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Article

Quantifying the Spatiotemporal Dynamics and Driving Factors of Lake Turbidity in Northeast China from 1985 to 2023

1
School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China
2
State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(20), 3481; https://doi.org/10.3390/rs17203481 (registering DOI)
Submission received: 8 August 2025 / Revised: 22 September 2025 / Accepted: 16 October 2025 / Published: 18 October 2025

Abstract

Turbidity is a crucial indicator for evaluating water quality. This study obtained the long-term spatial distribution of water turbidity across Northeast China from 1985 to 2023. A combination of the geographically and temporally weighted regression (GTWR) model, the Lindeman, Merenda, and Gold (LMG) method, and statistical data analysis methods were employed to quantify the spatiotemporal impacts of driving factors on turbidity changes. The stepwise regression model was able to credibly estimate turbidity, achieving a low RMSE of 18.432 Nephelometric Turbidity Units (NTU). Temporal variations in turbidity showed that 69.90% of lakes exhibited a decreasing trend. Spatial variations revealed that lakes with significantly increased turbidity were predominantly concentrated in the Songnen and Sanjiang Plains, whereas lakes with lower turbidity were situated in the Eastern Mountains regions and Liaohe Plain. Temporal changes were closely associated with socioeconomic development and anthropogenic interventions implemented by governments on the aquatic environment. Vegetation coverage, precipitation, and elevation demonstrated significant contributions (exceeding 16.39%) to turbidity variations in the Lesser Khingan and Eastern Mountains regions, where natural factors played a more dominant role. In contrast, cropland area, wind speed, and impervious surface area showed higher contribution rates of above 14.00% in the Songnen, Sanjiang, and Liaohe Plains, where anthropogenic factors were dominant. These findings provide valuable insights for informed decision-making in water environmental management in Northeast China and facilitate the aquatic ecosystem sustainability under human activities and climate change.
Keywords: turbidity; spatiotemporal dynamics; driving factors; Landsat; Northeast China turbidity; spatiotemporal dynamics; driving factors; Landsat; Northeast China

Share and Cite

MDPI and ACS Style

Ma, Y.; Zheng, Q.; Song, K.; Fang, C.; Li, S.; Chen, Q.; Ma, Y. Quantifying the Spatiotemporal Dynamics and Driving Factors of Lake Turbidity in Northeast China from 1985 to 2023. Remote Sens. 2025, 17, 3481. https://doi.org/10.3390/rs17203481

AMA Style

Ma Y, Zheng Q, Song K, Fang C, Li S, Chen Q, Ma Y. Quantifying the Spatiotemporal Dynamics and Driving Factors of Lake Turbidity in Northeast China from 1985 to 2023. Remote Sensing. 2025; 17(20):3481. https://doi.org/10.3390/rs17203481

Chicago/Turabian Style

Ma, Yue, Qiang Zheng, Kaishan Song, Chong Fang, Sijia Li, Qiuyue Chen, and Yongchao Ma. 2025. "Quantifying the Spatiotemporal Dynamics and Driving Factors of Lake Turbidity in Northeast China from 1985 to 2023" Remote Sensing 17, no. 20: 3481. https://doi.org/10.3390/rs17203481

APA Style

Ma, Y., Zheng, Q., Song, K., Fang, C., Li, S., Chen, Q., & Ma, Y. (2025). Quantifying the Spatiotemporal Dynamics and Driving Factors of Lake Turbidity in Northeast China from 1985 to 2023. Remote Sensing, 17(20), 3481. https://doi.org/10.3390/rs17203481

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