Abstract
This study demonstrates the feasibility of coastal water quality mapping using satellite remote sensing images. Water quality sampling campaigns were conducted over a coastal area in northern Taiwan for measurements of three water quality variables including Secchi disk depth, turbidity, and total suspended solids. SPOT satellite images nearly concurrent with the water quality sampling campaigns were also acquired. A spectral reflectance estimation scheme proposed in this study was applied to SPOT multispectral images for estimation of the sea surface reflectance. Two models, univariate and multivariate, for water quality estimation using the sea surface reflectance derived from SPOT images were established. The multivariate model takes into consideration the wavelength-dependent combined effect of individual seawater constituents on the sea surface reflectance and is superior over the univariate model. Finally, quantitative coastal water quality mapping was accomplished by substituting the pixel-specific spectral reflectance into the multivariate water quality estimation model.
Highlights
In light of the synoptic spatial coverage and routine availability of satellite images, there have been many implementations of environmental monitoring using remote sensing images in recent years.Among these practices, applications focusing on terrestrial and water environments such as landuse/landcover classification and change detection, landslide site detection, lake and reservoir trophic state monitoring [1-8], ocean harmful algal bloom monitoring [9-12], and coastal water quality monitoring [13-21] are most frequently conducted, there have been significantly fewer examples of coastal water quality monitoring, as compared to monitoring of impounded water bodies such as lakes and reservoirs
An essential difference in the nature of water quality in coastal area and impounded water bodies is that water quality of impounded water bodies is almost never affected by the downstream condition, whereas coastal water quality is constantly affected by upstream flow discharge and seawater flushing
In order to map the spatial distribution of the water quality variables using remote sensing images, it is necessary to establish water quality estimation models based on the reflectance of sea surface
Summary
Yuan-Fong Su 1, Jun-Jih Liou 1, Ju-Chen Hou 1, Wei-Chun Hung 1, Shu-Mei Hsu 1, Yi-Ting Lien 1, Ming-Daw Su 1, 2, Ke-Sheng Cheng 1, 2,* and Yeng-Fung Wang 3. Hydrotech Research Laboratory, National Taiwan University, Taipei, Taiwan. Received: 11 September 2008; in revised form: 27 September 2008 / Accepted: 6 October 2008 /
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