Remote Sensing of Nearshore Water Quality Using Bio-Optical Modeling and Retrieval Techniques

9806982 Bagheri这项研究将开发遥感方法，作为一种工具，用于帮助评估和管理水质在近岸沃茨使用成像光谱仪AVIRIS数据和现场测量，增强了SeaWIFS数据的天气目的。 试验地点是哈德逊/拉里坦河口，这是一个复杂的生态系统，对纽约大都市区很重要，被认为是美国东海岸最密集开发/工业化的河口。 拟议的研究将从SeaWIFS、AVIRIS、现场分光辐射计和相关的离散水采样中获取多时间/多传感器数据。 基于这些测量光学水质模型将被构建连接的水成分浓度的固有光学特性（IOP），使用特定的固有光学特性（SIOP），地下（IR）的辐射反射率。 这将最大限度地减少同时进行遥感数据采集和大规模现场取样的需要。 在AVIR飞行期间，所有必要的是精确测量上升和下降（ir）辐射，以及有限的现场测量进行验证。 将使用三种方法为该项目开发遥感算法（按重要性递增顺序排列）：（1）光谱库方法;（2）导数分析方法;（3）基于生物光学建模和辐射传递理论的分析算法方法。 ***

9806982 Bagheri This research will develop remote sensing methodology for use as a tool in aiding the assessment and the management of water quality in nearshore waters using imaging spectrometer AVIRIS data and in situ field measurements, augmented by SeaWIFS data for synoptic purposes. The test site is the Hudson/Raritan Estuary which is a complex ecosystem, important to the New York metropolitan area and considered the most intensively developed/industrialized estuary on the US east coast. The proposed research will acquire multitemporal/multisensor data from SeaWIFS, AVIRIS, field spectroradiometer and associated discrete water sampling. Based on these measurements optical water quality models will be constructed linking the water constituent concentrations to the inherent optical properties (IOP), using the specific inherent optical properties (SIOP), to the subsurface (ir)radiance reflectance. This will minimize the need for simultaneous remote sensing data acquisition and large scale in situ sampling. All that will be necessary during the AVIR flight is the accurate measurement of up and downwelling (ir)radiance, and limited in situ measurements for verification. Three approaches will be used to develop remote sensing algorithms for this project (in increasing order of importance): (1) a spectral library approach; (2) a derivative analysis approach; and (3) an analytical algorithm approach based on bio-optical modeling and Radiative Transfer Theory (RTT). ***