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Related Experiment Video

Updated: Feb 4, 2026

In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

Estimating Basin-Scale Water Budgets with SMAP Soil Moisture Data.

Randal D Koster¹, Wade T Crow², Rolf H Reichle¹

¹Global Modeling and Assimilation Office, NASA/GSFC, Greenbelt, Maryland.

Water Resources Research

|October 16, 2018

Summary

Soil Moisture Active Passive (SMAP) retrievals can estimate precipitation and streamflow. This study shows SMAP data alone can inform large-scale water budget analyses, even with limitations during high rainfall periods.

Keywords:

1855 Remote sensing (1640, 4337)1866 Soil moisture 1876 Water budgets

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Area of Science:

Hydrology
Remote Sensing
Water Resource Management

Background:

Soil moisture data is crucial for understanding water budgets.
Existing methods for estimating precipitation and streamflow often require extensive ground-based data.
Satellite-based soil moisture data offers a potential alternative for large-scale hydrological assessments.

Purpose of the Study:

To assess the capability of Soil Moisture Active Passive (SMAP) Level-2 soil moisture retrievals to estimate precipitation and streamflow.
To evaluate the accuracy and transferability of SMAP-derived hydrological estimates in medium-sized watersheds.
To determine if SMAP data alone can be used for large-scale water budget analysis.

Main Methods:

Utilized SMAP Level-2 soil moisture retrievals from 2015-2017 for 145 watersheds in the conterminous US.
Developed and calibrated precipitation and streamflow estimation algorithms using SMAP data and observed hydrological variables.
Applied calibrated algorithms to SMAP data alone to estimate 10-day warm-season precipitation and streamflow totals.

Main Results:

Average estimation accuracy (r²) was 0.53 for precipitation and 0.22 for streamflow across all basins.
For well-calibrated basins, accuracy improved to 0.63 for precipitation and 0.51 for streamflow.
Identified biases during high rainfall periods but demonstrated potential for transferability of streamflow models between basins.

Conclusions:

SMAP soil moisture retrievals contain sufficient information for estimating large-scale water budgets independently.
The developed algorithms show promise for operational hydrological monitoring and water resource management.
Further research is needed to mitigate biases during extreme rainfall events and enhance model transferability.