The Experts below are selected from a list of 44097 Experts worldwide ranked by ideXlab platform
Luis A. Garcia - One of the best experts on this subject based on the ideXlab platform.
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Using Geospatial Techniques and Remote Sensing to Reduce the Number of Soil Salinity Samples
2014Co-Authors: Ahmed A. Eldeiry, Luis A. GarciaAbstract:Geostatistical techniques and remote sensing are used in this study to help reduce the number of Soil Salinity samples needed for mapping Soil Salinity. Two datasets were collected in alfalfa and corn fields and satellite images with different spatial and spectral resolutions from Ikonos, Landsat, and Aster were acquired and processed. Generalized least squares (GLS) was used to regress the collected Soil Salinity samples with the selected bands; and ordinary kriging was used to krig the residuals of the GLS model. Variograms were used as indictors for using the proper number of samples needed for kriging in order to map Soil Salinity. The objectives of this study are: 1) to utilize the variograms to help reduce the number of samples needed for mapping Soil Salinity; 2) to compare different cover types (alfalfa and corn) as well as compare different satellite images in capturing the variation of Soil Salinity. The results of this study show that the variograms can be used as a good indicator to significantly reduce the number of samples needed for mapping Soil Salinity. It was determined that corn fields capture more variation in Soil Salinity than alfalfa fields. Among the different satellite images used, the IKONOS images performed the best.
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Using Indicator Kriging Technique for Soil Salinity and Yield Management
Journal of Irrigation and Drainage Engineering, 2011Co-Authors: Ahmed A. Eldeiry, Luis A. GarciaAbstract:This paper presents a practical method to manage Soil Salinity and yield in order to obtain maximum economic benefits. The method was applied to a study area located in the southeastern part of the Arkansas River Basin in Colorado where Soil Salinity is a problem in some areas. The following were the objectives of this study: (1) generate classified maps and the corresponding zones of uncertainty of expected yield potential for the main crops grown in the study area; (2) compare the expected potential productivity of different crops based on the Soil Salinity conditions; and (3) assess the expected net revenue of multiple crops under different Soil Salinity conditions. Four 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 global position system unit. Different scenarios of crops and Salinity levels were evaluated. Indicator variograms were constructed for each scenario to represent the different classes of percent yield potential based on Soil Salinity thresholds of each crop. Indicator kriging (IK) was applied to each scenario to generate maps that show the expected percent yield potential areas and the corresponding zones of uncertainty for each of the different classes. Expected crop net revenue for each scenario was calculated and all the results were compared to determine the best scenarios. The results of this study show that IK 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. 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.
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comparison of ordinary kriging regression kriging and cokriging techniques to estimate Soil Salinity using landsat images
Journal of Irrigation and Drainage Engineering-asce, 2010Co-Authors: Ahmed A. Eldeiry, Luis A. GarciaAbstract:The objectives of this study are: (1) to evaluate the LANDSAT best band combinations to estimate Soil Salinity with different crop types; (2) to compare ordinary kriging, regression kriging, and cokriging techniques to generate accurate Soil Salinity maps when applied to LANDSAT images; and (3) to compare the performance of different crop types: alfalfa, cantaloupe, corn, and wheat as indicators of Soil Salinity. 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 subsets of band combinations from the LANDSAT images that correlate best with the Soil Salinity data sets were selected. Ordinary kriging, regression kriging, and cokriging were applied to 2,914 Soil Salinity data points collected in alfalfa, cantaloupe, corn, and wheat fields in conjunction with the selected LANDSAT ima...
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Using a Geo-statistical Approach for Soil Salinity and Yield Management
2010Co-Authors: Ahmed A. Eldeiry, Luis A. GarciaAbstract:This paper presents a practical method to manage Soil Salinity and yield in order to obtain maximum economic benefits. The method was applied to 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 following were the objectives: 1) generate classified maps and the corresponding zones of uncertainty of expected yield potential for the main crops grown in the study area; 2) compare the expected potential productivity of different crops based on the Soil Salinity conditions; 3) assess the expected net revenue of multiple crops under different Soil Salinity conditions. Different scenarios of crops and Salinity levels were evaluated. Indicator kriging was applied to each scenario to generate maps that show the expected percent yield potential areas and the corresponding zones of uncertainty for each of the different classes. The results of this study show that indicator kriging 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 indicator kriging and therefore it can be used for risk assessment of the percent yield potential. 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.
