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Estimating soil salinity from remote sensing data in corn fields




Eldeiry, Ahmed, author
Garcia Luis A., author
Reich, Robin M., author
Colorado State University, publisher

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This 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).


2005 annual AGU hydrology days was held at Colorado State University on March 7 - March 9, 2005.
Includes bibliographical references.
Ahmed Eldeiry is mispelled as Ahmed Eldiery.

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