Global soil moisture dynamics: attribution of contributions and their association with GPP

Soil moisture (SM) is central to the global land-atmosphere interaction and is of significant research importance. However, the dynamic structural features of SM remain insufficiently explored. This study utilizes GLDAS-Noah SM data from 1948 to 2024 to develop a collaborative framework for quantifying SM contributions, based on "three-dimensional decomposition + covariance attribution." It decomposes Total Soil Moisture Variability (TSMV) into two major temporal dynamics: long-term trends (Trend) and inter-annual variability (IAV), assessing the contributions of different soil depths, seasonal variations, and temporal dynamics to TSMV, thereby laying the methodological groundwork for analyzing global TSMV structural features. Furthermore, the relationship between SM and the gross primary productivity (GPP) of different ecosystems remains unclear. This study further integrates the MODIS MCD12C1 and GOSIF GPP datasets to explore the correlation between SM and GPP on a global scale. The results indicate that between 2000 and 2024, global Total Soil Moisture (TSM) shows a marked declining trend, with SM decreasing synchronously across all seasons. The IAV contribution from the 40-200 cm soil layer to TSMV is more significant, and this contribution exhibits notable spatial variation. Globally, SM and GPP show an overall positive correlation, particularly in the 10-100 cm root zone of grasslands and croplands, where the correlation is especially pronounced.

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Publication Details

Journal
Frontiers in Plant Science
Published
2025-10-31
DOI
https://doi.org/10.3389/fpls.2025.1691082
Citations
1
Primary Topic
Soil Moisture and Remote Sensing
Type
article
Field-Weighted Citation Impact
0.80

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article

Global soil moisture dynamics: attribution of contributions and their association with GPP

Songyan Zhu, Chengyun Wang, Panxing He, Yunjiang Liang et al.
1 citations
Frontiers in Plant Science
Soil Moisture and Remote Sensing
0.80
article

Global soil moisture dynamics: attribution of contributions and their association with GPP

Songyan Zhu, Chengyun Wang, Panxing He, Yunjiang Liang, Xiao Jianhua, Jun Ma, Lei Bao, Yang Li
article en
1 citations

Abstract

Soil moisture (SM) is central to the global land-atmosphere interaction and is of significant research importance. However, the dynamic structural features of SM remain insufficiently explored. This study utilizes GLDAS-Noah SM data from 1948 to 2024 to develop a collaborative framework for quantifying SM contributions, based on "three-dimensional decomposition + covariance attribution." It decomposes Total Soil Moisture Variability (TSMV) into two major temporal dynamics: long-term trends (Trend) and inter-annual variability (IAV), assessing the contributions of different soil depths, seasonal variations, and temporal dynamics to TSMV, thereby laying the methodological groundwork for analyzing global TSMV structural features. Furthermore, the relationship between SM and the gross primary productivity (GPP) of different ecosystems remains unclear. This study further integrates the MODIS MCD12C1 and GOSIF GPP datasets to explore the correlation between SM and GPP on a global scale. The results indicate that between 2000 and 2024, global Total Soil Moisture (TSM) shows a marked declining trend, with SM decreasing synchronously across all seasons. The IAV contribution from the 40-200 cm soil layer to TSMV is more significant, and this contribution exhibits notable spatial variation. Globally, SM and GPP show an overall positive correlation, particularly in the 10-100 cm root zone of grasslands and croplands, where the correlation is especially pronounced.

Frontiers in Plant ScienceVol. 16
Yanbian University (CN), Fudan University (CN), Wuhan University (CN), Hubei Water Resources Research Institute (CN), Northwest Institute of Eco-Environment and Resources (CN), University of Southampton (GB)
National Natural Science Foundation of China
Openalex Percentile: Top 23%
Soil Moisture and Remote Sensing
0.80
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