Browsing by Author "Eldeiry, Ahmed, author"
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Item Open Access Comparison of regression kriging and cokriging techniques to estimate soil salinity using LANDSAT images(Colorado State University. Libraries, 2009) Eldeiry, Ahmed, author; Garcia, Luis A., author; Colorado State University, publisherThe 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.Item Open Access Estimating soil salinity from remote sensing data in corn fields(Colorado State University. Libraries, 2005) Eldeiry, Ahmed, author; Garcia Luis A., author; Reich, Robin M., author; Colorado State University, publisherThis 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).Item Open Access Estimating soil salinity using remote sensing data(Colorado State University. Libraries, 2005-02) Garcia, Luis, author; Eldeiry, Ahmed, author; Elhaddad, Ayman, authorItem Open Access Furrow irrigaton system design for clay soils in arid regions(Colorado State University. Libraries, 2004) Eldeiry, Ahmed, author; Garcia, Luis, author; El-Zaher, Ahmed Samy A., author; Kiwan, Mohammed El-Sherbini, author; Colorado State University, publisherThis paper presents a simplified method for furrow irrigation system design for clay soils in arid regions. Field experiments were conducted for a furrow irrigations system at an experimental site in Egypt with clay soils, cultivated with cotton and irrigated by a three-turn crop rotation. Several parameters were measured including the furrow geometry, slope, furrow width, furrow length, infiltration characteristics, advance time, cut-off time, depletion time, and recession time. A volume balance model was applied to simulate water flow in the furrow system and the results were compared to those obtained from the field measurements. This study shows that a volume balance model can be satisfactorily applied to clay soils and the length of the furrow and its inlet discharge are the main factors affecting application efficiency. Also, this study indicates that in order to obtain high application efficiencies, one must use low discharge rates for small furrows and as the furrow length increases the discharge must also increase, and that furrow length can be increased with higher soil moisture contents.Item Open Access Mapping soil salinity using soil salinity samples and variograms: case study in the Lower Arkansas Basin(Colorado State University. Libraries, 2008) Eldeiry, Ahmed, author; GarcÃa, Luis A., author; Colorado State University, publisherThe 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.Item Open Access Potential contribution of residuals for better prediction of soil salinity from remote sensing data(Colorado State University. Libraries, 2006) Eldeiry, Ahmed, author; Garcia Luis A., author; Colorado State University, publisherSoil salinity predictions derived from Ikonos and Landsat satellite images are compared with field-collected soil salinity data for a study area in Colorado's lower Arkansas River Basin. The accuracy of the predictions is compared and issues of price, resolution, and coverage area are considered. Stepwise regression is used to select the combination of bands in the satellite images that best correlate with the field data. The Ordinary Least Squares (OLS) model is used to predict soil salinity using the combination of bands that resulted from the stepwise regression. The residuals for the OLS model are checked for whether they are roughly normal and approximately independently distributed with a mean of 0 and whether there is some constant variance or not. If the residuals do not meet these conditions it means that there is some kind of autocorrelation among them. The SAR model is used to remove some of the autocorrelation among the residuals. If the SAR model does not give satisfactory results, then a modified kriging model is used. The residuals of the OLS model which proved to have autocorrelation can be interpolated using kriging. The final predicted surface results from combining the surface produced from the OLS model with the surface produced by the kriged residuals. The results of this methodology to predict soil salinity from remote sensing data while taking into account the importance of residuals are promising.Item Open Access Spatial modeling using remote sensing, GIS, and field data to assess crop yield and soil salinity(Colorado State University. Libraries, 2004) Eldeiry, Ahmed, author; Garcia, Luis, author; Colorado State University, publisherA 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.Item Open Access Using nonlinear geostatistical models for soil salinity and yeild management(Colorado State University. Libraries, 2013) Eldeiry, Ahmed, author; Garcia, Luis A., advisor; Reich, Robin M., advisor; Grigg, Neil S., committee memberCrop production losses associated with soil salinity on irrigated lands are significant. The genetic complexity of crops with regards to salt tolerance has limited the success of improving salt tolerance through conventional breeding programs. In the meantime, land reclamation and leaching can be expensive and sometimes impractical when fresh water sources are scarce or not readily available. This research introduces a geostatistical approach for the management of crop yield under current soil salinity conditions. It uses three nonlinear geostatistical models - disjunctive kriging (DK), indicator kriging (IK), and probability kriging (PK) - to manage soil salinity and crop yield. The nonlinear models were applied to selected irrigated fields in a study area located in the south eastern part of the Arkansas River Basin in Colorado where soil salinity is a problem in some areas. The overall objectives of this research are: 1) estimate soil salinity in irrigated fields using nonlinear gestatistical models; 2) develop conditional probability (CP) maps that divide each field into zones with different soil salinity levels; 3) estimate the expected yield potential (YP) for several crops at different zones in fields under multiple soil salinity thresholds; 4) evaluate the performance of the nonlinear geostatistical models in developing the interpolated and CP maps provide guidance to farmers and researchers by considering the output of this research as input for precision management of agriculture; and 5) provide guidance to farmers and decision makers in precision management of agriculture. The three nonlinear geostatistical models DK, IK, and PK were used to develop CP maps based on soil salinity thresholds for different crops. These CP maps were compared with actual yield data taken while conducting a soil salinity survey for two fields cultivated with alfalfa and corn. The CP maps divide each field of interest into zones with different probabilities to reach a specific YP for a given crop at a specific soil salinity threshold. Different crops were selected to represent the dominant crops grown in the study area: alfalfa, corn, sorghum, and wheat. Six fields were selected to represent the range of soil salinity levels in the area. Soil salinity data were collected in the fields using an EM-38 and the location of each soil salinity sample point was determined using a GPS unit. Datasets of soil salinity collected in irrigated fields were used to generate the CP maps and to evaluate different scenarios of the expected YP% of several crops at multiple soil salinity thresholds. These datasets were selected to represent a wide range of soil salinity conditions in order to be able to evaluate a wide variety of crops (larger set of crops than those grown in the study area) according to their soil salinity tolerances. Yield data were collected at the same fields to compare the actual data with that estimated by the models. The crops were used for evaluation were selected based on two criteria: dominant in the study area, and represent high, moderate, and low soil salinity tolerances. Different scenarios of crops and salinity levels were evaluated. Semivariograms were constructed for each scenario to represent the different classes of percent yield potential based on soil salinity thresholds of each crop. The results of this research show the nonlinear geostatistical models are efficient in assessing the impact of soil salinity on the spatial variability yield productivity. The comparison of the actual yield data with the estimated yield from the three models shows good agreement where most of the yield samples were located at the appropriate zones estimated with the models. The IK and PK models generated very similar estimates for each of the zones. However, the zones generated by both of these models are slightly different to the zones generated using the DK model. Wheat and sorghum show the highest expected yield potential based on the different soil salinity conditions that were evaluated. Expected net revenue for alfalfa and corn are the highest under the different soil salinity conditions that were evaluated. The CP maps generated using the DK technique give an accurate characterization and quantification of the different zones of the fields. Upon the knowledge of the YP% of different areas, a management decision action can be taken to manage the productivity of a field by selecting another crop or adjusting the inputs such as fertilizer, seeding rates and herbicides in low productivity areas. The information provided by the models about the variability and hotspots can be used for the precision management of agricultural resources. The IK model can be used to generate guidance maps that divide each field into areas of expected percent yield potential based on soil salinity thresholds for different crops. Zones of uncertainty can be quantified by IK and used for risk assessment of the percent yield potential.