Geostatistical Interpolation of Soil Properties in Boom Clay in Flanders

Govaerts, Annelies; Vervoort, André

doi:10.1007/978-90-481-2322-3_20

Cited by 13 publications

(7 citation statements)

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“…Depending on the measurement error model, it may give exact or smoothed results (Fritsch et al, 2011). A variety of output surfaces including predictions, prediction standard errors, probability and quantile can be obtained with this interpolator tool (Govaerts and Vervoort, 2010). Kriging assumes the data provided come from a stationary stochastic process, and are normally-distributed (Lesch and Corwin, 2008;Tabari et al, 2011).…”

Section: Ordinary Krigingmentioning

confidence: 99%

Determining the best ISUM (Improved stock unearthing Method) sampling point number to model long-term soil transport and micro-topographical changes in vineyards

Rodrigo‐Comino

Keshavarzi

Zeraatpisheh

et al. 2019

Computers and Electronics in Agriculture

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Advances in soil erosion measuring tools and micro-topography modelling will contribute to our understanding of land degradation processes and help to design correct erosion mitigation measures in agricultural fields. Vineyards being one of the most degraded agricultural landscapes, it is necessary to accurately predict soil erosion levels within them. One possible method to achieve this goal in vine plantations is ISUM (improved stock unearthing method). To apply ISUM, it is necessary to detect the graft unions, which are recognised as passive bioindicators of the original micro-topography at the time of planting. In this paper, we propose a methodology to determine: i) how many measuring points are necessary to reach the best estimate of soil erosion for developing current soil surface level maps; and ii) which spatial interpolation method is the best to map the microtopographical changes. ISUM was applied in the Ruwer-Mosel valley vineyards (Germany) using up to 18 measuring points at 10 cm intervals between opposite pair graft unions of the inter-row vine areas. Several interpolation methods were used to map the micro-topography changes and anisotropic ordinary kriging (OK) emerged as the best as judged by the performance statistics of the coefficient of determination and the rootmean-square-error. Our findings demonstrated that soil erosion rates were 40.1, 39.4, 25.0, 38.9, 37.9, to 64.8 Mg ha-1 yr-1 over the 40 years since the establishment of the vineyard studied, when using 18, 15, 10, 7, 5 and 2 measuring points, respectively. We propose that ISUM can be standardised as using measuring points at 10 cm intervals.

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Section: Ordinary Krigingmentioning

confidence: 99%

Determining the best ISUM (Improved stock unearthing Method) sampling point number to model long-term soil transport and micro-topographical changes in vineyards

Rodrigo‐Comino

Keshavarzi

Zeraatpisheh

et al. 2019

Computers and Electronics in Agriculture

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“…In previous research, the geostatistical method (ordinary kriging (OK), cokriging) [14], [15], [16], geometric method (inverse distance weighting (IDW), local polynomial), and statistical methods such as the linear regression model (LR) [17], [18], [19] have been the most commonly used interpolation technologies [1], [20]. In addition, hybrid interpolation techniques, which combine two conceptually different approaches, have received increasing attention in recent years [21], [22].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Four Spatial Interpolation Methods for Estimating Soil Moisture in a Complex Terrain Catchment

et al. 2013

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Many spatial interpolation methods perform well for gentle terrains when producing spatially continuous surfaces based on ground point data. However, few interpolation methods perform satisfactorily for complex terrains. Our objective in the present study was to analyze the suitability of several popular interpolation methods for complex terrains and propose an optimal method. A data set of 153 soil water profiles (1 m) from the semiarid hilly gully Loess Plateau of China was used, generated under a wide range of land use types, vegetation types and topographic positions. Four spatial interpolation methods, including ordinary kriging, inverse distance weighting, linear regression and regression kriging were used for modeling, randomly partitioning the data set into 2/3 for model fit and 1/3 for independent testing. The performance of each method was assessed quantitatively in terms of mean-absolute-percentage-error, root-mean-square-error, and goodness-of-prediction statistic. The results showed that the prediction accuracy differed significantly between each method in complex terrain. The ordinary kriging and inverse distance weighted methods performed poorly due to the poor spatial autocorrelation of soil moisture at small catchment scale with complex terrain, where the environmental impact factors were discontinuous in space. The linear regression model was much more suitable to the complex terrain than the former two distance-based methods, but the predicted soil moisture changed too sharply near the boundary of the land use types and junction of the sunny (southern) and shady (northern) slopes, which was inconsistent with reality because soil moisture should change gradually in short distance due to its mobility in soil. The most optimal interpolation method in this study for the complex terrain was the hybrid regression kriging, which produced a detailed, reasonable prediction map with better accuracy and prediction effectiveness.

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“…Since selected soil moisture datasets have the same soil level settings as NMM-dust, there is no requirement for vertical interpolation. Horizontal interpolation of soil moisture data is conducted using Regression Kriging method [41], using DEM [42], land cover [43], and annual solar radiation as dependent variables. The interpolation of GVF data is conducted using original kriging [44].…”

Section: Methodsmentioning

confidence: 99%

Improving the Non-Hydrostatic Numerical Dust Model by Integrating Soil Moisture and Greenness Vegetation Fraction Data with Different Spatiotemporal Resolutions

Yang

2016

PLoS ONE

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Dust storms are devastating natural disasters that cost billions of dollars and many human lives every year. Using the Non-Hydrostatic Mesoscale Dust Model (NMM-dust), this research studies how different spatiotemporal resolutions of two input parameters (soil moisture and greenness vegetation fraction) impact the sensitivity and accuracy of a dust model. Experiments are conducted by simulating dust concentration during July 1–7, 2014, for the target area covering part of Arizona and California (31, 37, -118, -112), with a resolution of ~ 3 km. Using ground-based and satellite observations, this research validates the temporal evolution and spatial distribution of dust storm output from the NMM-dust, and quantifies model error using measurements of four evaluation metrics (mean bias error, root mean square error, correlation coefficient and fractional gross error). Results showed that the default configuration of NMM-dust (with a low spatiotemporal resolution of both input parameters) generates an overestimation of Aerosol Optical Depth (AOD). Although it is able to qualitatively reproduce the temporal trend of the dust event, the default configuration of NMM-dust cannot fully capture its actual spatial distribution. Adjusting the spatiotemporal resolution of soil moisture and vegetation cover datasets showed that the model is sensitive to both parameters. Increasing the spatiotemporal resolution of soil moisture effectively reduces model’s overestimation of AOD, while increasing the spatiotemporal resolution of vegetation cover changes the spatial distribution of reproduced dust storm. The adjustment of both parameters enables NMM-dust to capture the spatial distribution of dust storms, as well as reproducing more accurate dust concentration.

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Geostatistical Interpolation of Soil Properties in Boom Clay in Flanders

Cited by 13 publications

References 1 publication

Determining the best ISUM (Improved stock unearthing Method) sampling point number to model long-term soil transport and micro-topographical changes in vineyards

Determining the best ISUM (Improved stock unearthing Method) sampling point number to model long-term soil transport and micro-topographical changes in vineyards

Comparison of Four Spatial Interpolation Methods for Estimating Soil Moisture in a Complex Terrain Catchment

Improving the Non-Hydrostatic Numerical Dust Model by Integrating Soil Moisture and Greenness Vegetation Fraction Data with Different Spatiotemporal Resolutions

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