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Characterization and modeling of bio-optical properties of water in a lentic ecosystem using in situ Hyperspectral remote sensing

机译:利用原位高光谱遥感表征和建立透镜体生态系统中水的生物光学特性

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Hyperspectral remote sensing has shown great promise in characterizing and monitoring of optical properties of water. This study aims at characterizing the spectral reflectance and to develop hyperspectral algorithms for retrieval of bio-optical properties of Bhindawas wetland, a man-made lake in Haryana, India. The spectral reflectance of the lake was measured using SVC GER 1500 Spectroradiometer and water samples were collected from different sampling sites within the lake during three different field surveys in 2014. Characterization of spectral responses was carried out using principal component analysis and Canonical Correspondence Analysis (CCA). It revealed that the dataset was typical of Case Ⅱ waters by extracting two principal components that explained around 99% of the variation, and CCA identified that different optical parameters such as TSS, TOC, Chla and turbidity delineate its spectral response. Water quality results were correlated with reflectance to determine their relationships. Furthermore, multiple linear regression was used to derive the two and three band model for retrieval of TSS, Chla and Turbidity concentration for lake. Retrieval algorithms with significant accuracy were developed for Chla (R~2=0.80, RMSE=0.12μg/l), TSS (R~2=0.86, RMSE=59.1mg/l) and Turbidity (R~2=0.84, RMSE=13NTU).
机译:高光谱遥感在表征和监测水的光学特性方面显示出巨大的希望。这项研究旨在表征光谱反射率,并开发高光谱算法来检索印度哈里亚纳邦人工湖Bhindawas湿地的生物光学特性。使用SVC GER 1500分光光度计测量了湖泊的光谱反射率,并在2014年的三个不同的野外调查中从湖泊内的不同采样点收集了水样本。光谱响应的表征使用主成分分析和规范对应分析(CCA)进行。 )。通过提取两个主要成分解释了约99%的变化,表明该数据集是CaseⅡ水域的典型数据,CCA识别出不同的光学参数(如TSS,TOC,Chla和浊度)描述了其光谱响应。将水质结果与反射率关联起来,以确定它们之间的关系。此外,采用多元线性回归推导了两波段和三波段模型,用于湖泊的TSS,Chla和浊度浓度的检索。针对Chla(R〜2 = 0.80,RMSE =0.12μg/ l),TSS(R〜2 = 0.86,RMSE = 59.1mg / l)和浊度(R〜2 = 0.84,RMSE = 13 NTU)。

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