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In Situ Soil Moisture Sensors in Undisturbed Soils
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Estimating surface soil moisture from SMAP observations using a Neural Network technique.

J Kolassa1,2, R H Reichle2, Q Liu3,2

  • 1Universities Space Research Association/NPP, Columbia, MD, USA.

Remote Sensing of Environment
|January 2, 2018
PubMed
Summary
This summary is machine-generated.

A new Neural Network (NN) algorithm accurately estimates global soil moisture using satellite data. This method shows high skill, outperforming existing models in certain conditions and offering potential for improved climate modeling.

Keywords:
SMAPdata assimilationmicrowave radiometersoil moisture remote sensing

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Area of Science:

  • Earth Science
  • Remote Sensing
  • Climate Modeling

Background:

  • Accurate global surface soil moisture data is crucial for climate and hydrological studies.
  • Existing satellite-based soil moisture retrieval methods face challenges, particularly in densely vegetated areas.

Purpose of the Study:

  • To develop and evaluate a Neural Network (NN) algorithm for estimating global surface soil moisture.
  • To assess the performance of the NN algorithm against in situ measurements and other satellite products.

Main Methods:

  • Utilized passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite.
  • Integrated surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) and vegetation water content.
  • Trained the NN algorithm on GEOS-5 soil moisture data for consistency with the GEOS-5 climatology.

Main Results:

  • The NN algorithm achieved high accuracy, with an average unbiased root mean square error (ubRMSE) of 0.037 m³m⁻³ against core validation sites and 0.026 m³m⁻³ against ISMN data.
  • NN retrievals demonstrated significantly higher skill than GEOS-5 model estimates and comparable or better performance than the SMAP Level-2 Passive (L2P) product, especially in challenging conditions.
  • Triple collocation analysis indicated lower error in NN retrievals compared to L2P, particularly in densely vegetated and transition zones.

Conclusions:

  • The developed NN algorithm provides a feasible and skillful method for global surface soil moisture estimation.
  • The NN approach shows promise for assimilation into land modeling systems due to its consistency with GEOS-5 climatology.
  • This method offers a valuable alternative for soil moisture retrieval, especially where ancillary parameters for physical models are uncertain.