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Assessment of soil moisture trend coherence among major global soil moisture products
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DOI：10.1016/j.jhydrol.2026.135568.png)
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Abstract
En 中文
Soil moisture (SM) is a critical variable for global ecosystems, playing a key role in water cycles, climate regulation and agricultural productivity. While numerous remote sensing and reanalysis products provide global SM, their representation of long-term trends often exhibits significant discrepancies, creating uncertainty for climate studies and applications. To better evaluate the trend performance of these products, this study analyzes the temporal trends (2000-2023) of four seamless global SM products (ERA5-Land, ESA CCI, GLDAS, and GLEAM) and validates them against an amount of ground station data. Furthermore, a controlled gap-simulation experiment incorporating discontinuous sensor-based products (SMAP, SMOS, AMSR2 and NNsm) into a dualbaseline framework is conducted to disentangle the statistical artifacts caused by data gaps from the broader context of trend uncertainty. The results show a dominant global drying trend, though substantial discrepancies in trend magnitude and direction persist among products. Among the evaluated datasets, GLEAM demonstrates outstanding performance, achieving the highest agreement with in-situ observations in both trend magnitude (R = 0.57) and directional consistency (66.0%). Further analysis reveals that on a continuous reference time series, data missingness primarily leads to the missed detection of trend signals rather than causing actual trend reversals, which are mainly attributed to sensor physics and algorithmic limitations. In addition, the long-term trends of ESA CCI are heavily influenced by its scaling data (GLDAS), which may limit the product's trend independence and requires further investigation. This study provides new methodological perspectives for the attribution of SM trend discrepancies, offering valuable guidance for user selection by recommending the prioritization of physically robust datasets (e.g., GLEAM) and the use of multi-product ensembles to mitigate specific biases.
Keywords:
GlobalSoil moisture product
Trend analysis
Coherence assessment
Inter-comparison
Mann-Kendall test
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