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Mapping Soil Salinity Using Soil Salinity Samples and Variograms: Case Study in the Lower Arkansas Basin
2008Co-Authors: Ahmed A. Eldeiry, Luis A. GarciaAbstract: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 krig- ing model was used to evaluate the relationship between Soil Salinity and a number of satellite im- ages. 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 meas- ured 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.
Bai Zhang - One of the best experts on this subject based on the ideXlab platform.
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mapping Soil Salinity sodicity by using landsat oli imagery and plsr algorithm over semiarid west jilin province china
Sensors, 2018Co-Authors: Hao Yu, Baojia Du, Zongming Wang, Liangjun Hu, Bai ZhangAbstract:Soil Salinity and sodicity can significantly reduce the value and the productivity of affected lands, posing degradation, and threats to sustainable development of natural resources on earth. This research attempted to map Soil Salinity/sodicity via disentangling the relationships between Landsat 8 Operational Land Imager (OLI) imagery and in-situ measurements (EC, pH) over the west Jilin of China. We established the retrieval models for Soil Salinity and sodicity using Partial Least Square Regression (PLSR). Spatial distribution of the Soils that were subjected to hybridized Salinity and sodicity (HSS) was obtained by overlay analysis using maps of Soil Salinity and sodicity in geographical information system (GIS) environment. We analyzed the severity and occurring sizes of Soil Salinity, sodicity, and HSS with regard to specified Soil types and land cover. Results indicated that the models’ accuracy was improved by combining the reflectance bands and spectral indices that were mathematically transformed. Therefore, our results stipulated that the OLI imagery and PLSR method applied to mapping Soil Salinity and sodicity in the region. The mapping results revealed that the areas of Soil Salinity, sodicity, and HSS were 1.61 × 106 hm2, 1.46 × 106 hm2, and 1.36 × 106 hm2, respectively. Also, the occurring area of moderate and intensive sodicity was larger than that of Salinity. This research may underpin efficiently mapping regional Salinity/sodicity occurrences, understanding the linkages between spectral reflectance and ground measurements of Soil Salinity and sodicity, and provide tools for Soil Salinity monitoring and the sustainable utilization of land resources.
Konstantin Ivushkin - One of the best experts on this subject based on the ideXlab platform.
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Global mapping of Soil Salinity change
Remote Sensing of Environment, 2019Co-Authors: Konstantin Ivushkin, Harm Bartholomeus, Arnold K. Bregt, Alim Pulatov, Bas Kempen, Luís Moreira De SousaAbstract:Abstract Soil Salinity increase is a serious and global threat to agricultural production. The only database that currently provides Soil Salinity data with global coverage is the Harmonized World Soil Database, but it has several limitations when it comes to Soil Salinity assessment. Therefore, a new assessment is required. We hypothesized that combining Soil properties maps with thermal infrared imagery and a large set of field observations within a machine learning framework will yield a global Soil Salinity map. The thermal infrared imagery acts as a dynamic variable and allows us to characterize the Soil Salinity change. For this purpose we used Google Earth Engine computational environment. The random forest classifier was trained using 7 Soil properties maps, thermal infrared imagery and the ECe point data from the WoSIS database. In total, six maps were produced for 1986, 2000, 2002, 2005, 2009, 2016. The validation accuracy of the resulting maps was in the range of 67–70%. The total area of salt affected lands by our assessment is around 1 billion hectares, with a clear increasing trend. Comparison with 3 studies investigating local trends of Soil Salinity change showed that our assessment was in correspondence with 2 of these studies. The global map of Soil Salinity change between 1986 and 2016 was produced to characterize the spatial distribution of the change. We conclude that combining Soil properties maps and thermal infrared imagery allows mapping of Soil Salinity development in space and time on a global scale.
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satellite thermography for Soil Salinity assessment of cropped areas in uzbekistan
Land Degradation & Development, 2017Co-Authors: Konstantin Ivushkin, Harm Bartholomeus, A K Bregt, Alim PulatovAbstract:A change of canopy temperature can indicate stress in vegetation. Use of canopy temperature to assess salt stress in specific plant species has been well studied in laboratory and greenhouse experiments, but its potential for use in landscape-level studies using remote sensing techniques has not yet been explored. Our study investigated the application of satellite thermography to assess Soil Salinity of cropped areas at the landscape level. The study region was Syrdarya Province, a salt-affected, irrigated semi-arid province of Uzbekistan planted mainly to cotton and wheat. We used moderate-resolution imaging spectroradiometer satellite images as an indicator for canopy temperature and the provincial Soil Salinity map as a ground truth dataset. Using analysis of variance, we examined relations among the Soil Salinity map and canopy temperature, normalized difference vegetation index, enhanced vegetation index, and digital elevation model. The results showed significant correlations between Soil Salinity and canopy temperature, but the strength of the relation varied over the year. The strongest relation was observed for cotton in September. The calculated F values were higher for canopy temperature than for the other indicators investigated. Our results suggest that satellite thermography is a valuable landscape-level approach for detecting Soil Salinity in areas under agricultural crops. © 2016 The Authors. Land Degradation & Development Published by John Wiley & Sons Ltd.
