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Updated: May 25, 2026
07:11
Sampling Soils in a Heterogeneous Research Plot
Published on: January 7, 2019
Analysis of large scale spatial variability of soil moisture using a geostatistical method
Tarendra Lakhankar1, Andrew S Jones, Cynthia L Combs
1NOAA-Cooperative Remote Sensing Science & Technology Center, (NOAA-CREST), City University of New York, NY 10031, USA. tlakhankar@ccny.cuny.edu
Sensors (Basel, Switzerland)
|February 9, 2012
Summary
This study validates the Agriculture Meteorological (AGRMET) model using soil moisture data. Precipitation significantly impacts soil moisture spatial structure, with smaller decorrelation lengths observed post-storm.
Area of Science:
- Hydrology
- Environmental Science
- Meteorology
Background:
- Accurate soil moisture dynamics are essential for hydrological and meteorological modeling.
- Large-scale soil moisture variability is critical for calibrating and validating satellite-based data assimilation systems.
Purpose of the Study:
- To evaluate the statistical spatial structure of large-scale soil moisture estimates.
- To validate the Agriculture Meteorological (AGRMET) model using in situ data.
- To assess the impact of precipitation on soil moisture spatial patterns.
Main Methods:
- Geostatistical approaches were employed for spatial analysis.
- In situ soil moisture data from the Oklahoma Mesonet were used for validation.
- Comparison of observed and AGRMET-simulated soil moisture spatial structures.
Main Results:
- AGRMET model data exhibited greater spatial decorrelation than in situ data.
- Precipitation events were found to drive large-scale soil moisture spatial structures.
- A decrease in decorrelation length was observed after precipitation events.
Conclusions:
- The AGRMET model requires refinement for large-scale soil moisture simulation.
- Precipitation plays a key role in shaping soil moisture spatial variability.
- Geostatistical methods can aid in quality control and data imputation for soil moisture networks.
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