Evidence map›Paper›PMID 40169619›Full record

ArticleScientific data2025

A unified ensemble soil moisture dataset across the continental United States.

Lingcheng Li, Xinming Lin, Yilin Fang, Z Jason Hou, L Ruby Leung, Yaoping Wang, Jiafu Mao, Yaping Xu, Elias Massoud, Mingjie Shi

Abstract read
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Lingcheng LiPacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99354, United States.
Xinming LinPacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99354, United States.
Yilin FangPacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99354, United States.
Z Jason HouPacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99354, United States.ORCID http://orcid.org/0000-0002-9388-6060
L Ruby LeungPacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99354, United States.ORCID http://orcid.org/0000-0002-3221-9467
Yaoping WangEnvironmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge, TN, 37830, United States.
Jiafu MaoEnvironmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge, TN, 37830, United States.ORCID http://orcid.org/0000-0002-2050-7373
Yaping XuDepartment of Environmental and Geosciences, Sam Houston State University, Huntsville, TX, 77340, United States.
Elias MassoudIntegrated Computational Earth Sciences group, Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge, TN, 37830, United States.
Mingjie ShiPacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99354, United States. mingjie.shi@pnnl.gov.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A unified ensemble soil moisture (SM) package has been developed over the Continental United States (CONUS). The data package includes 19 products from land surface models, remote sensing, reanalysis, and machine learning models. All datasets are unified to a 0.25-degree and monthly spatiotemporal resolution, providing a comprehensive view of surface SM dynamics. The statistical analysis of the datasets leverages the Koppen-Geiger Climate Classification to explore surface SM's spatiotemporal variabilities. The extracted SM characteristics highlight distinct patterns, with the western CONUS showing larger coefficient of variation values and the eastern CONUS exhibiting higher SM values. Remote sensing datasets tend to be drier, while reanalysis products present wetter conditions. In-situ SM observations serve as the basis for wavelet power spectrum analyses to explain discrepancies in temporal scales across datasets facilitating daily SM records. This study provides a comprehensive soil moisture data package and an analysis framework that can be used for Earth system model evaluations and uncertainty quantification, quantifying drought impacts and land-atmosphere interactions and making recommendations for drought response planning.

Identifiers

PMID40169619
PMCPMC11961677

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.