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Volume 17
Issue 6
10.3390/rs17061000
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Article

A Deep Learning Framework for Long-Term Soil Moisture-Based Drought Assessment Across the Major Basins in China

by
Ye Duan
1,2,
Yong Bo
1,3,
Xin Yao
4,
Guanwen Chen
4,
Kai Liu
1,2,
Shudong Wang
1,2,*,
Banghui Yang
5 and
Xueke Li
6
1
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CICFEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
4
The Third Surveying and Mapping Institute of Guizhou Province, Guiyang 550004, China
5
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
6
Department of Earth and Environmental Science, University of Pennsylvania, Philadelphia, PA 19104, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(6), 1000; https://doi.org/10.3390/rs17061000
Submission received: 21 December 2024 / Revised: 28 February 2025 / Accepted: 10 March 2025 / Published: 12 March 2025
(This article belongs to the Special Issue Multi-Source Remote Sensing Data for Water Resource Management in Agriculture)
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Abstract

Drought is a critical hydrological challenge with ecological and socio-economic impacts, but its long-term variability and drivers remain insufficiently understood. This study proposes a deep learning-based framework to explore drought dynamics and their underlying drivers across China’s major basins over the past four decades. The Long Short-Term Memory network was employed to reconstruct gaps in satellite-derived soil moisture (SM) datasets, achieving high accuracy (R2 = 0.928 and RMSE = 0.020 m3m−3). An advanced explainable artificial intelligence (XAI) approach was applied to unravel the mechanistic relationships between SM and critical hydrometeorological variables. Our results revealed a slight increasing trend in SM value across China’s major basins over the past four decades, with a more pronounced downward trend in cropland that was more sensitive to water resource management. XAI results demonstrated distinct regional disparities: the northern arid regions displayed pronounced seasonality in drought dynamics, whereas the southern humid regions were less influenced by seasonal fluctuations. Surface solar radiation and air temperature were identified as the primary drivers of droughts in the Haihe, Yellow, Southwest, and Pearl River Basins, whereas precipitation is the dominant factor in the Middle and Lower Yangtze River Basins. Collectively, our study offers valuable insights for sustainable water resource management and land-use planning.
Keywords: soil moisture; droughts; remote sensing; deep learning; long short-term memory (LSTM); expected gradients (EG) soil moisture; droughts; remote sensing; deep learning; long short-term memory (LSTM); expected gradients (EG)

Share and Cite

MDPI and ACS Style

Duan, Y.; Bo, Y.; Yao, X.; Chen, G.; Liu, K.; Wang, S.; Yang, B.; Li, X. A Deep Learning Framework for Long-Term Soil Moisture-Based Drought Assessment Across the Major Basins in China. Remote Sens. 2025, 17, 1000. https://doi.org/10.3390/rs17061000

AMA Style

Duan Y, Bo Y, Yao X, Chen G, Liu K, Wang S, Yang B, Li X. A Deep Learning Framework for Long-Term Soil Moisture-Based Drought Assessment Across the Major Basins in China. Remote Sensing. 2025; 17(6):1000. https://doi.org/10.3390/rs17061000

Chicago/Turabian Style

Duan, Ye, Yong Bo, Xin Yao, Guanwen Chen, Kai Liu, Shudong Wang, Banghui Yang, and Xueke Li. 2025. "A Deep Learning Framework for Long-Term Soil Moisture-Based Drought Assessment Across the Major Basins in China" Remote Sensing 17, no. 6: 1000. https://doi.org/10.3390/rs17061000

APA Style

Duan, Y., Bo, Y., Yao, X., Chen, G., Liu, K., Wang, S., Yang, B., & Li, X. (2025). A Deep Learning Framework for Long-Term Soil Moisture-Based Drought Assessment Across the Major Basins in China. Remote Sensing, 17(6), 1000. https://doi.org/10.3390/rs17061000

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