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ORIGINAL RESEARCH article

Front. Plant Sci.

Sec. Functional Plant Ecology

Volume 16 - 2025 | doi: 10.3389/fpls.2025.1691082

This article is part of the Research TopicInteractive Effects of Climate Change and Human Activities on Plant Productivity in Grassland and Cropland EcosystemsView all 18 articles

Global Soil Moisture Dynamics: Attribution of Contributions and Their Association with GPP

Provisionally accepted
  • Yanbian University, Yanji, China

The final, formatted version of the article will be published soon.

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.

Keywords: SM1, spatiotemporal evolution2, Contribution attribution3, trend4, IAV5, GPP6

Received: 22 Aug 2025; Accepted: 01 Oct 2025.

Copyright: © 2025 Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Yang Li, nnlyon@126.com

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