Spatial Prediction of Soil Salinity Using Electromagnetic Induction Techniques: 2. An Efficient Spatial Sampling Algorithm Suitable for Multiple Linear Regression Model Identification and Estimation

TLDR

A regression‑based statistical method predicts field‑scale soil salinity from rapid electromagnetic induction measurements. This study introduces a spatial sampling algorithm that selects a minimal set of calibration sites for efficient multiple linear regression model estimation. The algorithm selects spatially representative sites, applies two statistical criteria for optimal variable combinations, detects faulty signal data, and is evaluated against design‑based sampling plans using survey data from two fields.

Abstract

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.