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Spatial modeling using remote sensing, GIS, and field data to assess crop yield and soil salinity

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dc.contributor.authorEldeiry, Ahmed, author
dc.contributor.authorGarcia, Luis, author
dc.contributor.authorColorado State University, publisher
dc.date.accessioned2020-01-29T15:33:25Z
dc.date.available2020-01-29T15:33:25Z
dc.date.issued2004
dc.description24th annual AGU hydrology days was held at Colorado State University on March 10-12, 2004.
dc.descriptionIncludes bibliographical references.
dc.description.abstractA comprehensive salinity monitoring program has been conducted in a portion of the Arkansas Valley in southeastern Colorado from 1999 to the present. This area was selected for study because it provides a good illustration of a salinity-affected area. The main objective of this presentation is to utilize spatial statistical modeling using information from remote sensing, GIS, GPS, along with field data to develop salinity maps and predict yield. The approach presented in this paper involves integrating remotely sensed data with topographical data (elevation, slope, and aspect) and field data (water table fluctuation, groundwater salinity, soil texture, yield data, and soil salinity) to establish and validate the appropriate spatial techniques to accurately predict crop yield in relation to soil salinity. For the field scale study, several fields were selected to represent different irrigation systems, soil types, and crop patterns. In each field, 7 to 15 wells were installed. At these fields, water table depth, groundwater salinity, soil salinity, and yield samples are collected regularly during the growing season. In addition to field data collection, a satellite image from IKONOS on July 11 was acquired. It has four bands (blue, green, red, and infrared) with a 4-meter spatial resolution. In this study, trend surface models, which describe the large-scale spatial variability, have been developed based on the lowest values Akaike Information Criterion Corrected (AICC) and high R2. P-Value of each related variable and for all the related variables together should be less than 0.05 to guarantee a strong relation among the variables. Also, P-Value from Moran should be greater than 0.05 to guarantee that there is no autocorrelation among the residuals.
dc.format.mediumborn digital
dc.format.mediumproceedings (reports)
dc.identifier.urihttps://hdl.handle.net/10217/200033
dc.identifier.urihttp://dx.doi.org/10.25675/10217/200033
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.titleSpatial modeling using remote sensing, GIS, and field data to assess crop yield and soil salinity
dc.title.alternativeHydrology days 2004
dc.title.alternativeAGU hydrology days 2004
dc.typeText

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