Estimation of Total Suspended Matter Concentration of Ha Long Bay, Vietnam, from Formosat-5 Image

Pham Minh Chau, Chi Kuei Wang

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1 Citation (Scopus)


This study proposes the use of spatial high-resolution Formosat-5 (FS5) images for estimating total suspended matter (TSM) concentrations in a coastal region. Although many atmospheric correction methods are available, none of them are proposed to apply to FS5. Therefore, to remove the atmospheric effect, we performed a linear regression between the digital number (DN) of an FS5 image and the Landsat-8 Operational Land Imager (OLI) level-2 remote-sensing reflectance (Rrs) by using 160 samples of five ground targets. The ground targets, namely roof material, asphalt, water, vegetation, and other materials (sand and soil), were assumed to have negligible differences within 24 h. The results show that the linear model used for computing FS5 reflectance exhibited good coefficients of determination (R2) ranging from 0.87 to 0.96 for blue, green, red, and near-infrared bands. Next, in situ TSM measurements were not collected during the FS5 over-passing in Ha Long Bay, Vietnam, so we used two existing algorithms with a red band to estimate the TSM concentration. These algorithms developed for different coastal waters exhibited satisfactory agreement between derived field data and observed TSM concentrations with R2 ranging from 0.86 to 0.95. We also cross-checked the accuracy of the FS5-derived TSM concentration through comparison with an OLI-derived TSM image. The OLI-derived TSM image was validated and discussed for Vietnamese coastal waters, including Ha Long Bay. Lastly, based on comparisons between FS5-and OLI-derived TSM images in terms of spatial distribution, histograms, and root mean square error, we indicated the FS5 images after the removal of atmospheric effects could be totally used for estimating TSM in coastal water regions.

Original languageEnglish
Article number441
JournalJournal of Marine Science and Engineering
Issue number3
Publication statusPublished - 2022 Mar

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Water Science and Technology
  • Ocean Engineering


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