Feasibility of the Spatiotemporal Fusion Model in Monitoring Ebinur Lake’s Suspended Particulate Matter under the Missing-Data Scenario

Chi-Yung Jim,Fei Zhang,Ngai Weng Chan,Changjiang Liu,Mou Leong Tan,Pan Duan

doi:10.3390/rs13193952

Abstract

High-frequency monitoring of suspended particulate matter (SPM) concentration can improve water resource management. Missing high-resolution satellite images could hamper remote-sensing SPM monitoring. This study resolved the problem by applying spatiotemporal fusion technology to obtain high spatial resolution and dense time-series data to fill image-data gaps. Three data sources (MODIS, Landsat 8, and Sentinel 2) and two spatiotemporal fusion methods (the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion (FSDAF)) were used to reconstruct missing satellite images. We compared their fusion accuracy and verified the consistency of fusion images between data sources. For the fusion images, we used random forest (RF) and XGBoost as inversion methods and set “fusion first” and “inversion first” strategies to test the method’s feasibility in Ebinur Lake, Xinjiang, arid northwestern China. Our results showed that (1) the blue, green, red, and NIR bands of ESTARFM fusion image were better than FSDAF, with a good consistency (R2 ≥ 0.54) between the fused Landsat 8, Sentinel 2 images, and their original images; (2) the original image and fusion image offered RF inversion effect better than XGBoost. The inversion accuracy based on Landsat 8 and Sentinel 2 were R2 0.67 and 0.73, respectively. The correlation of SPM distribution maps of the two data sources attained a good consistency of R2 0.51; (3) in retrieving SPM from fused images, the “fusion first” strategy had better accuracy. The optimal combination was ESTARFM (Landsat 8)_RF and ESTARFM (Sentinel 2)_RF, consistent with original SPM maps (R2 = 0.38, 0.41, respectively). Overall, the spatiotemporal fusion model provided effective SPM monitoring under the image-absence scenario, with good consistency in the inversion of SPM. The findings provided the research basis for long-term and high-frequency remote-sensing SPM monitoring and high-precision smart water resource management.

Highlights

We explored four research questions: (1) For the shallow saltwater Ebinur Lake in arid northwestern China, what is the fusion accuracy of ESTARFM and FSDAF? (2) What is the consistency of the fused images between MODISLandsat 8 (ML8) and MODIS-Sentinel 2 (MS2)? (3) What is the accuracy of Landsat 8 and Sentinel 2 data in monitoring Suspended particulate matter (SPM) in Ebinur Lake? (4) With gaps in satellite images, which strategy is more suitable for the effective monitoring of SPM in Ebinur Lake? Are they consistent with the SPM images retrieved from the original Landsat 8 and Sentinel
The main objective of this research was to explore the feasibility of monitoring SPM in Ebinur Lake by the spatiotemporal fusion model in the missing-image scenario
ESTARFM is more suitable for the research questions associated with the Ebinur Lake area than FSDAF; The original and fused images of Landsat 8 and Sentinel 2 have high consistency in the blue, green, red, and NIR bands

Summary

Introduction

Research on the continuous monitoring of water quality has attracted considerable attention [3,4]. Suspended particulate matter (SPM) is one of the important attributes to determine water clarity by controlling light penetration through the water column [5]. It plays an important role in regulating water productivity and aquatic ecosystem functions [6]. SPM serves as a key carrier of carbon, oxygen, nutrients, and heavy metals [7,8], and the main raw material to continuously change the in situ environment and exert pressure on the ecosystem [9,10]. The dynamic monitoring of SPM constitutes a critical domain of the intelligent management package for water resources [11]

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Conclusion