Extraction of Chlorophyll-a Concentration Based on Spectral Unmixing Model Using Field Hyperspectral Data in Taihu Lake

Abstract

In China, one of the most common ecological problems of inland water bodies is represented by the eutrophication which diminishes water quality. And the chlorophyll-laden water becomes an obvious sign. Chlorophyll-a concentration measurement is usually used for assessing tropic status of lakes. The development of spectral resolution enables hyperspectral technology possible to monitor water quality successfully, which is based on developing relationships between radiance/reflectance in single band or band ratios and chlorophyll concentration. In this paper, a spectral unmixing model was established based on single-phase field hyperspectral data. Three data types were supported for this model: original data, normalization data and differential data. Selected end-member from known reflectance spectrum, we retrieved chlorophyll-a concentration. The result shows the spectral unmixing model based on differential data gives the best result. Validated this model and shows a good precision and stabilization. Finally, three-phase field hyperspectral datum were processed and chlorophyll-a concentration was extracted using the best model. The result shows that spectral unmixing model is a feasible model in the practical application of remote sensing water quality monitoring.

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Document Details

Document Type
Technical Report
Publication Date
Jul 25, 2005
Accession Number
ADA449879

Entities

People

  • Liu Qinhuo
  • Wen Jianguang
  • Xiao Qing
  • Yi Zhou

Organizations

  • Chinese Academy of Sciences

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Abstracts
  • Chlorophylls
  • Data Processing
  • Earth Sciences
  • Equations
  • Extraction
  • Information Operations
  • Materials
  • Monitoring
  • Precision
  • Reflectance
  • Remote Sensing
  • Spatial Distribution
  • Spectra
  • Water Quality

Fields of Study

  • Agricultural and Food sciences
  • Environmental science

Readers

  • Atmospheric Remote Sensing.
  • Computational Modeling and Simulation
  • Environmental Engineering