Lockdown effects on total suspended solids concentrations in the Lower Min River (China) during COVID-19 using time-series remote sensing images

Xu, Hong‐Guang; Xu, Guangzhi; Wen, Xin; Xiu-juan, HU; Wang, Yifan

doi:10.1016/j.jag.2021.102301

Cited by 34 publications

(29 citation statements)

References 27 publications

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“…The absolute time differences between the sampling dates and the satellite images were within five days, with 77.14% within two days (Figure 2). In remote-sensing applications, this time difference is acceptable [59][60][61]. MODIS data (MOD09GA: reflectance production data with a spatial resolution of 500 m) were downloaded from https://ladsweb.modaps.eosdis.nasa.gov/ (accessed on 2 July 2021), while Sentinel 2 and Landsat data were acquired from the US Geological Survey website at http://lglovis.usgs.goc/ (accessed on 2 July 2021).…”

Section: Data Source and Pre-processingmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Data Source and Pre-processingmentioning

confidence: 99%

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

et al. 2021

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“…It was found that the mean turbidity showed a 24.9% decline from 33.4 NTU to 25.1 NTU after the lockdown in Wuhan, which dropped 16.0% compared to that in the previous year (Sun et al 2021). Later, when investigating the lockdown effects of the COVID-19 on total suspended solids (TSS) concentrations in the Lower Min River (China) during COVID-19 using different multi-temporal optical remotely sensed images acquired from November 2019 to April 2020 such as Landsat-5 Thematic Mapper (TM), Landsat-8 Operational Land Imager (OLI), and China's GaoFen-1 (GF-1) Wide Field of View (WFV) images, Xu et al (2021) indicated that the lockdown measures have resulted in a 48% fall in TSS concentrations in February 2020. Xu et al (2021) also concluded that industrial production, social and economic activities, and river shipping appear to be the main factors contributing to the river's TSS decline in the lockdown period.…”

Section: Studies Of Impacts On Water Qualitymentioning

confidence: 99%

Gis And Remote Sensing: A Review Of Applications To The Study Of The Covid-19 Pandemic

Kieu

Nguyen

Hoang

2021

GES

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The spread of the 2019 novel coronavirus disease (COVID-19) has engulfed the world with a rapid, unexpected, and far-reaching global crisis. In the study of COVID-19, Geographic Information Systems (GIS) and Remote Sensing (RS) have played an important role in many aspects, especially in the fight against COVID-19. This review summarises 102 scientific papers on applications of GIS and RS on studies of the COVID-19 pandemic. In this study, two themes of GIS and RS-related applications are grouped into the six categories of studies of the COVID-19 including spatio-temporal changes, WebGISbased mapping, the correlation between the COVID-19 and natural, socio-economic factors, and the environmental impacts. The findings of this study provide insight into how to apply new techniques (GIS and RS) to better understand, better manage the evolution of the COVID-19 pandemic and effectively assess its impacts.

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“…Numerous studies have assessed spatio-temporal changes of natural environments during this epidemic, such as the improved air quality and water quality ( Venter et al, 2020 , Xu et al, 2021 ). However, the impact of the lockdown of cities on our socioeconomic systems is more complicated and hidden ( Beyer et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Lockdown induced night-time light dynamics during the COVID-19 epidemic in global megacities

Xiu

et al. 2021

International Journal of Applied Earth Observation and Geoinfor

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The lockdown of cities to against the COVID-19 epidemic directly decreases urban socioeconomic activities. Remotely sensed night-time light (NTL) provides a macro perspective to capture these variations. Here, taking 20 global megacities as examples, we adopted the NASA’s Black Marble NTL data with a daily resolution to investigate their spatio-temporal changes. We collected daily NTL products for four weeks (one month) before and after the date of lockdown in each city, which were then summarized as weekly and monthly averaged NTL images after pre-processing (cloud removing, outlier detection, etc.). Results show that NTL overall decreased after the lockdown of cities, but with regional disparities and varying spatial patterns. Asian cities experienced the most obvious reduction of NTL. Particularly, the monthly averaged NTL in Mumbai, India, decreased by nearly 20% compared to one month before. However, there were no significant decline in NTL in European cities. African cities also experienced stable changes of NTL. Spatially, city centers darkened more obviously than the urban periphery. Facing emergencies, NTL data has broad applications in monitoring socioeconomic dynamics and assessing public policies in a near real-time manner.

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Lockdown effects on total suspended solids concentrations in the Lower Min River (China) during COVID-19 using time-series remote sensing images

Abstract: Graphical abstract

Cited by 34 publications

References 27 publications

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

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

Gis And Remote Sensing: A Review Of Applications To The Study Of The Covid-19 Pandemic

Lockdown induced night-time light dynamics during the COVID-19 epidemic in global megacities

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