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Updated: Sep 16, 2025

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SMRFR: A global multilayer soil moisture dataset generated using Random Forest from multi-source data.

Yuhan Liu1, Yuanyuan Zha2, Gulin Ran1

  • 1State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, 430072, China.

Scientific Data
|July 9, 2025
PubMed
Summary
This summary is machine-generated.

A new machine learning framework, Soil Moisture via Random Forest Regression (SMRFR), generates a global, multi-layer soil moisture dataset. This dataset accurately captures soil moisture dynamics for improved agricultural and climate research.

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Area of Science:

  • Earth Science
  • Environmental Science
  • Data Science

Background:

  • Accurate soil moisture monitoring is vital for agriculture, hydrology, and climate modeling.
  • Existing datasets often lack continuous, multi-layer, or global coverage.

Purpose of the Study:

  • To develop a novel machine learning framework, SMRFR (Soil Moisture via Random Forest Regression), for generating a continuously updated, multi-layer global soil moisture dataset.
  • To provide daily soil moisture estimates at five soil layers (0-100 cm) with a 9 km spatial resolution from 2000-2023.

Main Methods:

  • Utilized publicly available reanalysis and remote sensing data.
  • Employed a Random Forest Regression model for soil moisture estimation.
  • Validated the model for spatial and temporal variability, generalization capacity, and accuracy.

Main Results:

  • SMRFR effectively captures spatial and temporal soil moisture variability.
  • Demonstrated strong generalization across continents and accurate capture of rainfall-induced dynamics.
  • Achieved an unbiased root mean square error of 0.0339 m³/m³ on the validation set.

Conclusions:

  • The SMRFR dataset provides a valuable, continuously updated reference for soil moisture analysis.
  • Enables improved regional to global scale modeling of soil moisture dynamics.
  • Supports advancements in agricultural, hydrological, and ecological research.