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- Publisher Website: 10.3390/rs15051204
- Scopus: eid_2-s2.0-85149986633
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Article: An Advanced Spatiotemporal Fusion Model for Suspended Particulate Matter Monitoring in an Intermontane Lake
Title | An Advanced Spatiotemporal Fusion Model for Suspended Particulate Matter Monitoring in an Intermontane Lake |
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Authors | |
Keywords | Ebinur Lake enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) suspended particulate matter (SPM) water quality monitoring |
Issue Date | 2023 |
Citation | Remote Sensing, 2023, v. 15, n. 5, article no. 1204 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/351639 |
DC Field | Value | Language |
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dc.contributor.author | Zhang, Fei | - |
dc.contributor.author | Duan, Pan | - |
dc.contributor.author | Jim, Chi Yung | - |
dc.contributor.author | Johnson, Verner Carl | - |
dc.contributor.author | Liu, Changjiang | - |
dc.contributor.author | Chan, Ngai Weng | - |
dc.contributor.author | Tan, Mou Leong | - |
dc.contributor.author | Kung, Hsiang Te | - |
dc.contributor.author | Shi, Jingchao | - |
dc.contributor.author | Wang, Weiwei | - |
dc.date.accessioned | 2024-11-21T06:37:55Z | - |
dc.date.available | 2024-11-21T06:37:55Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Remote Sensing, 2023, v. 15, n. 5, article no. 1204 | - |
dc.identifier.uri | http://hdl.handle.net/10722/351639 | - |
dc.description.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. | - |
dc.language | eng | - |
dc.relation.ispartof | Remote Sensing | - |
dc.subject | Ebinur Lake | - |
dc.subject | enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) | - |
dc.subject | suspended particulate matter (SPM) | - |
dc.subject | water quality monitoring | - |
dc.title | An Advanced Spatiotemporal Fusion Model for Suspended Particulate Matter Monitoring in an Intermontane Lake | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.3390/rs15051204 | - |
dc.identifier.scopus | eid_2-s2.0-85149986633 | - |
dc.identifier.volume | 15 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | article no. 1204 | - |
dc.identifier.epage | article no. 1204 | - |
dc.identifier.eissn | 2072-4292 | - |