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Journal of Arid Land
Journal of Arid Land  2025, Vol. 17 Issue (5): 624-643    DOI: 10.1007/s40333-025-0014-x    
Research article     
Variations of soil moisture and its influencing factors in arid and semi-arid areas, China
NIU Jiqiang1,2,*(), LIU Zijian1, CHEN Feiyan1,2, LIU Gangjun3, ZHOU Junli4, ZHOU Peng5,6, LI Hongrui1, LI Mengyang1
1School of Geographic Sciences, Xinyang Normal University, Xinyang 464000, China
2Henan Key Technology Engineering Research Center of Microwave Remote Sensing and Resource Environment Monitoring, Xinyang Normal University, Xinyang 464000, China
3School of Science, STEM College, Royal Melbourne Institute of Technology (RMIT) University, Melbourne 3001, Australia
4Henan Remote Sensing Institute, Zhengzhou 450000, China
5Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing 100875, China
6Institute of Disaster Risk Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
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Abstract  

Soil moisture (SM) is a critical variable in terrestrial ecosystems, especially in arid and semi-arid areas where water sources are limited. Despite its importance, understanding the spatiotemporal variations and influencing factors of SM in these areas remains insufficient. This study investigated the spatiotemporal variations and influencing factors of SM in arid and semi-arid areas of China by utilizing the extended triple collation (ETC), Mann-Kendall test, Theil-Sen estimator, ridge regression analysis, and other relevant methods. The following findings were obtained: (1) at the pixel scale, the long-term monthly SM data from the European Space Agency Climate Change Initiative (ESA CCI) exhibited the highest correlation coefficient of 0.794 and the lowest root mean square error (RMSE) of 0.014 m3/m3; (2) from 2000 to 2022, the study area experienced significant increase in annual average SM, with a rate of 0.408×10-3 m3/(m3•a). Moreover, higher altitudes showed a notable upward trend, with SM increasing rates at 0.210×10-3 m³/(m3•a) between 1000 and 2000 m, 0.530×10-3 m3/(m3•a) between 2000 and 4000 m, and 0.760×10-3 m3/(m3•a) at altitudes above 4000 m; (3) land surface temperature (LST), root zone soil moisture (RSM) (10-40 cm depth), and normalized difference vegetation index (NDVI) were identified as the primary factors influencing annual average SM, which accounted for 34.37%, 24.16%, and 22.64% relative contributions, respectively; and (4) absolute contribution of LST was more significant in subareas at higher altitudes, with average absolute contributions of 0.800×10-3 m3/(m3•a) between 2000 and 4000 m and 0.500×10-2 m3/(m3•a) above 4000 m. This study reveals the spatiotemporal variations and main influencing factors of SM in Chinese arid and semi-arid areas, highlighting the more pronounced absolute contribution of LST to SM in high-altitude areas, providing valuable insights for ecological research and water resource management in these areas.

Key words： soil moisture      arid and semi-arid areas      remote sensing      extended triple collation      ridge regression analysis     
Received: 13 September 2024      Published: 31 May 2025
Corresponding Authors: *NIU Jiqiang (E-mail: niujiqiang@xynu.edu.cn)
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NIU Jiqiang
LIU Zijian
CHEN Feiyan
LIU Gangjun
ZHOU Junli
ZHOU Peng
LI Hongrui
LI Mengyang
Cite this article:

NIU Jiqiang, LIU Zijian, CHEN Feiyan, LIU Gangjun, ZHOU Junli, ZHOU Peng, LI Hongrui, LI Mengyang. Variations of soil moisture and its influencing factors in arid and semi-arid areas, China. Journal of Arid Land, 2025, 17(5): 624-643.

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http://jal.xjegi.com/10.1007/s40333-025-0014-x     OR     http://jal.xjegi.com/Y2025/V17/I5/624

Fig. 1 Geographic location and digital elevation data of the study area. Note that the figure is based on the standard map (No. GS(2019)1822) of the Map Service System (https://bzdt.ch.mnr.gov.cn/), and the boundary has not been modified.
Data Data type Temporal coverage
(yyyy-mm-dd)		 Spatial resolution Temporal resolution
ESA CCI SM Remote sensing 1978-01-01-Present 0.25° daily
ERA5-Land SM Reanalysis 1950-01-01-Present 0.25° daily/monthly
GLDAS SM Land model 2000-01-01-Present 0.25° daily/monthly
Precipitation Remote sensing 2000-06-01-2024-06-01 0.10° monthly
LST and RSM Land model 2000-01-01-2024-03-31 0.25° monthly
Evapotranspiration Land model 1980-01-01-2022-12-31 0.10° monthly
NDVI Remote sensing 2000-02-18-2024-06-09 1 km 16 d
Table 1 Basic data information used in this study
Fig. 2 Coefficient of determination (R2) and root mean square error (RMSE) of SM data products with unknown true SM. (a and b), ESA CCI; (c and d), GLDAS; (e and f), ERA5-Land; (g and h), the box plot represents R2 and RMSE of the three datasets against the true SM. Box boundaries indicate the 25th and 75th percentiles, and whiskers below and above the box indicate the 10th and 90th percentiles, respectively. The lines across the boxes indicate the median values, and the points represent the mean values. Blank areas indicate no data or unsatisfied extended triple collation (ETC) condition.
Fig. 3 Linear trend of mean SM in arid and semi-arid areas, China from 2000 to 2022
Fig. 4 Trend characteristics of SM in arid and semi-arid areas, China from 2000 to 2022. (a and b), spatial distributions of Theil-Sen slope and its re-classification; (c and d), spatial distribution and its statistics of the Mann-Kendall test. Z value is the standardized value of test statistic, and an absolute value greater than 1.96 indicates a significant trend.
Fig. 5 Average SM trends in different altitude areas from 2000 to 2022. (a), higher altitude area; (b), high altitude area; (c), low altitude area; (d), lower altitude area; (e), statistical results of the Mann-Kendall test.
Fig. 6 Spatial patterns (a-e) and trends (f-j) of precipitation (Pre), land surface temperature (LST), evaporation (E), NDVI, and root zone soil moisture (RSM) in arid and semi-arid areas, China from 2000 to 2022
Fig. 7 Relative contributions of Pre (a), LST (b), E (c), NDVI (d), and RSM (e) to annual SM in arid and semi-arid areas, China from 2000 to 2022, and probability density function (PDF) of relative contribution of the five factors (f)
Fig. 8 Spatial distribution (a) and area percentage (b) of dominant factors with the largest relative contribution to annual SM variations across different altitude areas from 2000 to 2022. ASA, arid and semi-arid areas of China.
Fig. 9 Spatial distribution and statistics of absolute contribution of five factors to SM in arid and semi-arid areas, China from 2000 to 2022. (a and b), Pre; (c and d), LST; (e and f), E; (g and h), NDVI; (i and j), RSM.
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