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Thermography for Soil Salinity assessment
1Co-Authors: Konstantin IvushkinAbstract:Increased Soil Salinity is a significant agricultural problem that decreases yields for common crops. It is quite dynamic in time, which makes timely Soil Salinity data a crucial point in agricultural management. Remote sensing can provide the necessary spatial and temporal resolution, but widely accepted methods and techniques for Soil Salinity monitoring using remote sensing are not present yet. Canopy temperature change is one of the stress indicators in plants. Its behaviour in response to salt stress on individual plant level is well studied, but its potential for field or landscape scale studies is not investigated yet. In this study, potential of satellite and UAV thermography for plot, regional and global scale Soil Salinity assessment was investigated. The results demonstrated that using thermography for Soil Salinity monitoring is a valuable approach. It proved to be more universal, compared with previously used approaches, like vegetation indices. The universality has been reflected both in the diverse Soil and vegetation conditions, under which the thermography approach was tested.
Ahmed A. Eldeiry - One of the best experts on this subject based on the ideXlab platform.
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Using Geospatial Techniques and Remote Sensing to Reduce the Number of Soil Salinity Samples
2014Co-Authors: Ahmed A. Eldeiry, Luis A. GarciaAbstract:Geostatistical techniques and remote sensing are used in this study to help reduce the number of Soil Salinity samples needed for mapping Soil Salinity. Two datasets were collected in alfalfa and corn fields and satellite images with different spatial and spectral resolutions from Ikonos, Landsat, and Aster were acquired and processed. Generalized least squares (GLS) was used to regress the collected Soil Salinity samples with the selected bands; and ordinary kriging was used to krig the residuals of the GLS model. Variograms were used as indictors for using the proper number of samples needed for kriging in order to map Soil Salinity. The objectives of this study are: 1) to utilize the variograms to help reduce the number of samples needed for mapping Soil Salinity; 2) to compare different cover types (alfalfa and corn) as well as compare different satellite images in capturing the variation of Soil Salinity. The results of this study show that the variograms can be used as a good indicator to significantly reduce the number of samples needed for mapping Soil Salinity. It was determined that corn fields capture more variation in Soil Salinity than alfalfa fields. Among the different satellite images used, the IKONOS images performed the best.
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Using Indicator Kriging Technique for Soil Salinity and Yield Management
Journal of Irrigation and Drainage Engineering, 2011Co-Authors: Ahmed A. Eldeiry, Luis A. GarciaAbstract:This paper presents a practical method to manage Soil Salinity and yield in order to obtain maximum economic benefits. The method was applied to a study area located in the southeastern part of the Arkansas River Basin in Colorado where Soil Salinity is a problem in some areas. The following were the objectives of this study: (1) generate classified maps and the corresponding zones of uncertainty of expected yield potential for the main crops grown in the study area; (2) compare the expected potential productivity of different crops based on the Soil Salinity conditions; and (3) assess the expected net revenue of multiple crops under different Soil Salinity conditions. Four 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 global position system unit. Different scenarios of crops and Salinity levels were evaluated. Indicator variograms were constructed for each scenario to represent the different classes of percent yield potential based on Soil Salinity thresholds of each crop. Indicator kriging (IK) was applied to each scenario to generate maps that show the expected percent yield potential areas and the corresponding zones of uncertainty for each of the different classes. Expected crop net revenue for each scenario was calculated and all the results were compared to determine the best scenarios. The results of this study show that IK 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. 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.
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comparison of ordinary kriging regression kriging and cokriging techniques to estimate Soil Salinity using landsat images
Journal of Irrigation and Drainage Engineering-asce, 2010Co-Authors: Ahmed A. Eldeiry, Luis A. GarciaAbstract:The objectives of this study are: (1) to evaluate the LANDSAT best band combinations to estimate Soil Salinity with different crop types; (2) to compare ordinary kriging, regression kriging, and cokriging techniques to generate accurate Soil Salinity maps when applied to LANDSAT images; and (3) to compare the performance of different crop types: alfalfa, cantaloupe, corn, and wheat as indicators of Soil Salinity. 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 subsets of band combinations from the LANDSAT images that correlate best with the Soil Salinity data sets were selected. Ordinary kriging, regression kriging, and cokriging were applied to 2,914 Soil Salinity data points collected in alfalfa, cantaloupe, corn, and wheat fields in conjunction with the selected LANDSAT ima...
