2021
DOI: 10.1007/s12145-021-00609-2
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How the variations of terrain factors affect the optimal interpolation methods for multiple types of climatic elements?

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Cited by 14 publications
(4 citation statements)
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References 19 publications
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“…Indeed, the use of the spline method for water quality spatial analysis has been verified by numerous scientists. Guo et al [ 62 ] investigated the five typical spatial interpolation methods, namely kriging, natural neighborhood, IDW, spline and trend surface, and found that the IDW and spline methods are appropriate for platform and small undulating areas. The national neighbor method and spline method provided the most accurate estimates of nitrates in the aquifer in the Qazvin plain [ 63 ].…”
Section: Resultsmentioning
confidence: 99%
“…Indeed, the use of the spline method for water quality spatial analysis has been verified by numerous scientists. Guo et al [ 62 ] investigated the five typical spatial interpolation methods, namely kriging, natural neighborhood, IDW, spline and trend surface, and found that the IDW and spline methods are appropriate for platform and small undulating areas. The national neighbor method and spline method provided the most accurate estimates of nitrates in the aquifer in the Qazvin plain [ 63 ].…”
Section: Resultsmentioning
confidence: 99%
“…LST changes are significantly influenced by spatial factors, particularly altitude and topography 50 , 51 . Ignoring these spatial attributes can lead to avoidable losses in measurement precision 52 .…”
Section: Discussionmentioning
confidence: 99%
“…Cloud-Covered Filling LST changes are significantly influenced by spatial factors, particularly altitude and topography. 50,51 Ignoring these spatial attributes can lead to avoidable losses in measurement precision. 52 To mitigate this, an efficient data-filling method is introduced, which combines both temporal and spatial correlations.…”
Section: Real Cloud Simulation Validationmentioning
confidence: 99%
“…The overall cost time from CRHSs to commercial facilities was obtained by using the OD cost matrix analysis in ArcGIS network analysis, and then the accessibility distribution from the overall CRHSs to commercial facilities was generated by using the ArcGIS inverse distance weighting interpolation method. The inverse distance weight [60,61] uses a linear combination of a set of sampling points to determine pixel values. The formulae are as follows:…”
Section: Network Analysismentioning
confidence: 99%