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MODIS observations reveal decrease in lake suspended particulate matter across China over the past two decades
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DOI：10.1016/j.rse.2023.113724.png)
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摘要
En 中文
Variations in the concentrations and distribution of suspended particulate matter (SPM) of lakes can be used to show the responses of lake environment to climate and landscape change. However, the shifts and trends of SPM and potential drivers have not been well investigated across large spatial and temporal dimensions. This study developed a robust machine learning model to generate SPM time series in 269 lakes across China larger than 30 km2 from 2002 to 2021 using MODIS/Aqua imagery. The support vector regression model showed satisfactory performance on SPM retrievals over four orders of magnitude (0.1-1000 mg L-1) (mean absolute percentage error = 26%). The model performance was shown to be insensitive to changes in environmental and observing conditions (e.g., aerosol type and thickness, viewing geometry), based on a radiative transfer simulation model. The long-term MODIS record showed a spatial pattern of lower SPM in the western lakes compared to the shallow lakes of east China. Importantly, the SPM showed a significant decrease in the 21st century (average rate of change of-0.2 mg L-1/decade). The interannual variations in SPM were aggregated into five categories, ranging from lakes with continuous changing patterns to those with reversed changing patterns. The driving factors behind the changing patterns vary between different climate zones and ecoregions. A warmer and wetter climate was associated with decreasing SPM in western lakes, while the decrease in wind speed and reduced possibility of soil erosion were the primary drivers of progressively lower SPM in the eastern shallow lakes. These results not only show a comprehensive picture of the SPM dynamics of lakes in China but also provide new insights into the complex mechanisms that drive SPM spatiotemporal dynamics.
Keyword:
Remote sensing
Satellite
SPM
Water clarity
Turbidity
Machine learning
期刊
IF:
11.4
论文数:
1.1W
被引数:
9.4W
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引用论文
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NATIONAL SCIENCE REVIEW
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NATIONAL SCIENCE REVIEWIF17.1
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