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Related Experiment Video

Updated: Dec 15, 2025

In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

Data Assimilation to extract Soil Moisture Information from SMAP Observations.

Jana Kolassa^1,2, Rolf H Reichle², Qing Liu^3,2

¹Universities Space Research Association, Columbia, MD.

Remote Sensing

|July 14, 2020

Summary

This study enhances soil moisture data by assimilating NASA

Keywords:

SMAP soil moisture bias correction data assimilation neural networks

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Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Published on: December 21, 2019

Area of Science:

Earth Science
Hydrology
Remote Sensing

Background:

Accurate soil moisture data is crucial for understanding land surface processes and climate.
The Soil Moisture Active Passive (SMAP) mission provides valuable satellite-based soil moisture observations.
Assimilation of SMAP data into land surface models can improve soil moisture estimates.

Purpose of the Study:

To compare different methods for assimilating SMAP soil moisture retrievals into the NASA Catchment model.
To evaluate the impact of neural network (NN) and physically-based retrievals with various bias correction strategies.
To assess the extraction of independent information from SMAP observations.

Main Methods:

Assimilated NN and physically-based SMAP soil moisture retrievals into the NASA Catchment model over the contiguous United States.
Employed global and localized bias correction methods for SMAP retrievals.
Validated model performance against in situ measurements from SMAP core validation sites (CVS).

Main Results:

Assimilation of NN retrievals without bias correction improved correlations and reduced ubRMSE against in situ data.
Global bias correction methods showed potential for extracting more independent information but were vulnerable to retrieval errors.
Localized bias correction yielded slightly lower skill improvements compared to global methods.

Conclusions:

Different SMAP data assimilation strategies offer varying degrees of soil moisture improvement.
Global bias correction can enhance information extraction but requires robust quality control.
Careful consideration of bias correction and land model recalibration is essential for optimal assimilation results.