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Comparison of regression kriging and cokriging techniques to estimate soil salinity using LANDSAT images

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dc.contributor.authorEldeiry, Ahmed, author
dc.contributor.authorGarcia, Luis A., author
dc.contributor.authorColorado State University, publisher
dc.date.accessioned2020-02-06T18:38:38Z
dc.date.available2020-02-06T18:38:38Z
dc.date.issued2009
dc.description2009 annual AGU hydrology days was held at Colorado State University on March 25 - March 27, 2009.
dc.descriptionIncludes bibliographical references.
dc.description.abstractThe objectives of this study are: 1) to evaluate the best band combinations to estimate soil salinity with each crop type; 2) to compare regression kriging and cokriging techniques when applied to LANDSAT images to generate accurate soil salinity maps; and to compare the performance of different crop types: alfalfa, cantaloupe, corn, and wheat as indicators of soil salinity; and. 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 subset of band combinations from LANDSAT images that correlates best with the soil salinity data was selected. Regression kriging and cokring were applied to 2,915 soil salinity data points collected in alfalfa, cantaloupe, corn, and wheat fields in conjunction with the selected subset band combinations from the LANDSAT images. Ordinary Least Squares (OLS) was used to regress the correlated band combinations to generate a soil salinity surface. The residuals of the OLS model were kriged and combined with the soil salinity surface generated using the OLS model to produce the final regression kriging soil salinity surface. The same LANDSAT band combinations used with the regression kriging technique were used as secondary data variables with the cokriging technique, while 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 NDVI); cantaloupe (green, and near infrared); corn (near infrared, thermal, and NDVI); and wheat (blue and thermal). The regression kriging technique performed better than the cokriging technique since it was able to capture most of the small variations in soil salinity. Corn fields performed the best and alfalfa fields performed the least. Wheat comes in the second place and cantaloupe comes in the third place.
dc.format.mediumborn digital
dc.format.mediumproceedings (reports)
dc.identifier.urihttps://hdl.handle.net/10217/200716
dc.identifier.urihttp://dx.doi.org/10.25675/10217/200716
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofHydrology Days
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.titleComparison of regression kriging and cokriging techniques to estimate soil salinity using LANDSAT images
dc.title.alternativeHydrology days 2009
dc.title.alternativeAGU hydrology days 2009
dc.typeText

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