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Spectral Analysis of Water Reflectance for Hyperspectral Remote Sensing of Water Quailty in Estuarine Water

机译:河口水水质高光谱遥感水反射光谱分析

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Hyperspectral remote sensing offers an effective approach for frequent, synoptic water quality measurements over a large spatial extent. However, the optical complexity of case 2 water makes the water quality monitoring by remote sensing in estuarine water a challenge. The prime objective of this study was to develop algorithms for hyperspectral remote sensing of water quality based on in situ spectral measurement of water reflectance. In this study, water reflectance spectra R(λ) were acquired by a pair of Ocean Optic 2000 spectroradiometers during the summers from 2008 to 2011 at Patuxent River, a tributary of Chesapeake Bay, USA. Simultaneously, concentrations of chlorophyll a and total suspended solids (TSS), as well as absorption of colored dissolved organic matter (CDOM) were measured. Empirical models that based on spectral features of water reflectance generally showed good correlations with water quality parameters. The retrieval model that using spectral bands at red/NIR showed a high correlation with chlorophyll a concentration (R2 = 0.81). The ratio of green to blue spectral bands is the best predictor for TSS (R2 = 0.75), and CDOM absorption is best correlated with spectral features at blue and NIR regions (R2 = 0.85). These empirical models were further applied to the ASIA Eagle hyperspectral aerial imagery to demonstrate the feasibility of hyperspectral remote sensing of water quality in the optical complex estuarine waters.
机译:高光谱遥感为在大空间范围内进行频繁的天气天气水质测量提供了一种有效的方法。然而,情况2的水的光学复杂性使得通过河口水的遥感监测水质成为一个挑战。这项研究的主要目的是开发基于水反射率原位光谱测量的水质高光谱遥感算法。在这项研究中,水反射光谱R(λ)是在2008年至2011年夏季由一对海洋光学2000分光光度计在美国切萨皮克湾的支流Patuxent River上获得的。同时,测量叶绿素a和总悬浮固体(TSS)的浓度,以及有色溶解有机物(CDOM)的吸收。基于水反射光谱特征的经验模型通常显示出与水质参数的良好相关性。使用红色/近红外光谱带的检索模型显示出与叶绿素a浓度高度相关(R2 = 0.81)。绿色和蓝色光谱带的比率是TSS的最佳预测因子(R2 = 0.75),而CDOM吸收与蓝色和NIR区域的光谱特征相关性最好(R2 = 0.85)。这些经验模型进一步应用于ASIA Eagle高光谱航空影像,以证明在光学复杂河口水域中高光谱遥感水质的可行性。

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