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Retrieving inherent optical properties for turbid inland waters: an improved quasi-analytical algorithm based on the linear spectral backscattering coefficient constraint

机译:检索浑浊的内陆水域的固有光学特性:基于线性光谱反向散射系数约束的改进的拟分析算法

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The inherent optical property is a significant bridge between the hyperspectral remote sensing data and water color and water quality parameters. Based on the water optical radiation transfer process and existing quasi-analytical algorithm (QAA), this study provides an improved algorithm, namely a linear spectral backscattering coefficient constraint quasi-analytical algorithm (LSBCC-QAA), suitable for the retrieval of inherent optical properties for turbid inland waters to address the deficiency of the QAA on the retrieval of inherent optical properties for turbid inland waters. LSBCC-QAA uses the water-leaving reflectance of the bands between 1600 and 1700 nm to estimate the water surface reflectance of the bands between 400 and 900 nm and selects 700~850 nm as the reference wavelengths to estimate the water backscattering coefficients, taking full advantage of the continuity of the backscattering coefficient spectrum. The preliminary validated results show that the particle absorption coefficient, particle backscattering coefficient and phytoplankton absorption coefficient retrieved by LSBCC-QAA are more consistent with the actual situation than those retrieved by the common QAA_v6 algorithm or QAA-Turbid algorithm. Compared with the measured particle diffuse attenuation coefficient, the error of the LSBCC-QAA retrieved particle diffuse attenuation coefficient ranges from 16.0% to 22.9%, and the average error is 18.4%.
机译:固有的光学特性是高光谱遥感数据与水彩和水质参数之间的重要桥梁。基于水的光辐射传输过程和现有的拟分析算法(QAA),本研究提供了一种改进的算法,即线性光谱反向散射系数约束拟分析算法(LSBCC-QAA),适用于固有光学特性的检索解决浑浊的内陆水域问题,以解决QAA在获取浑浊的内陆水域固有光学特性方面的不足。 LSBCC-QAA使用1600-1700 nm波段的出水反射率来估计400-900 nm波段的水面反射率,并选择700〜850 nm作为参考波长来估计水的反向散射系数,后向散射系数谱的连续性的优势。初步验证结果表明,与普通的QAA_v6算法或QAA-Turbid算法相比,LSBCC-QAA反演的粒子吸收系数,粒子后向散射系数和浮游植物吸收系数与实际情况更加吻合。与测得的颗粒扩散衰减系数相比,LSBCC-QAA检索到的颗粒扩散衰减系数的误差范围为16.0%至22.9%,平均误差为18.4%。

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