Landsat observations of total suspended solids concentrations in the Pearl River Estuary, China, over the past 36 years

Qiang Sun; Wei Luo; Xianzhang Dong; Shaohua Lei; Meng Mu; Shuai Zeng

doi:10.1016/j.envres.2024.118461

Landsat observations of total suspended solids concentrations in the Pearl River Estuary, China, over the past 36 years

Environ Res. 2024 May 15:249:118461. doi: 10.1016/j.envres.2024.118461. Epub 2024 Feb 12.

Authors

Qiang Sun¹, Wei Luo², Xianzhang Dong³, Shaohua Lei⁴, Meng Mu⁵, Shuai Zeng⁶

Affiliations

¹ South China Institute of Environmental Science, Ministry of Ecology and Environment, NO.18 Ruihe RD., Guangzhou, 510535, China; National Key Laboratory of Urban Ecological Environmental Simulation and Protection, Guangzhou, 510535, China.
² School of Geography and Environmental Engineering, Jiangxi Provincial Key Laboratory of Low-Carbon Solid Waste Recycling, Gannan Normal University, Ganzhou, 341000, China.
³ Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing, 210023, China.
⁴ National Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing, 210029, China.
⁵ School of City and Urban Planning, Yancheng Teachers University, Yancheng, 224000, China.
⁶ South China Institute of Environmental Science, Ministry of Ecology and Environment, NO.18 Ruihe RD., Guangzhou, 510535, China; National Key Laboratory of Urban Ecological Environmental Simulation and Protection, Guangzhou, 510535, China. Electronic address: szeng@scies.org.

PMID: 38354886
DOI: 10.1016/j.envres.2024.118461

Abstract

Information on long-term trends in total suspended solids (TSS) is critical for assessing aquatic ecosystems. However, the long-term patterns of TSS concentration (C_TSS) and its latent drivers have not been well investigated. In this study, we developed and validated three semi-analysis algorithms for deriving C_TSS using Landsat images. Subsequently, the long-term trends in C_TSS in the Pearl River Estuary (PRE) from 1987 to 2022 and the driving factors were clarified. The developed algorithms yielded excellent performance in estimating C_TSS, with mean absolute percentage errors <25% and root mean square errors of <13 mg/L. Long-term Landsat observations showed an overall decreasing trend and significant spatiotemporal dynamics of the C_TSS in the PRE from 1987 to 2022. The analysis of driving factors suggested that industrial sewage, cropland, forests and grasslands, and built-up land were the four potential driving forces that explained 87.81% of the long-term variation in C_TSS. This study not only provides 36-year recorded datasets of C_TSS in estuary water, but also offers new insights into the complex mechanisms that regulate C_TSS spatiotemporal dynamics for water resource management.

Keywords: Driving factors; Remote sensing; Spatiotemporal dynamics; Water quality.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
China
Environmental Monitoring* / methods
Estuaries*
Rivers* / chemistry
Satellite Imagery