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Updated: Aug 7, 2025

In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

Retrieving Soil Physical Properties by Assimilating SMAP Brightness Temperature Observations into the Community Land

Hong Zhao¹, Yijian Zeng¹, Xujun Han²

¹Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands.

Sensors (Basel, Switzerland)

|March 11, 2023

Summary

This study improved soil property retrieval using Soil Moisture Active and Passive (SMAP) data assimilation. While soil properties were enhanced, soil moisture and flux estimates still require model improvements.

Keywords:

CLM SMAP brightness temperature data assimilation soil properties uncertainties unified passive and active microwave observation operator

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Area of Science:

Earth and Environmental Sciences
Remote Sensing
Land Surface Modeling

Background:

Accurate land surface modeling requires precise soil property information.
Soil Moisture Active and Passive (SMAP) satellite data offers valuable insights into soil conditions.

Purpose of the Study:

To integrate a microwave observation operator with the Community Land Model (CLM) for data assimilation.
To investigate the impact of SMAP brightness temperature assimilation on soil property and moisture retrieval.

Main Methods:

Coupling of a physically-based discrete emission-scattering model with the CLM.
Implementation of the Local Ensemble Transform Kalman Filter (LETKF) algorithm for data assimilation.
Assimilation of SMAP brightness temperatures (TBH and TBV) using in situ data at the Maqu site.

Main Results:

Significant reduction in root mean square errors for retrieved clay and sand fractions.
TBH assimilation reduced clay fraction RMSE by over 48%; TBV assimilation reduced sand and clay RMSE by 36% and 28%, respectively.
Despite improved soil properties, discrepancies remain in soil moisture and land surface flux estimates.

Conclusions:

Accurate soil property retrieval alone is insufficient for improving soil moisture and flux estimates.
Uncertainties within the CLM model structure, such as fixed PTF structures, need to be addressed.
Further model development is necessary to mitigate identified discrepancies and enhance land surface modeling accuracy.