Estimation of the spectral diffuse attenuation coefficient of downwelling irradiance in inland and coastal waters from hyperspectral remote sensing data: Validation with experimental data

被引:27
|
作者
Simon, Arthi [1 ]
Shanmugam, Palanisamy [1 ]
机构
[1] Indian Inst Technol, Dept Ocean Engn, Ocean Opt & Imaging Lab, Madras 600036, Tamil Nadu, India
关键词
Remote sensing; Inland-marine waters; Attenuation; Algal blooms; Water quality; SATELLITE DATA; OCEANIC WATERS; LIGHT-FIELD; ABSORPTION; SCATTERING; RADIANCE; CLASSIFICATION; ALGORITHM; SURFACE; MODEL;
D O I
10.1016/j.jag.2016.02.003
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
A semi-analytical model is developed for estimating the spectral diffuse attenuation coefficient of down welling irradiance (K-d(lambda)) in inland and coastal waters. The model works as a function of the inherent optical properties (absorption and backscattering), depth, and solar zenith angle. Results of this model are validated using a large number of in-situ measurements of K-d(lambda) in clear oceanic, turbid coastal and productive lagoon waters. To further evaluate its relative performance, K-d(lambda) values obtained from this model are compared with results from three existing models. Validation results show that the present model is a better descriptor of K-d(lambda) and shows an overall better performance compared to the existing models. The applicability of the present model is further tested on two Hyperspectral Imager for the Coastal Ocean (HICO) remote sensing images acquired simultaneously with our field measurements. The K-d(lambda) spectra derived from HICO imageries have good agreement with measured data with the mean relative percent error of less than 12% which are well within the benchmark for a validated uncertainty of +/- 35% endorsed for the remote sensing products in oceanic waters. The model offers potential advantages for predicting changes in spectral and vertical K-d values in a wide variety of waters within inland and coastal environments. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:117 / 125
页数:9
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