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Related Experiment Video

Updated: Nov 19, 2025

In Situ Soil Moisture Sensors in Undisturbed Soils
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PASSIVE/ACTIVE MICROWAVE SOIL MOISTURE CHANGE DISAGGREGATION USING SMAPVEX12 DATA.

Bin Fang1, Venkat Lakshmi1, Thomas J Jackson2

  • 1Earth and Ocean Sciences, University of South Carolina, Columbia SC 29223.

Journal of Hydrology
|January 29, 2021
PubMed
Summary
This summary is machine-generated.

Soil moisture disaggregation using radar data improved spatial accuracy for satellite soil moisture estimates. This method enhances agricultural and hydrological studies by providing finer-scale soil moisture data.

Keywords:
Microwave soil moistureSMAPdisaggregation change detection algorithmradar backscatter

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Area of Science:

  • Remote Sensing
  • Hydrology
  • Agriculture

Background:

  • The Soil Moisture Active Passive (SMAP) satellite requires soil moisture retrieval algorithms tested under diverse conditions.
  • Aircraft-based passive/active microwave sensor data (PALS) has a coarse resolution (1600 m), unsuitable for detailed studies.
  • Soil moisture disaggregation can enhance spatial resolution for satellite-derived soil moisture data.

Purpose of the Study:

  • To disaggregate coarse-resolution SMAP soil moisture data using high-resolution radar observations.
  • To evaluate the accuracy of disaggregated soil moisture estimates against ground-based measurements.
  • To assess the impact of Vegetation Water Content (VWC) on the disaggregation algorithm's performance.

Main Methods:

  • Utilized the SMAP Validation Experiment 2012 (SMAPVEX12) data from Manitoba, Canada.
  • Employed a change detection algorithm to disaggregate PALS radiometer soil moisture retrievals using UAVSAR radar backscatter coefficients.
  • Validated disaggregated soil moisture at 5 m and 800 m resolutions against in situ ground measurements.

Main Results:

  • Disaggregated soil moisture estimates at 5 m / 800 m resolutions better characterized spatial variabilities.
  • Disaggregated data showed good agreement with in situ measurements.
  • Vegetation Water Content (VWC) had a minimal impact on the disaggregation algorithm's performance (R2: 0.628-0.794).

Conclusions:

  • Soil moisture disaggregation effectively enhances the spatial resolution of satellite-derived soil moisture data.
  • The developed algorithm provides accurate soil moisture estimates suitable for agricultural, meteorological, and hydrological applications.
  • The disaggregation method is robust across varying Vegetation Water Content levels.