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

Water transparency (or Secchi disk depth: Z(SD)) 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 Z(SD) 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 (K-d(lambda)) will occur for each water type. The performance of the proposed Z(SD) estimation algorithm was compared to that of the original state-of-the-art algorithm using a simulated dataset (N = 91,287, Z(SD) values 0.01 to 44.68 m) and an in situ measured dataset (N = 305, Z(SD) 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 Z(SD) estimation algorithm using medium resolution imaging spectrometer (MERIS) images acquired from Lake Kasumigaura, Japan. The results obtained from 19 matchups revealed that the estimated Z(SD) matched well with the in situ measured Z(SD), with a MAPE of 15%. The developed Z(SD) estimation algorithm can probably be applied to different optical water types due to its semianalytical features.

Automated summary

This study improved a Z(SD) estimation algorithm based on a new underwater visibility theory. The algorithm was shown to have significant improvement in accuracy compared to the original algorithm, as demonstrated by simulated and in situ data. The algorithm was further evaluated using imaging spectrometer images, with promising results in estimating Z(SD) for different optical water types.