data for inland water quality variable estimation [39][40][41]. To ensure a high quality for latter applications, images containing significant clouds, thick aerosols, and sun glint were excluded and subsequently preprocessed to generate reflectance products, following the methods in prior studies [17,42]. In addition, the floating algae index (FAI) developed by Hu [43] was applied to capture and mask the image pixels with surface cyanobacterial blooms areas.…”