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Estimation of Water Transparency from Remote Sensing Data Based on A New Underwater Visibility Theory

Jiang, Dalin 筑波大学 DOI:10.15068/0000055096

2020.07.22

概要

The water transparency, is a direct record of water optics and an important indicator of water quality. It is measured by the Secchi disk depth(ZSD) and has been a routine measurement in field survey of aquatic environments since the 1860s. Over a century later, the remote sensing technique has also been widely used for retrieving the ZSD values because of the technique's large area coverage and rapid data acquisition.

Lee et al. (2015) proposed a new theory for underwater visibility. Based on this new theory, Lee et al. (2015) also developed a semi-analytical algorithm for retrieving the ZSD from remote sensing data (termed as Lee15 hereafter). Generally, there are three requirements for accurately estimating ZSD when the Lee15 algorithm is used: (1) accurate measurements of remote sensing reflectance (Rrs); (2) accurate estimations of diffuse attenuation coefficient of downwelling irradiance (Kd); and (3) the proper values of KT/Kd ratios (KT is the diffuse attenuation coefficient of upwelling radiance).

However, challenges are still remained for each requirement. They are: (1)the widely used above-water approach for measuring Rrs suffers from the residual reflected skylight (Δ) effect, which will influence the accuracy of in situ Rrs; (2) the sixth version of quasi-analytical algorithm (QAA_v6) used for estimating absorption and backscattering coefficients (a and bb) often failed in turbid inland waters, and thus reduced the estimation accuracy of Kd; and (3) the KT/Kd ratio has been reported to be varied in a wide range. Therefore, the errors in Rrs, Kd and KT/Kd ratios will be finally propagated to the estimations of the ZSD.

To address the above challenges, first, I proposed a new simple method, which only required the in situ Rrs spectrum itself as input, to further remove the Δ effects in the in situ Rrs measurements. The performance of the proposed method was evaluated using both simulation data and in situ Rrs spectra measured using a radiance sensor with a black tube (Δ-free in situ Rrs). The results showed that the proposed method outperformed other existing methods and can be applied to various types of waters. In addition, the proposed method can improve the quality of Rrs spectra collected under various sky conditions (e.g., clear, scattered clouds and overcast). Second, I proposed a hybrid quasi-analytical algorithm (QAA_hybrid) instead of the QAA_v6 for retrieving more accurate a and bb even in turbid inland waters. Third, I proposed a new algorithm to estimate a dynamic KT/Kd ratio instead of using the fixed ratio in the original algorithm. The results obtained from in situ Rrs showed that the improved ZSD estimation algorithm gave more accurate ZSD estimations, with the root mean square error (RMSE) reduced from 2 m to 1.7 m, mean absolute percentage error (MAPE) reduced from 54 % to 35 % (N=472, ZSD ranges from 0.2 m to 45 m).

The improved ZSD estimation algorithm was applied to the 2003–2012 MERIS images for six Japanese lakes (i.e. Lakes Mashu, Kussharo, Inawashiro, Kasumigaura, Suwa and Biwa). The results obtained from 66 matchups demonstrated that the improved ZSD algorithm estimated ZSD values from MERIS data with the RMSE of 2 m and the MAPE of 38%, even though some overestimations were still observed in Lake Biwa due mainly to the imperfect atmospheric correction for satellite images. The satellite-derived long-term ZSD trends agreed well with that derived from the in situ long-term ZSD. The results indicate that the improved ZSD algorithm has good potential in monitoring ZSD from remote sensing data.

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