Accuracy Assessments of Stochastic and Deterministic Interpolation Methods in Estimating Soil Attributes Spatial Variability

Martins, Rodrigo Nogueira; Santos, Fernando Ferreira Lima dos; Araújo, Guilherme De Moura; Viana, Lucas de Arruda; Rosas, Jorge Tadeu Fim

doi:10.1080/00103624.2019.1670836

Cited by 10 publications

(8 citation statements)

References 28 publications

(27 reference statements)

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“…However, they did not interpolate sensor-based predictions. Different sampling designs were also compared for mapping various soil properties in References [30,57]. In Reference [30], similar to our study, soil PR, BD, and soil moisture maps derived from a dense grid of 145 samples (20 × 20 m) were more accurate than their corresponding maps derived from a thin grid of 41 samples (40 × 40 m).…”

Section: Baseline Versus Sensor-aided Mapssupporting

confidence: 69%

“…Different sampling designs were also compared for mapping various soil properties in References [30,57]. In Reference [30], similar to our study, soil PR, BD, and soil moisture maps derived from a dense grid of 145 samples (20 × 20 m) were more accurate than their corresponding maps derived from a thin grid of 41 samples (40 × 40 m). When using the sensor-based prediction on the dense grid for OK, the RMSE of validation improved, indicating more accurate maps.…”

Section: Baseline Versus Sensor-aided Mapssupporting

confidence: 69%

“…In southeast Brazil, where this study is conducted, OK was used to map ECa measured by a Veris 3100 sensor, as well as corn yield, cost, and profit in a 19-ha irrigated farm [29]. In another study in a 5.7 ha area, OK was compared to inverse distance weighting (IDW) for mapping soil penetration resistance (PR), bulk density (BD), and moisture, using two sampling grids [30]. The best interpolation method varied by soil property and sampling grid.…”

Section: Introductionmentioning

confidence: 99%

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Field Proximal Soil Sensor Fusion for Improving High-Resolution Soil Property Maps

et al. 2020

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Mapping soil properties, using geostatistical methods in support of precision agriculture and related activities, requires a large number of samples. To reduce soil sampling and measurement time and cost, a combination of field proximal soil sensors was used to predict and map laboratory-measured soil properties in a 3.4-ha pasture field in southeastern Brazil. Sensor soil properties were measured in situ on a 10 × 10-m dense grid (377 samples) using apparent electrical conductivity meters, apparent magnetic susceptibility meter, gamma-ray spectrometer, water content reflectometer, cone penetrometer, and portable X-ray fluorescence spectrometer (pXRF). Soil samples were collected on a 20 × 20-m thin grid (105 samples) and analyzed in the laboratory for organic C, sum of bases, cation exchange capacity, clay content, soil volumetric moisture, and bulk density. Another 25 samples collected throughout the area were also analyzed for the same soil properties and used for independent validation of models and maps. To test whether the combination of sensors enhances soil property predictions, stepwise multiple linear regression (MLR) models of the laboratory soil properties were derived using individual sensor covariate data versus combined sensor data—except for the pXRF data, which were evaluated separately. Then, to test whether a denser grid sample boosted by sensor-based soil property predictions enhances soil property maps, ordinary kriging of the laboratory-measured soil properties from the thin grid was compared to ordinary kriging of the sensor-based predictions from the dense grid, and ordinary cokriging of the laboratory properties aided by sensor covariate data. The combination of multiple soil sensors improved the MLR predictions for all soil properties relative to single sensors. The pXRF data produced the best MLR predictions for organic C content, clay content, and bulk density, standing out as the best single sensor for soil property prediction, whereas the other sensors combined outperformed the pXRF sensor for the sum of bases, cation exchange capacity, and soil volumetric moisture, based on independent validation. Ordinary kriging of sensor-based predictions outperformed the other interpolation approaches for all soil properties, except organic C content, based on validation results. Thus, combining soil sensors, and using sensor-based soil property predictions to increase the sample size and spatial coverage, leads to more detailed and accurate soil property maps.

