Mapping Bottom Depth and Albedo in Coastal Waters of the South China Sea Islands and Reefs Using Landsat TM and ETM+ Data
Digital Object Identifier (DOI)
Optical models for the retrieval of shallow water bottom depth and albedo using multispectral data usually require in situ water depth data to tune the model parameters. In the South China Sea (SCS), however, such in situ data are often lacking or obsolete (perhaps from half a century ago) for most coastal waters around its islands and reefs. Here, we combine multispectral data collected by MODIS and Landsat to estimate bottom depth and albedo for four coral reef regions in the SCS, with results partially validated by some scarce in situ data. The waters in these remote regions are oligotrophic whose optical properties can be well derived from Moderate Resolution Imaging Spectroradiometer (MODIS) measurements when the waters are optically deep. The MODIS-derived optical properties are used to estimate the water column attenuation to the Landsat measurements over shallow waters, thus eliminating the requirement of model tuning using field measured water depths. The model is applied to four Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images covering Pratas Atoll, Woody Island, Scarborough Shoal, and North Danger Reefs. The retrieved bathymetry around Pratas Atoll and North Danger Reefs are validated with some in situ data between 1 and 25 m. The relative difference and root mean square difference between the two measurements were 17% and 1.6 m, for Pratas Atoll and 11% and 1.1 m for North Danger Reefs, respectively. These results suggest that the approach developed here may be extended to other shallow, clear waters in the SCS.
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Citation / Publisher Attribution
International Journal of Remote Sensing, v. 35, issue 11-12, p. 4156-4172
Scholar Commons Citation
Hu, Lianbo; Liu, Zhen; Liu, Zhishen; Hu, Chuanmin; and He, Ming-Xia, "Mapping Bottom Depth and Albedo in Coastal Waters of the South China Sea Islands and Reefs Using Landsat TM and ETM+ Data" (2014). Marine Science Faculty Publications. 1933.