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Spatial Prediction of Soil Salinity Using Electromagnetic Induction Techniques: 2. An Efficient Spatial Sampling Algorithm Suitable for Multiple Linear Regression Model Identification and Estimation
Author(s) -
Lesch Scott M.,
Strauss David J.,
Rhoades James D.
Publication year - 1995
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/94wr02180
Subject(s) - sampling (signal processing) , algorithm , linear regression , computer science , calibration , data mining , regression analysis , regression , scale (ratio) , statistics , mathematics , machine learning , filter (signal processing) , geography , computer vision , cartography
In our companion paper we described a regression‐based statistical methodology for predicting field scale salinity (EC e ) patterns from rapidly acquired electromagnetic induction (EC a ) measurements. This technique used multiple linear regression (MLR) models to construct both point and conditional probability estimates of soil salinity from EC a 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 ampling plans. Survey data from two of the fields analyzed in the previous paper are used to demonstrate these techniques.