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Inversion And Analysis Of Chromophoric Dissolved Organic Matter In Estuarine And Coastal Regions Using Hyperspectral Remote Sensing

CDOM (chromophoric dissolved organic matter) plays an important role in determining underwater light field and aquatic photochemical and biological processes. Knowing CDOM properties, origin, sink, content, and distribution is able to provide us not only a useful approach to evaluate, but also a new perspective to understand water quality, carbon cycle, as well as the climate change. Remote sensing inversion of CDOM bears the potential capability to assess CDOM at large scale, but it has not been fully investigated yet. Particularly, the previous approaches cannot meet the accuracy and spatial resolution requirement for analyzing complex waters in estuarine and coastal regions. Therefore, a new scheme, which combines a newly developed inversion algorithm and hyperspectral remote sensing, is proposed to solve problems encountered in CDOM evaluation. This research covers three study sites, in the estuarine and coastal regions of the Mississippi River, Hudson River, and Neponset River. Very high resolution in situ data were collected in these sites and EO-1 Hyperion satellite images were also acquired accordingly. Based on a quasi-analytical algorithm (QAA), a QAA-CDOM algorithm was developed, by which CDOM absorption coefficient ag (440) is separated fromadg (440)(total absorption coefficient of CDOM and non-algal particles). Some QAA's parameters and functions were also optimized, using available datasets (in situ, IOCCG, and NOMAD). Result validation in the Atchafalaya plume has proved that QAA-CDOM is capable of estimating ag (440) in excellent accuracy (RMSE =0.11 m-1 andR2 =0.73 in the Atchafalaya River plume region). More importantly, applying QAA-CDOM to other locations, including the Mississippi River, Amazon River, and Moreton Bay, also derived very reasonable and accurate ag(440), covering a wide range from 0.01 to 15 m -1 . This confirms that our method is applicable to a wide range of estuarine regions. The uncertainties involved in CDOM inversion were also analyzed, aiming to know the origin, magnitude, and propagation of uncertainty in different inversion phases. This work strongly indicates that the proposed scheme, QAA-CDOM hyperspectral remote sensing inversion, is robust and reliable to quantify CDOM's concentration, distribution and dynamic for diverse waters, and hence can be applied to other regions.
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