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Estimating soil salinity from remote sensing data in corn fields

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dc.contributor.authorEldeiry, Ahmed, author
dc.contributor.authorGarcia Luis A., author
dc.contributor.authorReich, Robin M., author
dc.contributor.authorColorado State University, publisher
dc.date.accessioned2020-01-30T15:46:45Z
dc.date.available2020-01-30T15:46:45Z
dc.date.issued2005
dc.description2005 annual AGU hydrology days was held at Colorado State University on March 7 - March 9, 2005.
dc.descriptionIncludes bibliographical references.
dc.descriptionAhmed Eldeiry is mispelled as Ahmed Eldiery.
dc.description.abstractThis paper describes an approach to develop soil salinity maps using remotely sensed data. This approach is applied to an ongoing salinity monitoring program in a portion of the Arkansas Valley in southeastern Colorado. The approach involves integrating remote sensing data from Ikonos, GIS, and spatial analysis. The collected soil salinity data is tied to the corresponding values from the satellite image bands. The stepwise regression method is applied to find the best correlation between soil salinity data and corresponding pixel values on the satellite image bands. Two regression methods were tested with the combinations of variables: ordinary least squares (OLS) and spatial autoregressive (SAR). The results show that, the green band, the near infrared band, and the near infrared band divided by the red band ration are strongly related to soil salinity. When these variables were introduced to the OLS model, that analysis of residuals suggested that there might be some spatial dependency based on the Lagrange multiplier test. When the same variables were introduced to the SAR model, the likelihood ratio test indicated that the SAR model was a significant improvement over the OLS model. Also the SAR model has a smaller value of Akaike Information Corrected Criteria (AICC).
dc.format.mediumborn digital
dc.format.mediumproceedings (reports)
dc.identifier.urihttps://hdl.handle.net/10217/200620
dc.identifier.urihttp://dx.doi.org/10.25675/10217/200620
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.titleEstimating soil salinity from remote sensing data in corn fields
dc.title.alternativeHydrology days 2005
dc.title.alternativeAGU hydrology days 2005
dc.typeText

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