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Related Experiment Video

Updated: Dec 29, 2025

In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

Leveraging Google Earth Engine for Drought Assessment using Global Soil Moisture Data.

Nazmus Sazib^1,2, Iliana Mladenova^1,3, John Bolten¹

¹Hydrological Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, MD.

Remote Sensing

|February 6, 2020

Summary

This study introduces global soil moisture datasets and web tools for agricultural drought monitoring. These tools, integrated with Google Earth Engine, enhance drought assessment and early warning systems.

Keywords:

Drought Google Earth Engine SMAP SMOS Soil moisture

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

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Published on: June 8, 2015

Area of Science:

Earth Science
Agricultural Science
Remote Sensing

Background:

Soil moisture is crucial for assessing crop and drought conditions, yet accessible datasets for agricultural drought monitoring are scarce.
Existing drought monitoring often relies on precipitation and vegetation indices, which may not fully capture soil water status.

Purpose of the Study:

To evaluate two global soil moisture datasets and associated web-based processing tools for agricultural drought monitoring and crop forecasting.
To demonstrate the value of satellite-derived soil moisture data within the Google Earth Engine (GEE) platform.

Main Methods:

Integration of Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite data into a modified Palmer model using a 1-D Ensemble Kalman Filter (EnKF).
Development of web-based tools for exploring soil moisture variability, estimating drought characteristics (duration, intensity), and comparing them with other drought indicators.
Assessment of soil moisture products, vegetation indices (NDVI), and precipitation indices (SPI) in drought-prone regions of South Africa and Ethiopia.

Main Results:

The 3-month Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) showed strong agreement with root zone soil moisture anomalies.
Soil moisture anomalies indicated shorter drought durations but higher intensities compared to SPIs.
The integration of soil moisture data into GEE and the developed tools facilitated easier drought impact assessment and planning.

Conclusions:

The developed soil moisture products and tools, accessible via GEE, significantly improve drought monitoring and risk assessment capabilities.
GEE enhances the accessibility of Earth observation data and processing tools, particularly benefiting users in developing countries with limited resources.
The study highlights the utility of satellite-derived soil moisture for agricultural drought early warning systems and improved crop forecasting.