Regression-based synergy of optical, shortwave infrared and microwave remote sensing for monitoring the grain-size of intertidal sediments

Daphne van der Wal*, Peter M.J. Herman

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

47 Citations (Scopus)

Abstract

A method is developed for monitoring the sediment grain-size of intertidal flats in the Westerschelde (southwest Netherlands), using information from both space-borne microwave (SAR) and optical/shortwave infrared remote sensing. Estimates of the backscattering coefficient were extracted from time-series of C-band ERS SAR imagery. Surface reflectance in the visible, near-infrared (VNIR) and shortwave infrared (SWIR) part of the electromagnetic spectrum, as well as spectral indices, were derived from matching multi-temporal Landsat TM imagery. In addition, surface reflectances were derived from a set of airborne multispectral (VNIR) CASI images, and hyperspectral (VNIR) measurements using a field spectroradiometer. The data were related to matching field measurements of surface characteristics, including sediment properties. Regression-based algorithms were developed to map the spatio-temporal distribution of mud content using (a) the C-band SAR backscattering coefficient, (b) surface reflectance in the green and SWIR, and (c) a combination of these, with corroborative field measurements. Mud content of the sediment has been successfully mapped by all three algorithms, but a combination of information from microwave and VNIR/SWIR provided best results. The algorithms were generally consistent in time, making them suitable for generating time-series and for monitoring. However, they should be validated and calibrated in order to be applicable to other intertidal areas.

Original languageEnglish
Pages (from-to)89-106
Number of pages18
JournalRemote sensing of environment
Volume111
Issue number1
DOIs
Publication statusPublished - 2007
Externally publishedYes

Keywords

  • Mapping
  • Mud
  • Multi-sensor data fusion
  • Particle size
  • SAR
  • SWIR
  • VNIR
  • ITC-ISI-JOURNAL-ARTICLE

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