Comparison of Ordinary Kriging, Regression Kriging, and Cokriging Techniques to Estimate Soil Salinity Using LANDSAT Images
Publication: Journal of Irrigation and Drainage Engineering
Volume 136, Issue 6
Abstract
The objectives of this study are: (1) to evaluate the LANDSAT best band combinations to estimate soil salinity with different crop types; (2) to compare ordinary kriging, regression kriging, and cokriging techniques to generate accurate soil salinity maps when applied to LANDSAT images; and (3) to compare the performance of different crop types: alfalfa, cantaloupe, corn, and wheat as indicators of soil salinity. This study was conducted in an area in the southern part of the Arkansas River Basin in Colorado. Six LANDSAT images acquired during the years: 2000, 2001, 2003, 2004, 2005, and 2006 in conjunction with field data were used to estimate soil salinity in the study area. The optimal subsets of band combinations from the LANDSAT images that correlate best with the soil salinity data sets were selected. Ordinary kriging, regression kriging, and cokriging were applied to 2,914 soil salinity data points collected in alfalfa, cantaloupe, corn, and wheat fields in conjunction with the selected LANDSAT image band combination subsets. Ordinary least-squares (OLSs) were used to regress the correlated band combinations to generate a soil salinity surface. The residuals of the OLS multiple regression model were kriged and combined with the soil salinity surface generated using the OLS multiple regression model to produce the final soil salinity surface of the regression kriging model. The same LANDSAT band combinations used with the regression kriging technique were used as secondary data variables with the cokriging technique, while the soil salinity data was used as a primary variable. The results show that the best band combinations for estimating soil salinity with different crops are as follows: alfalfa [red, near infrared, and normalized difference vegetation index (NDVI)]; cantaloupe (blue and green); corn (near, thermal, and NDVI); and wheat (blue and thermal). The performance of the different geostatistical models used in this study is: (1) ordinary kriging; (2) regression kriging; and (3) cokriging. Estimation of soil salinity works best for corn, then wheat, cantaloupe, and alfalfa.
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Published In
Journal of Irrigation and Drainage Engineering
Volume 136 • Issue 6 • June 2010
Pages: 355 - 364
Copyright
© 2010 ASCE.
History
Received: Feb 23, 2009
Accepted: Dec 10, 2009
Published online: May 14, 2010
Published in print: Jun 2010
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