Next Article in Journal
Correction: Dong, J. et al. Integrated Evaluation of Urban Development Suitability Based on Remote Sensing and GIS Techniques–A Case Study in Jingjinji Area, China. Sensors 2008, 8, 5975–5986
Previous Article in Journal
Microencapsulation of Flavors in Carnauba Wax
Article Menu

Export Article

Open AccessArticle
Sensors 2010, 10(1), 913-932; doi:10.3390/s100100913

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

1
NOAA–Cooperative Remote Sensing Science & Technology Center, (NOAA-CREST), City University of New York, NY 10031, USA
2
Cooperative Institute for Research in the Atmosphere (CIRA), Colorado State University, Fort Collins, CO 80523, USA
3
National Renewable Energy Laboratory, Golden, CO 80401, USA
*
Author to whom correspondence should be addressed.
Received: 28 December 2009 / Revised: 11 January 2010 / Accepted: 19 January 2010 / Published: 25 January 2010
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [484 KB, uploaded 21 June 2014]   |  

Abstract

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. View Full-Text
Keywords: soil moisture; kriging; variogram; AGRMET; Oklahoma Mesonet soil moisture; kriging; variogram; AGRMET; Oklahoma Mesonet
Figures

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Lakhankar, T.; Jones, A.S.; Combs, C.L.; Sengupta, M.; Vonder Haar, T.H.; Khanbilvardi, R. Analysis of Large Scale Spatial Variability of Soil Moisture Using a Geostatistical Method. Sensors 2010, 10, 913-932.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top