A Semianalytical Algorithm for Estimating Water Transparency in Different Optical Water Types from MERIS Data

Abstract

Water transparency (or Secchi disk depth: ZSD) is a key parameter of water quality; thus, it is very important to routinely monitor. In this study, we made four efforts to improve a state-of-the-art ZSD estimation algorithm that was developed in 2019 on the basis of a new underwater visibility theory proposed in 2015. The four efforts were: (1) classifying all water into clear (Type I), moderately turbid (Type II), highly turbid (Type III), or extremely turbid (Type IV) water types; (2) selecting different reference wavelengths and corresponding semianalytical models for each water type; (3) employing an estimation model to represent reasonable shapes for particulate backscattering coefficients based on the water type classification; and (4) constraining likely wavelength range at which the minimum diffuse attenuation coefficient (Kdλ) will occur for each water type. The performance of the proposed ZSD estimation algorithm was compared to that of the original state-of-the-art algorithm using a simulated dataset (N = 91,287, ZSD values 0.01 to 44.68 m) and an in situ measured dataset (N = 305, ZSD values 0.3 to 16.4 m). The results showed a significant improvement with a reduced mean absolute percentage error (MAPE) from 116% to 65% for simulated data and from 32% to 27% for in situ data. Outliers in the previous algorithm were well addressed in the new algorithm. We further evaluated the developed ZSD estimation algorithm using medium resolution imaging spectrometer (MERIS) images acquired from Lake Kasumigaura, Japan. The results obtained from 19 matchups revealed that the estimated ZSD matched well with the in situ measured ZSD, with a MAPE of 15%. The developed ZSD estimation algorithm can probably be applied to different optical water types due to its semianalytical features.

Patterns of the relationship between the Secchi disk depth and the optical characteristics of water in the Neva Estuary (Baltic Sea): the influence of environmental variables

“…For example, D sd ceased to be a useful indicator of chlorophyll a concentration in a eutrophic reservoir when it exceeded 100 mg/m 3 , but was a good predictor of chlorophyll concentration below this threshold (Golubkov and Golubkov, 2024). To address this issue, various algorithms are being developed to derive D sd from satellite data, aiming to interpret empirical data from diverse conditions in large-scale areas (Idris et al, 2022;Msusa et al, 2022;Roy and Das, 2022;Guo et al, 2022a). Nonetheless, field studies directly conducted on water bodies are still necessary to obtain empirical data from a variety of locations for further refinement of algorithms (Zhang et al, 2022).…”

Monitoring Water Clarity Using Landsat 8 Imagery in Jiaozhou Bay, China, From 2013 to 2022

“…For instance, Idris et al [7] utilized Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data to retrieve water clarity in the Eastern coast of the Malaysian Peninsula and the Malacca Strait and analyzed the seasonal and interannual variations in water clarity within this region. Msusa et al [8] developed a semi-analytical algorithm based on moderateresolution Medium Resolution Imaging Spectrometer (MERIS) satellite data and assessed its performance by utilizing MERIS images obtained over Kasumigaura Lake in Japan. Foad et al [9] 2 > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < utilized Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and MODIS data to assess water clarity in Lake Superior, Lake Michigan, and Lake Huron, and analyzed the interannual variations in water clarity from 1998 to 2012.…”