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Section: Baseline Versus Sensor-aided Mapssupporting

confidence: 69%

Section: Baseline Versus Sensor-aided Mapssupporting

confidence: 69%

Section: Introductionmentioning

confidence: 99%

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Field Proximal Soil Sensor Fusion for Improving High-Resolution Soil Property Maps

et al. 2020

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“…The reduction in the number of readings per sample point also caused an increase in the error of the interpolation estimate. Moreover, this behavior was observed by Souza et al (2014) and Nogueira Martins et al (2019), who studied the effect of reducing the density of the sampling grid on spatial variability of soil attributes. As in the present study, Souza et al (2014) found that with the reduction in the number of samples, the relationship between the nugget effect and the plateau (Table 1) gradually diminishes, which characterizes the structure of spatial dependence.…”

Section: Resultsmentioning

confidence: 64%

“…The results show that for ECa, the number of readings per sample point plays a crucial role in obtaining maps with lower uncertainties. Therefore, in studies where the objective is to minimize the uncertainties of the maps generated for ECa, the density of the sampling grid, as reported by Souza et al (2014) and Nogueira Martins et al (2019), and the number of readings per sample point should be considered. Thus, the results indicate that there may be a relationship between sample and subsample densities (number of readings per sampling point) that may influence the elaboration of the maps.…”

Section: Resultsmentioning

confidence: 99%

Spatial Variability of Soil Apparent Electrical Conductivity - Effect of the Number of Subsamples

et al. 2021

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Soil apparent electrical conductivity (ECa) sensors have been used to detect spatial variability because they correlate with soil attributes. Studies with soil attributes have shown that the number of subsamples and sampling points influences mapping. However, there are no studies that investigated the influence of sampling or subsampling density on ECa maps. Therefore, this study verified the influence of ECa readings per sample point on the semivariance and kriging analysis. The data were collected from an area (2.5 ha) of coffee plants. One hundred sampling points were measured considering 20 readings each. 1, 5, 10, 15, and 20 sample point readings were tested. The influence of the number of readings per sampling point on the ECa mapping was determined using linear regression analysis at a significance level of 5%. The results obtained showed that ECa readings per sampling point significantly influence ECa maps. In addition, they demonstrated that reducing the number of readings per sampling point increases prediction errors by kriging. Thus, ECa maps determined with the highest readings per sampling point were mostly accurate.

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Interpolating resident attitudes toward exurban roadside forest management

2022

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Context: Knowledge about spatial patterns of human dimensions data within landscape ecology is nascent despite its importance in natural resources management decision-making. We explored this topic within the context of utility roadside forest vegetation management, a complex situation involving ecological, cultural, and aesthetic aspects of forests and reliable power.Objectives We applied spatial interpolation to investigate patterns of human attitudes toward exurban roadside vegetation management data across an exurban landscape.Methods Mail surveys (n = 1962) were used to collect social science data from residents in four areas of Connecticut, USA. For each area, three attitudes variables were evaluated for spatial autocorrelation using Moran's I statistic. Based on identi ed autocorrelation distance or scale, attitudes were interpolated using inverse distance weighting. Model validation of interpolated surfaces was completed using root mean square error.Results: Statistically signi cant spatial autocorrelation was present for ve of 12 study area-attitude pairings at variable distances. Accuracy of interpolations also varied among study areas, suggesting that the choice of spatial scale of analysis in uenced model results.Conclusions: Social processes within the exurban landscape were spatially heterogeneous and multiscalar for the same variables in different locations, exemplifying the complexity of social processes within exurban land use. Interpolation assumptions often applied toward ecological studies did not work well for social processes studied in this analysis. Results demonstrated the importance of understanding spatial dimensions at which social processes operate and, therefore, may in uence ecological outcomes of the roadside forest within the context of state-level natural resources management and policy.

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Accuracy Assessments of Stochastic and Deterministic Interpolation Methods in Estimating Soil Attributes Spatial Variability

Cited by 10 publications

References 28 publications

Field Proximal Soil Sensor Fusion for Improving High-Resolution Soil Property Maps

Field Proximal Soil Sensor Fusion for Improving High-Resolution Soil Property Maps

Spatial Variability of Soil Apparent Electrical Conductivity - Effect of the Number of Subsamples

Interpolating resident attitudes toward exurban roadside forest management

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