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Related Experiment Video

Updated: Aug 9, 2025

In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

Global long term daily 1 km surface soil moisture dataset with physics informed machine learning.

Qianqian Han¹, Yijian Zeng¹, Lijie Zhang²

¹Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7514 AE, Enschede, The Netherlands.

Scientific Data

|February 22, 2023

Summary

A new physics-informed machine learning approach created the Global Surface Soil Moisture (GSSM1km) dataset. This high-resolution soil moisture data supports climate and hydrologic studies.

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Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Published on: December 21, 2019

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Last Updated: Aug 9, 2025

In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Published on: December 21, 2019

Area of Science:

Earth Science
Hydrology
Climate Science

Background:

Global, continuous, high-resolution soil moisture datasets are limited, hindering hydrologic and climate applications.
Soil moisture is a critical variable influencing weather patterns and climate dynamics.

Purpose of the Study:

To generate a global, long-term, high-resolution surface soil moisture dataset using physics-informed machine learning.
To provide a continuous 1km resolution dataset covering the period 2000-2020.

Main Methods:

Utilized physics-informed machine learning integrating International Soil Moisture Network (ISMN) data, remote sensing, and meteorological information.
Incorporated physical process knowledge to guide soil moisture dynamics modeling.
Generated the Global Surface Soil Moisture (GSSM1km) dataset at 1km spatial and daily temporal resolution.

Main Results:

The GSSM1km dataset demonstrates strong performance with a root mean square error of 0.05 cm³/cm³ and a correlation coefficient of 0.9.
Antecedent Precipitation Evaporation Index (APEI) was identified as the most significant predictor, followed by evaporation and longitude.
Dataset validated against independent testing and existing soil moisture products.

Conclusions:

The GSSM1km dataset offers a valuable resource for large-scale climate extreme investigations and long-term trend analysis.
Physics-informed machine learning proves effective in generating high-resolution, continuous soil moisture data.
The dataset enhances understanding of soil moisture dynamics crucial for climate and hydrologic research.