Study on Shallow Coastal Water Depth Derived Method Based on Hyperspectral Remote Sensing

  • 1. International Institute for Earth System Science, Nanjing University, Nanjing, Jiangsu 210093;
    2. The Key Lab of China Remote Sensing Satellite Ground Station, Chinese Academy of Sciences, Beijing 100086

Received date: 2006-10-09

  Revised date: 2007-04-20

  Online published: 2007-11-20


The theoretical basis on the water depth retrieval by optical remote sensing was analyzed based on the research on radiative transfer process of light wave in water.After a summary of water depth retrieval methods of the predecessors,derivative spectra method for water depth retrieval was introduced.Water reflective spectra were collected by using ASD field spectroradiometer,and water depths were measured by a digital echo sounding system simultaneously at yancheng coastal zone,in Jiangsu Province of China.The turbidity was inhomogeneous in the test area and scattered signal from material in the water was also different in spatial distribution.The reflectance of near infrared band(760-900 nm) was most sensitive to water depth(R=-0.73).As simulated in TM band settings,the correlation between water depth and reflectance ratio between TM4 and TM1 is better than others(R=-0.81).The correlation between water depth and the first derivative of reflectance at 711nm is significant(R=-0.87).Single band method,ratio method and derivative spectra method were respectively applied to calculating water depths.The accuracy evaluation of three models showed that the accuracy of single band model and ratio model were low for the points near the shore,which average relative error was more than 30%.The accuracy of derivative spectra model was improved as to the same points,which average relative error was 17%.The results indicated that derivative spectra method was an effective tool for reducing the error bring by variety of water quality.

Cite this article

WANG Jing-Jing, TIAN Qing-Jiu . Study on Shallow Coastal Water Depth Derived Method Based on Hyperspectral Remote Sensing[J]. SCIENTIA GEOGRAPHICA SINICA, 2007 , 27(6) : 843 -848 . DOI: 10.13249/j.cnki.sgs.2007.06.843


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