A Semianalytical Algorithm for Estimating Water Transparency in Different Optical Water Types from MERIS Data
A Semianalytical Algorithm for Estimating Water Transparency in Different Optical Water Types from MERIS Data
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DOI：
10.3390/rs14040868
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发表时间：
2022-02
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通讯作者：
A. Msusa;Dalin Jiang;B. Matsushita
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作者：
A. Msusa;Dalin Jiang;B. Matsushita
水体透明度（或赛奇盘深度：ZSD）是水质的关键参数；因此，定期监测非常重要。在本研究中，我们在2015年提出的新水下能见度理论的基础上，做出了四项努力来改进2019年开发的最先进的ZSD估计算法。这四项努力是：（1）将所有水体分为清澈（I型）、中度浑浊（II型）、高度浑浊（III型）或极度浑浊（IV型）水类型； (2)针对每种水类型选择不同的参考波长和相应的半分析模型； (3)基于水类型分类，采用估计模型来表示颗粒物后向散射系数的合理形状； (4) 限制每种水类型出现最小漫衰减系数 (Kdλ) 的可能波长范围。使用模拟数据集（N = 91,287，ZSD 值 0.01 至 44.68 m）和原位测量数据集（N = 305，ZSD 值 0.3 至 16.4 m）将所提出的 ZSD 估计算法的性能与原始最先进算法的性能进行比较。结果显示，模拟数据的平均绝对百分比误差 (MAPE) 从 116% 降低到 65%，现场数据从 32% 降低到 27%，显着改善。新算法很好地解决了先前算法中的异常值。我们使用从日本霞浦湖获取的中分辨率成像光谱仪 (MERIS) 图像进一步评估了开发的 ZSD 估计算法。从 19 场比赛中获得的结果表明，估计的 ZSD 与现场测量的 ZSD 匹配良好，MAPE 为 15%。由于其半解析特性，所开发的 ZSD 估计算法可能适用于不同的光学水类型。
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.