Performance Of Spatial Interpolation Methods Research Articles

Performance of spatial interpolation methods in predicting GNSS zenith total delay

This study proposes an alternative approach to the grid-wise VMF1 over Europe, through spatial interpolation of the site-wise VMF1 product. The performance of Ordinary Kriging and IDW was evaluated for predicting ZTD. Using 2020 data collected at 24 GNSS stations, two scenarios were constructed for February and August. Ordinary Kriging and IDW predictions were performed by making model adjustments via performance metrics and method successes were compared. The results showed that the ordinary kriging achieved a success rate of 83.2 %, while the IDW method achieved 77.1 % success. We compared the results with the grid-wise VMF1 to control the accuracy of spatial interpolations. Finally, the methods proposed as alternatives to gridded VMF1 were verified through the CSRS-PPP(ZTD). Ordinary-Kriging (RMSEFeb: [0.92–2.95 cm]; RMSEAug: [1.76–3.85 cm]) was in better compliance with the CSRS-PPP(ZTD), compared to the grid-wise VMF1 (RMSEFeb: [1.59–3.01 cm]; RMSEAug: [2.32–3.93 cm]). These results support that the quality of gridded products can be improved by spatial interpolations.

Quantitative Analysis of the Performance of Spatial Interpolation Methods for Rainfall Estimation Using Commercial Microwave Links

AbstractUsing signal level measurements from commercial microwave links (CMLs) has proven to be a valuable tool for near-ground 2D rain mapping. Such mapping is commonly based on spatial interpolation methods, where each CML is considered as a point measurement instrument located at its center. The validity of the resulted maps is tested against radar observations. However, since radar has limitations, accuracy of CML-based reconstructed rain maps remains unclear. Here we provide a quantitative comparison of the performance of CML-based spatial interpolation methods for rain mapping by conducting a systematic analysis: first by quantifying the performance of maps generated from semisynthetic CML data, and thereafter turning to real-data analysis of the same rain events. A radar product of the German Weather Service serves as ground truth for generating semisynthetic data, in which several temporal aggregations of the radar rainfall fields are used to create different decorrelation distances. The study was done over an area of 225 × 245 km2 in southern Germany, with 808 CMLs. We compare the performance of two spatial interpolation methods—inverse distance weighting and ordinary kriging—in two cases: where each CML is represented as a single point, and where three points are used. The points’ measurements values in the latter are determined using an iterative algorithm. The analysis of both cases is based on a 48-h rain event. The results reconfirm the validity of CML-based rain retrieval, showing a slight systematic performance improvement when an iterative algorithm is applied so each CML is represented by more than a single point, independent of the interpolation method.

Spatial prediction of soil properties in a watershed scale through maximum likelihood approach

Surface map of soil properties plays an important role in various applications in a watershed. Ordinary kriging (OK) and regression kriging (RK) are conventionally used to prepare these surface maps but generally need large number of regularly girded soil samples. In this context, REML-EBLUP (REsidual Maximum Likelihood estimation of semivariogram parameters followed by Empirical Best Linear Unbiased Prediction) shown capable but not fully tested in a watershed scale. In this study, REML-EBLUP approach was applied to prepare surface maps of several soil properties in a hilly watershed of Eastern India and the performance was compared with conventionally used spatial interpolation methods: OK and RK. Evaluation of these three spatial interpolation methods through root-mean-squared residuals (RMSR) and mean squared deviation ratio (MSDR) showed better performance of REML-EBLUP over the other methods. Reduction in sample size through random selection of sampling points from full dataset also resulted in better performance of REML-EBLUP over OK and RK approach. The detailed investigation on effect of sample number on performance of spatial interpolation methods concluded that a minimum sampling density of 4/km2 may successfully be adopted for spatial prediction of soil properties in a watershed scale using the REML-EBLUP approach.

The performance of spatial interpolation methods and choropleth maps to estimate properties at points: A soil survey case study

The performance of spatial interpolation methods and choropleth maps to estimate properties at points: A soil survey case study

The performance of spatial interpolation methods and choropleth maps to estimate properties at points: A soil survey case study

A study was designed to compare the performance of six spatial interpolation methods to estimate soil properties at unvisited points. These methods were global mean, moving average, nearest neighbour, inverse squared distance, Laplacian smoothing splines and ordinary point kriging. These methods were also applied in combination with a choropleth map (soil map) by stratifying the area. The soil properties estimated were thickness of A1 horizon, maximum areic mass of phosphate adsorbed by soil, mean highest water table and mean lowest water table. The performance of the methods was measured by estimating the spatial means of the squared and absolute error (quality criteria not conditional on the sample of test points) by a stratified simple random sample of test points. The mean squared error was very large in proportion to the spatial variation over the total area for all methods and properties. Differences between methods were small. In general, no statistically significant stratification or weighting effects were found. The effect of weighting plus stratification was usually not significant either. Overall, weighting with inverse squared distance was as satisfactory as weighting by ordinary point kriging. However, the latter was superior near data points. Also, when combined with soil map stratification, kriging was more reliable in the sense that it estimated all properties well. Estimates obtained using the means of six soil map units were better, although not significantly, than those obtained from unstratified kriging and as good as kriging within three map units.

Theoretical and Experimental Performance of Spatial Interpolation Methods for Soil Salinity Analysis

Interpolation methods are required for analysis of soil salinity data by geographic information systems. This study was conducted to determine interpolation methods that are best suited to map soil salinity. Methods of closest neighbor, kriging, inverse-distance moving average, and thin plate smoothing splines were compared by cross-validation for precision and smoothing, using 341 measured values of electrical conductivity of saturated paste extract in a 16 000 ha area in southwest Iran. Interpolation precision of all methods were low, with a mean absolute difference between measured and predicted values ranging from 42 to 76% of the mean measured soil salinity. This was due to the large ratio of nugget effect to the sill of the variogram and to the high variability of data. Thin plate smoothing splines and ordinary kriging were the most precise methods, whereas closest neighbor was the least precise. The smoothing of the methods was assessed by comparing the dispersion standard deviation of interpolated values with that of observed values. The most precise methods were also those that performed an important smoothing. Ordinary kriging and thin plate smoothing splines produced contour maps that were much easier to interpret. A theoretical analysis of the performance of the methods (precision and smoothing) led to conclusions similar to those based on the cross-validation study. Such a theoretical analysis can be used to select an appropriate interpolation method without the need for time consuming cross-validation.