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

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.

The Spatially Seamless Spatiotemporal Fusion Model Based on Generative Adversarial Networks

“…Spatiotemporal fusion of remote sensing images plays a crucial role in various fields. In recent investigations, researchers have applied spatiotemporal fusion to different domains such as soil carbon emissions monitoring in subtropical forests [2], the retrieval of suspended particulate matter in saline lakes [3], the retrieval of daily surface temperatures and among others [4]. The pronounced utility of spatiotemporal fusion is particularly evident in its pivotal role within the domains of Earth science and environmental monitoring.…”