Evidence map›Paper›PMID 41680217›Full record

ArticleScientific data2026

Global daily 9 km remotely sensed soil moisture (2015-2025) with microwave radiative transfer-guided learning.

Sijia Feng, Aoyang Li, Rui Zhou, Klaus Butterbach-Bahl, Kaiyu Guan, Zhenong Jin, Majken C Looms, Sherrie Wang, Christian Igel, Claire Treat and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2026. 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

12 authors.

Sijia Feng *Pioneer Center Land-CRAFT, Department of Agroecology, Aarhus University, Aarhus, 8000, Denmark.
Aoyang Li *Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
Rui ZhouAgroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.ORCID http://orcid.org/0009-0005-2579-792X
Klaus Butterbach-BahlPioneer Center Land-CRAFT, Department of Agroecology, Aarhus University, Aarhus, 8000, Denmark.
Kaiyu GuanAgroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
Zhenong JinDepartment of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN, 55108, USA.ORCID http://orcid.org/0000-0002-1252-2514
Majken C LoomsDepartment of Geosciences and Natural Resource Management (IGN), University of Copenhagen, Copenhagen, 1165, Denmark.
Sherrie WangDepartment of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Christian IgelDepartment of Computer Science, University of Copenhagen, Copenhagen, 2100, Denmark.
Claire TreatPioneer Center Land-CRAFT, Department of Agroecology, Aarhus University, Aarhus, 8000, Denmark.ORCID http://orcid.org/0000-0002-1225-8178
Jørgen Eivind OlesenDepartment of Agroecology, Aarhus University, Blichers Allé 20, Tjele, 8830, Denmark.ORCID http://orcid.org/0000-0002-6639-1273
Sheng WangPioneer Center Land-CRAFT, Department of Agroecology, Aarhus University, Aarhus, 8000, Denmark. shengwang12@gmail.com.ORCID http://orcid.org/0000-0003-3385-3109

Funding

Villum Fonden (Villum Foundation) 00072051
6 · The paper itself

Abstract

Accurate estimation of surface soil moisture (SM) in terrestrial ecosystems is essential for understanding hydroclimate dynamics. The L-band Soil Moisture Active Passive (SMAP) mission provides 9-km global daily surface SM by using a microwave radiative transfer model (RTM)-based algorithm. However, the accuracy of SMAP SM is limited in regions with dense vegetation cover and complex surface conditions, due to the empirical parameterization and oversimplified radiative transfer processes. To overcome the limitations, we developed a Process-Guided Machine Learning (PGML) framework to integrate RTM theories and deep learning to predict global daily surface 9-km SM from April 2015 to June 2025. Informed by domain knowledge, we developed the PGML model structure using RTM and hydrological theories, designed a Kling-Gupta efficiency-based cost function, pretrained it with RTM simulations, and fine-tuned it with in-situ measurements. The independent validation shows that PGML SM has strong agreement with in-situ measurements (R = 0.868 and unbiased RMSE = 0.054 m

Identifiers

PMID41680217
PMCPMC13009162

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.