Assessment and visualization of spatial interpolation of soil pH values in farmland

Wu, Yi-Hwa; Hung, Ming-Chih; Patton, Jamie

doi:10.1007/s11119-013-9316-7

Cited by 21 publications

(16 citation statements)

References 22 publications

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“…Even with the same technique and same input point data, diferent parameters may result in diferent surfaces. Potentially, a given set of points and a given spatial interpolation technique can generate many diferent surfaces [10,14]. Therefore, it is important to evaluate and understand the accuracy and reliability of surface data generated from spatial interpolation.…”

Section: Spatial Interpolation Methodsmentioning

confidence: 99%

“…This ield was managed under a cornsoybean rotation [14]. Soils within the ield were mapped as mollisols.…”

Section: Study Area and Datamentioning

confidence: 99%

“…Based on a previous study [14], six interpolated surfaces were chosen for demonstration purposes. They are IDW (parameters: power 2, 10 neighbors), spline (parameters: tension, 10 neighbors), kriging (parameters: circular, 10 neighbors), IDW (parameters: power 4, 20 neighbors), spline (parameters: thin plate, 20 neighbors), and kriging (parameters: exponential, 20 neighbors).…”

Section: Quantitative Assessmentmentioning

confidence: 99%

“…Four error measures were used as accuracy index [14]. They are (1) mean absolute error (MAE), see Eq.…”

Section: Quantitative Assessmentmentioning

confidence: 99%

“…However, interpolated surfaces cannot be described by one numeric index, as many characteristics cannot be observed or evaluated by quantitative assessments. To date, there is no "rule of thumb" on which spatial interpolation techniques are most appropriate for certain situations [14].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Spatial Interpolation Techniques Using Visualization and Quantitative Assessment

Wu¹,

Hung²

2016

Applications of Spatial Statistics

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Spatial interpolation has been widely and commonly used in many studies to create surface data based on a set of sampled points, such as soil properties, temperature, and precipitation. Currently, there are many commercial Geographic Information System (GIS) or statistics software ofering spatial interpolation functions, such as inverse distance weighted (IDW), kriging, spline, and others. To date, there is no "rule of thumb" on the most appropriate spatial interpolation techniques for certain situations, though general suggestions have been published. Many studies rely on quantitative assessment to determine the performance of spatial interpolation techniques. Most quantitative assessment methods provide a numeric index for the overall performance of an interpolated surface. Although it is objective and convenient, there are many facts or trends not captured by quantitative assessments. This study used 2D visualization and 3D visualization to identify trends not evident in quantitative assessment. This study also presented a special case, a closed system in which all interpolated surfaces should sum up to 100%, to demonstrate the interaction between interpolated surfaces that were created separately and independently.

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Section: Spatial Interpolation Methodsmentioning

confidence: 99%

“…This ield was managed under a cornsoybean rotation [14]. Soils within the ield were mapped as mollisols.…”

Section: Study Area and Datamentioning

confidence: 99%

Section: Quantitative Assessmentmentioning

confidence: 99%

“…Four error measures were used as accuracy index [14]. They are (1) mean absolute error (MAE), see Eq.…”

Section: Quantitative Assessmentmentioning

confidence: 99%

Section: Introductionmentioning