An advanced spatiotemporal fusion model for suspended particulate matter monitoring in an intermontane lake

Fei ZHANG, Pan DUAN, Chi Yung JIM, Verner Carl JOHNSON, Changjiang LIU, Ngai Weng CHAN, Mou Leong TAN, Hsiang-Te KUNG, Jingchao SHI, Weiwei WANG

Research output: Contribution to journalArticlespeer-review

2 Citations (Scopus)

Abstract

Ebinur Lake is the largest brackish-water lake in Xinjiang, China. Strong winds constantly have an impact on this shallow water body, causing high variability in turbidity of water. Therefore, it is crucial to continuously monitor suspended particulate matter (SPM) for water quality management. This research aims to develop an advanced spatiotemporal fusion model based on the inversion technique that enables time-continuous and detailed monitoring of SPM over an intermontane lake. The findings shows that: (1) the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) fusion in blue, green, red, and near infrared (NIR) bands was better than the flexible spatiotemporal data fusion (FSDAF) model in extracting SPM information; (2) the inversion model constructed by random forest (RF) outperformed the support vector machine (SVM) and partial least squares (PLS) algorithms; and (3) the SPM concentrations acquired from the fused images of Landsat 8 OLI and ESTARFM matched with the actual data of Ebinur Lake based on the visual perspective and accuracy assessment. Copyright © 2023 by the authors.

Original languageEnglish
Article number1204
JournalRemote Sensing
Volume15
Issue number5
DOIs
Publication statusPublished - Mar 2023

Citation

Zhang, F., Duan, P., Jim, C. Y., Johnson, V. C., Liu, C., Chan, N. W., . . . Wang, W. (2023). An advanced spatiotemporal fusion model for suspended particulate matter monitoring in an intermontane lake. Remote Sensing, 15(5). Retrieved from https://doi.org/10.3390/rs15051204

Keywords

  • Ebinur Lake
  • Suspended particulate matter (SPM)
  • Water quality monitoring
  • Enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM)

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