Eldeiry, Ahmed, authorGarcía, Luis A., authorColorado State University, publisher2020-02-052020-02-052008https://hdl.handle.net/10217/200706http://dx.doi.org/10.25675/10217/2007062008 annual AGU hydrology days was held at Colorado State University on March 26 - March 28, 2008.Includes bibliographical references.The objective of this study was to develop a methodology to generate high accuracy soil salinity maps with the minimum number of soil salinity samples. Variograms are used in this study to estimate the number of soil salinity samples that need to be collected. A modified residual kriging model was used to evaluate the relationship between soil salinity and a number of satellite images. Two datasets, one representing corn fields where Aster, Landsat 7, and Ikonos images were used, and the other representing alfalfa fields where the Landsat 5 and Ikonos images were used. The satellite images were acquired from different sources to check the correlation between measured soil salinity and remote sensing data. Two strategies were applied to the datasets to produce subset samples. For the corn fields dataset, nine subsets of the data ranging from 10% to 90% of the data in 10% increments were produced. For the alfalfa fields dataset, three subsets of the data 75 %, 50%, and 25% of the data were produced. A modified residual kriging model was applied to the reduced datasets for each image. For each combination of satellite image and subset of the data, a variogram was generated and the correlation between soil salinity and the remote sensing data was evaluated. The results show that the variograms can be used to significantly reduce the number of soil salinity samples that need to be collected.born digitalproceedings (reports)engCopyright 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.Text