An Advanced Spatiotemporal Fusion Model for Suspended Particulate Matter Monitoring in an Intermontane Lake

Author:

Zhang Fei12,Duan Pan23,Jim Chi4ORCID,Johnson Verner5,Liu Changjiang26,Chan Ngai7ORCID,Tan Mou7ORCID,Kung Hsiang-Te8,Shi Jingchao8,Wang Weiwei2

Affiliation:

1. College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China

2. College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830017, China

3. College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China

4. Department of Social Sciences, Education University of Hong Kong, Lo Ping Road, Tai Po, Hong Kong 999077, China

5. Department of Physical and Environmental Sciences, Colorado Mesa University, Grand Junction, CO 81501, USA

6. Xinjiang Institute of Technology, Aksu 843000, China

7. GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, Penang 11800, Malaysia

8. Departments of Earth Sciences, The University of Memphis, Memphis, TN 38152, USA

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.

Funder

National Natural Science Foundation of China

State Key Laboratory of Lake Science and Environment

Tianshan Talent Project (Phase III) of the Xinjiang Uygur Autonomous Region

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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