Geostatistical methods to identify and map spatial variations of soil salinity

Paz, Juan Pablo; Mateu, Jorge; Jordán, M. M.; Mataix-Solera, Jorge; Melendez-Pastor, Ignacio; Pedreño, José Navarro

doi:10.1016/j.gexplo.2010.10.003

Cited by 47 publications

(26 citation statements)

References 15 publications

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“…Geostatistics is a branch of statistics focusing on theory and methods for spatial or spatiotemporal analyses with wide application in environmental surveys (Juan et al 2010). It is intimately related to interpolation methodology, but extends far beyond simple interpolation problems.…”

Section: Geostatistical Analysis With Arcgismentioning

confidence: 99%

Comparison of ArcGIS and SAS Geostatistical Analyst to Estimate Population-Weighted Monthly Temperature for US Counties

Xiaopeng

Liang

Barker

et al. 2012

Journal of Resources and Ecology

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Temperature changes are known to have significant impacts on human health. Accurate estimates of population-weighted average monthly air temperature for US counties are needed to evaluate temperature's association with health behaviours and disease, which are sampled or reported at the county level and measured on a monthly-or 30-day-basis. Most reported temperature estimates were calculated using ArcGIS, relatively few used SAS. We compared the performance of geostatistical models to estimate population-weighted average temperature in each month for counties in 48 states using ArcGIS v9.3 and SAS v 9.2 on a CITGO platform. Monthly average temperature for Jan-Dec 2007 and elevation from 5435 weather stations were used to estimate the temperature at county population centroids. County estimates were produced with elevation as a covariate. Performance of models was assessed by comparing adjusted R 2 , mean squared error, root mean squared error, and processing time. Prediction accuracy for split validation was above 90% for 11 months in ArcGIS and all 12 months in SAS. Cokriging in SAS achieved higher prediction accuracy and lower estimation bias as compared to cokriging in ArcGIS. County-level estimates produced by both packages were positively correlated (adjusted R 2 range=0.95 to 0.99); accuracy and precision improved with elevation as a covariate. Both methods from ArcGIS and * Corresponding author: WEI Liang. LWei1@cdc.gov.. § These authors contributed equally to this work.Note: the findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. HHS Public AccessAuthor manuscript J Resour Ecol. Author manuscript; available in PMC 2015 July 09. Author Manuscript Author ManuscriptAuthor ManuscriptAuthor Manuscript SAS are reliable for U.S. county-level temperature estimates; However, ArcGIS's merits in spatial data pre-processing and processing time may be important considerations for software selection, especially for multi-year or multi-state projects.Keywords temperature estimation; county data; ArcGIS; SAS; cokriging BackgroundSpatial data analysis has received considerable attention and played an important role in disciplines of environmental science and socio-economic science due to the rapid development of Geographic Information Systems (GIS) in recent years. The need for reliable environmental geospatial databases is fast-growing (Croner et al. 1996). Ecology is the scientific study of the relations that people have with respect to each other and their natural environment. The environment is dynamically interlinked, imposed upon and constrains people at any time throughout their life. Meteorological measurements such as temperature and precipitation are needed to assess links between the environment and diseases in the population. Various spatial interpolation methods including inverse distance weighting (IDW), multiple regression, thin plate smoothing spline (TPSS), kriging and cokriging...

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Section: Geostatistical Analysis With Arcgismentioning

confidence: 99%

Comparison of ArcGIS and SAS Geostatistical Analyst to Estimate Population-Weighted Monthly Temperature for US Counties

Xiaopeng

Liang

Barker

et al. 2012

Journal of Resources and Ecology

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“…Mango trees in this condition can present scorched leaf tips and margins, leaf curling, and in severe cases reduced growth, abscission of leaves and death of trees (Zuazo et al, 2004). Environmental variables usually show spatial dependencies among observations, which encourage researchers to use geostatistical methods to model the spatial distribution of observations (Juan et al, 2011). Salinity is a variable in space and time due to the dynamic nature of the effects and interactions of various edaphic-climatic factors (soil permeability, groundwater level, amount and distribution of rainfall, relative humidity, temperature, etc.)…”

Section: Introductionmentioning

confidence: 99%

“…Some works have studied the spatial distribution of soil salinity using geostatistics analysis. Juan et al (2011) studied spatial variability of soil salinity (EC measurement) in a Mediterranean province of Southeast Spain through kriging and cokriging. They verified that soil salinity can change abruptly due to local characteristics.…”