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Using a Geo-statistical Approach for Soil Salinity and Yield Management
2010Co-Authors: Ahmed A. Eldeiry, Luis A. GarciaAbstract:This paper presents a practical method to manage Soil Salinity and yield in order to obtain maximum economic benefits. The method was applied to 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 following were the objectives: 1) generate classified maps and the corresponding zones of uncertainty of expected yield potential for the main crops grown in the study area; 2) compare the expected potential productivity of different crops based on the Soil Salinity conditions; 3) assess the expected net revenue of multiple crops under different Soil Salinity conditions. Different scenarios of crops and Salinity levels were evaluated. Indicator kriging was applied to each scenario to generate maps that show the expected percent yield potential areas and the corresponding zones of uncertainty for each of the different classes. The results of this study show that indicator kriging 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 indicator kriging and therefore it can be used for risk assessment of the percent yield potential. 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.
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Mapping Soil Salinity Using Soil Salinity Samples and Variograms: Case Study in the Lower Arkansas Basin
2008Co-Authors: Ahmed A. Eldeiry, Luis A. GarciaAbstract: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 krig- ing model was used to evaluate the relationship between Soil Salinity and a number of satellite im- ages. 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 meas- ured 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.
Alim Pulatov - One of the best experts on this subject based on the ideXlab platform.
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Global mapping of Soil Salinity change
Remote Sensing of Environment, 2019Co-Authors: Konstantin Ivushkin, Harm Bartholomeus, Arnold K. Bregt, Alim Pulatov, Bas Kempen, Luís Moreira De SousaAbstract:Abstract Soil Salinity increase is a serious and global threat to agricultural production. The only database that currently provides Soil Salinity data with global coverage is the Harmonized World Soil Database, but it has several limitations when it comes to Soil Salinity assessment. Therefore, a new assessment is required. We hypothesized that combining Soil properties maps with thermal infrared imagery and a large set of field observations within a machine learning framework will yield a global Soil Salinity map. The thermal infrared imagery acts as a dynamic variable and allows us to characterize the Soil Salinity change. For this purpose we used Google Earth Engine computational environment. The random forest classifier was trained using 7 Soil properties maps, thermal infrared imagery and the ECe point data from the WoSIS database. In total, six maps were produced for 1986, 2000, 2002, 2005, 2009, 2016. The validation accuracy of the resulting maps was in the range of 67–70%. The total area of salt affected lands by our assessment is around 1 billion hectares, with a clear increasing trend. Comparison with 3 studies investigating local trends of Soil Salinity change showed that our assessment was in correspondence with 2 of these studies. The global map of Soil Salinity change between 1986 and 2016 was produced to characterize the spatial distribution of the change. We conclude that combining Soil properties maps and thermal infrared imagery allows mapping of Soil Salinity development in space and time on a global scale.
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satellite thermography for Soil Salinity assessment of cropped areas in uzbekistan
Land Degradation & Development, 2017Co-Authors: Konstantin Ivushkin, Harm Bartholomeus, A K Bregt, Alim PulatovAbstract:A change of canopy temperature can indicate stress in vegetation. Use of canopy temperature to assess salt stress in specific plant species has been well studied in laboratory and greenhouse experiments, but its potential for use in landscape-level studies using remote sensing techniques has not yet been explored. Our study investigated the application of satellite thermography to assess Soil Salinity of cropped areas at the landscape level. The study region was Syrdarya Province, a salt-affected, irrigated semi-arid province of Uzbekistan planted mainly to cotton and wheat. We used moderate-resolution imaging spectroradiometer satellite images as an indicator for canopy temperature and the provincial Soil Salinity map as a ground truth dataset. Using analysis of variance, we examined relations among the Soil Salinity map and canopy temperature, normalized difference vegetation index, enhanced vegetation index, and digital elevation model. The results showed significant correlations between Soil Salinity and canopy temperature, but the strength of the relation varied over the year. The strongest relation was observed for cotton in September. The calculated F values were higher for canopy temperature than for the other indicators investigated. Our results suggest that satellite thermography is a valuable landscape-level approach for detecting Soil Salinity in areas under agricultural crops. © 2016 The Authors. Land Degradation & Development Published by John Wiley & Sons Ltd.
