Evidence map›Paper›PMID 41764217›Full record

ArticleScientific data2026

Development of Gridded Root-Zone Soil Moisture Product for India, 1981-2024.

Dipesh Singh Chuphal, Abhishek, Anuj Prakash Kushwaha, Gayathri Vangala, Vimal Mishra

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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

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Dipesh Singh Chuphal *Department of Civil Engineering, Indian Institute of Technology (IIT) Gandhinagar, Gandhinagar, India.ORCID http://orcid.org/0000-0002-0662-2906
Abhishek *Department of Civil Engineering, Indian Institute of Technology (IIT) Gandhinagar, Gandhinagar, India.
Anuj Prakash KushwahaDepartment of Earth Science, Indian Institute of Technology (IIT) Gandhinagar, Gandhinagar, India.ORCID http://orcid.org/0000-0003-3722-5249
Gayathri VangalaDepartment of Earth Science, Indian Institute of Technology (IIT) Gandhinagar, Gandhinagar, India.
Vimal MishraDepartment of Civil Engineering, Indian Institute of Technology (IIT) Gandhinagar, Gandhinagar, India. vmishra@iitgn.ac.in.ORCID http://orcid.org/0000-0002-3046-6296

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate and long-term soil moisture data is vital for drought monitoring and agricultural planning in monsoon-dependent and irrigation-intensive regions such as India. Satellite missions like SMAP and SMOS have improved global monitoring but remain limited by short records, shallow sensing depths, and reduced accuracy under dense vegetation and irrigation. To address these gaps, we reconstructed a 0.05° daily root-zone soil moisture (RZSM, 100 cm) dataset for India covering 1981-2024. The dataset was developed using a hybrid approach that combines simulations from the calibrated H08 land surface model with SMAP RZSM through Random Forest regression. Predictors included H08-derived soil moisture and evapotranspiration, precipitation, and temperature, trained against SMAP observations for 2016-2024. Cross-validation demonstrates strong agreement with SMAP, achieving R² and NSE values above 0.90 and an RMSE of less than 0.03 m³/m³ across most regions. Comparison with available in-situ measurement yields an RMSE of 0.04 m³/m³ and a correlation coefficient of 0.94. Independent validation with Solar-Induced Chlorophyll Fluorescence further confirmed consistency with vegetation activity during drought years (2002, 2009). This high-resolution, long-term dataset provides a robust resource for analysing drought variability, calibrating hydrological models, and assessing agricultural risks in India.

Identifiers

PMID41764217
PMCPMC13065839

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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.