Section: Introductionmentioning

confidence: 99%

Spatial analysis of soil salinity in a mango irrigated area in semi-arid climate region

Rodrigues¹,

Alves²,

Cunha³

et al. 2018

Aust J Crop Sci

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The salinization of irrigated areas in the semi-arid region of Brazil causes major problem for fruit production. Defining the spatial distribution of soil salinity provides important insight technique for improving the management of irrigated fruit-production areas. This work aimed to (1) assess the spatial distribution of soil salinity and (2) delineate management zones in an irrigated mango orchard in the semi-arid region based on soil salinity data. The experimental site in Juazeiro County, Bahia state, Brazil was defined by two sample grids, one under the canopy of the mango trees and the other in the row spacing with 96 georeferenced points spaced 8 x 8 m apart. Disturbed-soil cores were taken from the 0 to 0.2 m layer at each point. Soil texture, electrical conductivity (EC), exchangeable cations, and soluble cations were determined. Data were analyzed using descriptive data and geostatistical analysis. Soil salinity showed spatial dependence for samples collected in the row spacing of the mango trees. Exchangeable Na, and soluble Na, Ca, and Mg were the attributes that best defined the spatial distribution of soil salinity. Three management zones in the studied area were defined based on soil EC values, in which 63% classified as salic, 34% saline and 3% non-saline. This study clearly shows that soil-salinity problems associated with the development of mango plants may be due to an accumulation of salts in the spacing region between the plants.

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“…This step that tries to get the sampling process of soil is the most efficient and economical in precision agriculture. The costs of the soil samples are higher, making propagation difficult for most of the farmers (Juan et al, 2011;Rodrigues et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…The sampling techniques in most cases cause system series. Some of the experimental character and others already in commercial use aim to develop indirect techniques for measuring soil proprieties (Juan et al, 2011). In this way, the use of geophysics methods for measuring soil properties, and electromagnetic induction technique for measuring the apparent electrical conductivity of soil (EC a ) are really important for digital mapping soil, as it determines soil properties, with which EC a is directly related (Heil and Schmidhalter, 2012;Doolittle and Brevik, 2014).…”

Section: Introductionmentioning

confidence: 99%

Measurement of apparent electrical conductivity of soil and the spatial variability of soil chemical properties by electromagnetic induction

Siqueira¹,

Dafonte²,

González³

et al. 2016

Afr. J. Agric. Res.

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The aim of this research is to evaluate the relationship between the measurement of apparent electrical conductivity of soil and its chemical attributes by electromagnetic induction. Geophysics methods used for soil measurement, with electromagnetic induction technique for measuring apparent electrical conductivity of the soil (EC a ) is important for soil digital mapping, as it determines soil properties, with which EC a is directly related. The apparent soil electrical conductivity (EC a ) was measured by electromagnetic induction with EM38-DD device (Geonics Ltd) at two depths: Vertical dipole (effective depth of 1.5 m-EC a -V) and horizontal dipole (effective depth of 0.4 m -EC a -H) in 6 ha of land located in the Northwest of Spain (Castro de Ribeiras de Lea, Lugo) on several dates. The experimental semivariogram showed that there was a drift for EC a -V and EC a -H data. Soil chemical properties were shown in 23/06/2008 following the sample scheme building by ESAP program (40 spots). At the 40 optimized sampling points, the following soil properties were measured at 0.0 to 0.3 m depth: Organic matter (OM), pH in CaCl 2 , phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), potential acidity (H+Al), sum of the basis (SB), cation exchange capacity (CEC) and percent of base saturation (V%). The moderate negative correlation coefficient was found between Log EC a -V and organic matter. Both EC a -H and EC a -V exhibited comparatively low correlations with the chemical properties of soil.

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Geostatistical methods to identify and map spatial variations of soil salinity

Cited by 47 publications

References 15 publications

Comparison of ArcGIS and SAS Geostatistical Analyst to Estimate Population-Weighted Monthly Temperature for US Counties

Comparison of ArcGIS and SAS Geostatistical Analyst to Estimate Population-Weighted Monthly Temperature for US Counties

Spatial analysis of soil salinity in a mango irrigated area in semi-arid climate region

Measurement of apparent electrical conductivity of soil and the spatial variability of soil chemical properties by electromagnetic induction

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