Quantifying total suspended matter (TSM) in waters using Landsat images during 1984–2018 across the Songnen Plain, Northeast China. (15th May 2020)

• Record Type: Journal Article
• Authors: Du, Yunxia; Song, Kaishan; Liu, Ge; Wen, Zhidan; Fang, Chong; Shang, Yingxin; Zhao, Fangrui; Wang, Qiang; Du, Jia; Zhang, Bai
• Is Part Of: Journal of environmental management. Volume 262(2020)
• Publication Date: 2020-05-15
• Subjects: Inland water -- Landsat images -- Water quality parameters -- TSM; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2020.110334
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: Understanding the spatiotemporal dynamics of total suspended matter (TSM) in waters is necessary to promote efficient water resource management. In our study, we have estimated the spatiotemporal pattern of TSM with the combination of time-series Landsat images and field survey. Among various remote sensing-derived parameters, the red/blue band turns to be robust and the most sensitive to the TSM from field measurements. In Songnen Plain, the mean annual TSM in 60.5% of the water bodies decreased from 1984 to 2018. The decreasing of TSM is likely due to the increasing of vegetation in the area. The TSM concentration in waters declined from April to July, and then increased from September onwards. We also found the TSM in water bodies in Songnen Plain has very high spatial variation. Our results indicated that the meteorological factors such as wind and precipitation may affect the variation of TSM. Our results demonstrate that long-term Landsat data are useful to examine TSM in inland waters. Our findings can support for water resource management under human activities and climate change. Highlights: Landsat images and field surveys were used for mapping long-term TSM. The Landsat-based models were developed for spatiotemporal TSM. TSM were generally de creasing for 35 years during 1984–2018. TSM showed significant spatial heterogeneities in our study area. Vegetation, wind and precipitation were the key variables affecting TSM.