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Titre :
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Spatial prediction of soil salinity using electromagnetic induction techniques, 1: Statistical prediction models, a comparison of multiple linear regression and cokriging, 2: An efficient spatial sampling algorithm sintable for multiple linear regression model identification and estimation
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Auteurs :
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S. Lesch ;
D. Strauss ;
J. Rhoades
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Type de document :
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article/chapitre/communication
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Editeur :
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Washington [USA] : AGU, 1994
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Format :
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p. 373-398
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Langues:
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= Anglais
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Mots-clés:
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SOL HALOMORPHE
;
SALINITE
;
PREVISION
;
MODELE
;
ALGORITHME
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Résumé :
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1.-We describe a regression-based statistical methodology suitable for predicting field scale spatial salinity (ECe) condition from rapidly acquired electromagnetic induction (ECa) data. This technique uses multiple linear regression (MLR) models to estimate soil salinity from ECa survey date. The MLR models incorporate multiple ECa measurements and trend surface parameters to increase the prediction accuracy and can be fitted from limited amounts of ECe calibration data. This estimation technique is compared to some commonly recommended cokriging techniques, with respect to statistical modeling assumptions, calibration sample size requirements, and prediction capabilities. We show that MLR models are theoretically equivalent to and cost-effective relative to cokriging for estimating a spatially distributed random variable when the residuals from the regression model are spatially uncorrelated. MLR modeling and prediction techniques are demonstrated with data from three salinity surveys. 2.-In our companion paper we described a regression-based statistical methodology for predicting field scale salinity (ECe) patterns from rapidily acquired electromagnetic induction (ECa) measurements. This technique used multiple linear regression (MLR) models to construct both point and conditional probability estimates of soil salinity from ECa survey data. In this paper we introduce a spatial site selection algorithm designed to identify a minimal number of calibration sites for MLR model estimation. The algorithm selects sites that are spatially representative of the entire survey area and simultaneously facilitate the accurate estimation of model parameters. Additionally, we introduce two statistical criteria that are useful for selecting optimal MLR variable combinations, describe a technique for identifying faulty signal data, and explore some of the differences between our recommended model-based sampling plan are some more commonly used design-based sampling plans. Survey data from two of the fields analyzed in the previous paper are used to demonstrate these techniques.
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Source :
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Water Resources Research, vol. 31, février 1995, no 2
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