Influence of Spatial Structure on Accuracy of Interpolation Methods

Abstract

Effectiveness of precision agriculture depends on accurate and efficient mapping of soil properties. Among the factors that most affect soil property mapping are the number of soil samples, the distance between sampling locations, and the choice of interpolation procedures. The objective of this study is to evaluate the effect of data variability and the strength of spatial correlation in the data on the performance of (i) grid soil sampling of different sampling density and (ii) two interpolation procedures, ordinary point kriging and optimal inverse distance weighting (IDW). Soil properties with coefficients of variation (CV) ranging from 12 to 67% were sampled in a 20‐ha field using a regular grid with a 30‐m distance between grid points. Data sets with different spatial structures were simulated based on the soil sample data using a simulated annealing procedure. The strength of simulated spatial structures ranged from weak with nugget to sill (N/S) ratio of 0.6 to strong (N/S ratio of 0.1). The results indicated that regardless of CV values, soil properties with a strong spatial structure were mapped more accurately than those that had weak spatial structure. Kriging with known variogram parameters performed significantly better than the IDW for most of the studied cases (P < 0.01). However, when variogram parameters were determined from sample variograms, kriging was as accurate as the IDW only for sufficiently large data sets, but was less precise when a reliable sample variogram could not be obtained from the data.

Spatial variability of vegetation index and soil properties in an integrated crop-livestock system

“…Soil OM, K, P, clay contents and base saturation represented soil properties with medium variability (CV < 30%), whereas Trotter et al (2014) had found CV ranging from 35 to 66% for P, K and S. ECa represented soil properties with high variability. According to Kravchenko (2003), the level of data variability is important for site-specific management, as soil properties with high variability are After, the monochromatic reflectance calculations of each band (ρ λi ) were achieved using Eq. 3 proposed by Allen et al (2002):…”

Modeling of multiple regression and multiple linear regressions for prediction of groundwater quality (case study: north of Shiraz)

“…IDW (Panagopoulos et al 2006) and Splines (Kravchenko 2003;Keshavarzi and Sarmadian 2010) are also commonly used classical interpolation methods to analyze the spatial variability of water properties.…”

Systematic Evaluation of Kriging and Inverse Distance Weighting Methods for Spatial Analysis of Soil Bulk Density.

“…Suggested N/S ratios generally varies from 0.01 to 1.0 with the majority of reported variograms having N/S ratios of 0.1 (strong spatial structure) to 0.6 (weak spatial structure). A higher value of N/S ratio 0.6 means that 60% of the variability in the data consists of un-explainable, short distance, random variation -corresponding to a weak spatial structure, respectively (Kravchenko 2003;Muller et al 2001;Cambardella and Karlen 1999;Chang et al 1999). In this study, the N/S ratio was 0.38 for 1999 and 0.79 for 2000, suggesting a stronger spatial structure of the bulk density in 1999 than in 2000.…”

“…Typical examples are conditions based on geostatistical concepts (kriging model), locality (nearest neighbour and finite element methods), smoothness and tension (splines), or ad-hoc functional forms (polynomials). The interpolation techniques commonly used in hydrology include inverse distance weighting (IDW) and kriging (Kravchenko 2003;Franzen and Peck 1995). Both methods estimate value of a parameter at unsampled locations based on the measurements from the surrounding locations with certain weights assigned to each of the measurements.…”

“…Inverse distance weighting is easier to implement, while kriging is more time-consuming and cumbersome; however, kriging generally provides a more accurate description of the data spatial structure, and produces valuable information about estimation error distributions (Kravchenko and Bullock 1999). A number of studies (Kravchenko 2003;Mueller et al 2001;Gotway et al 1996;Wollenhaupt et al 1994;Warrick et al 1988;Laslett et al 1987) have compared the performance of kriging and IDW methods, however, the results are rather contradictory (Kravchenko 2003;Lapen and Hayhoe 2003;Mueller et al 2001;Schloeder et al 2001).…”