Analysis of Large Scale Spatial Variability of Soil Moisture Using a Geostatistical Method

Tarendra Lakhankar, Andrew S. Jones, Cynthia L. Combs, Manajit Sengupta, Thomas Haar vonder Haar, Reza Khanbilvardi

Research output: Contribution to journalArticlepeer-review

40 Scopus Citations


Spatial and temporal soil moisture dynamics are critically needed to improve the parameterization for hydrological and meteorological modeling processes. This study evaluates the statistical spatial structure of large-scale observed and simulated estimates of soil moisture under pre- and post-precipitation event conditions. This large scale variability is a crucial in calibration and validation of large-scale satellite based data assimilation systems. Spatial analysis using geostatistical approaches was used to validate modeled soil moisture by the Agriculture Meteorological (AGRMET) model using in situ measurements of soil moisture from a state-wide environmental monitoring network (Oklahoma Mesonet). The results show that AGRMET data produces larger spatial decorrelation compared to in situ based soil moisture data. The precipitation storms drive the soil moisture spatial structures at large scale, found smaller decorrelation length after precipitation. This study also evaluates the geostatistical approach for mitigation for quality control issues within in situ soil moisture network to estimates at soil moisture at unsampled stations.

Original languageAmerican English
Pages (from-to)913-932
Number of pages20
Issue number1
StatePublished - 2010

NREL Publication Number

  • NREL/JA-550-47810


  • Kriging
  • Oklahoma mesonet
  • Soil moisture
  • Variogram


Dive into the research topics of 'Analysis of Large Scale Spatial Variability of Soil Moisture Using a Geostatistical Method'. Together they form a unique fingerprint.

Cite this