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

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...

Full description

Saved in:
Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-03, Vol.15 (5), p.1204
Main Authors: Zhang, Fei, Duan, Pan, Jim, Chi, Johnson, Verner, Liu, Changjiang, Chan, Ngai, Tan, Mou, Kung, Hsiang-Te, Shi, Jingchao, Wang, Weiwei
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Brackish water
Data integration
Earth resources technology satellites
Ebinur Lake
enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM)
Geospatial data
Image acquisition
Lakes
Landsat
Landsat satellites
Management
Monitoring
Neural networks
Particulate matter
Precipitation
Product information
Quality control
Quality management
Remote sensing
Satellite imagery
Shallow water
Spatiotemporal data
Support vector machines
suspended particulate matter (SPM)
Turbidity
Water
Water bodies
Water quality
Water quality management
water quality monitoring
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15051204