Comparison of interpolation methods for soil moisture prediction on China's Loess Plateau

Xie, Baoni; Jia, Xiaoxu; Qin, Zhanfei; Zhao, Chunlei; Shao, Mingan

doi:10.1002/vzj2.20025

Cited by 21 publications

(6 citation statements)

References 45 publications

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“…The map produced by the OK method smoothed out most of the local details. These findings were consistent with those of previous studies [82][83][84]. In the PTE estimation results produced by the EBK algorithm, we detected a more dramatic PTE variation in the study area.…”

Section: Spatial Prediction Accuracy Using Different Interpolation Mesupporting

confidence: 93%

Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods

Xia

Zhu³

et al. 2020

Remote Sensing

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Soil pollution by potentially toxic elements (PTEs) has become a core issue around the world. Knowledge of the spatial distribution of PTEs in soil is crucial for soil remediation. Portable X-ray fluorescence spectroscopy (p-XRF) provides a cost-saving alternative to the traditional laboratory analysis of soil PTEs. In this study, we collected 293 soil samples from Fuyang County in Southeast China. Subsequently, we used several geostatistical methods, such as inverse distance weighting (IDW), ordinary kriging (OK), and empirical Bayesian kriging (EBK), to estimate the spatial variability of soil PTEs measured by the laboratory and p-XRF methods. The final maps of soil PTEs were outputted by the model averaging method, which combines multiple maps previously created by IDW, OK, and EBK, using both lab and p-XRF data. The study results revealed that the mean PTE content measured by the laboratory methods was as follows: Zn (127.43 mg kg−1) > Cu (31.34 mg kg−1) > Ni (20.79 mg kg−1) > As (10.65 mg kg−1) > Cd (0.33 mg kg−1). p-XRF measurements showed a spatial prediction accuracy of soil PTEs similar to that of laboratory analysis measurements. The spatial prediction accuracy of different PTEs outputted by the model averaging method was as follows: Zn (R2 = 0.71) > Cd (R2 = 0.68) > Ni (R2 = 0.67) > Cu (R2 = 0.62) > As (R2 = 0.50). The prediction accuracy of the model averaging method for five PTEs studied herein was improved compared with that of the laboratory and p-XRF methods, which utilized individual geostatistical methods (e.g., IDW, OK, EBK). Our results proved that p-XRF was a reliable alternative to the traditional laboratory analysis methods for mapping soil PTEs. The model averaging approach improved the prediction accuracy of the soil PTE spatial distribution and reduced the time and cost of monitoring and mapping PTE soil contamination.

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Section: Spatial Prediction Accuracy Using Different Interpolation Mesupporting

confidence: 93%

Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods

Xia

Zhu³

et al. 2020

Remote Sensing

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“…Our research was conducted in a small area with homogeneous natural conditions; therefore, the density of samples and their distances are similar to previous studies. As was found in previous research (Xie et al, 2020), the IDW approach works well under uniform sample distribution due to the local variance, which is a driver of the estimated surface. RF accuracy for each interpolation is different, (even if the initial input dataset is the same) due to the “random nature” of the method.…”

Section: Discussionsupporting

confidence: 76%

Application GIS and remote sensing for soil organic carbon mapping in a farm-scale in the hilly area of central Vietnam

Chương

Pham

Nguyen

et al. 2022

Air, Soil and Water Research

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Soil Organic Carbon (SOC) influences many soil properties including nutrient and water holding capacity, nutrient cycling and stability, improved water infiltration and aeration. It also is an essential parameter in the assessment of soil quality, especially for agricultural production. However, SOC mapping is a complicated process that is costly and time-consuming due to the physical challenges of the natural conditions that is being surveyed. The best model for SOC mapping is still in debate among many researchers. Recently, the development of machine learning and Geographical Information Systems (GIS) has provided the potential for more accurate spatial prediction of SOC content. This research was conducted in a relatively small-scale capacity in the Central Vietnam region. The aim of this study is to compare the accuracy of Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Random Forest (RF) methods for SOC interpolation, with a dataset of 47 soil samples for an area of 145 hectares. Three environmental variables including elevation, slope, and the Normalized Difference Vegetation Index (NDVI) were used for the RF model. In the RF model, the values of the number of variables randomly sampled as candidates at each split, (mtry), and the number of bootstrap replicates, (ntree), were determined in terms of 1 and 1,000 respectively The results at our research site showed that using IDW is the most accurate method for SOC mapping, followed by the methods of RF and OK respectively. Concerning SOC mapping based-on auxiliary variables, in areas where there is human activity, the selection of auxiliary variables should be carefully considered because the variation in the SOC may not only be due to environmental variables but also by farming technologies.

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“…It is thus important to carefully consider these limitations and assumptions of cokriging before using it. Interpolation methods are increasingly being used as a tool to improve the estimation of SWC spatial distribution due to limited access to accurate observation data (Xie et al 2020). Since the MC-EMI VCP C1 gave the most accurate prediction from MLR, this coil orientation was used as a covariate for the interpolation of SWC using cokriging.…”

Section: Variography and Kriging Interpolated Surface Of Soil Water C...mentioning

confidence: 99%

Estimation of soil water content using electromagnetic induction sensors under different land uses

Mensah

Katanda

Krishnapillai

et al. 2023

Environ. Res. Commun.

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The complex nature of podzolic soils makes investigating their subsurface challenging. Near-surface geophysical techniques, like electromagnetic induction (EMI), offer significant assistance in studying podzolic soils. Multi-coil (MC-EMI) and multi-frequency (MF-EMI) sensors were selected to maximize soil water content (SWC) prediction in this study. The objectives were to (i) compare apparent electrical conductivity (ECa) measurements from the MC and MF-EMI sensors under different land use conditions, (ii) investigate the spatial variation of ECa, SWC, texture, soil organic matter (SOM), and bulk density (BD) under different land use conditions, and (iii) use statistical and geostatistical analysis to evaluate the effectiveness of ECa measurements in characterizing SWC under different land use conditions, considering the texture, SOM, and BD contents. The study found that MC-EMI had statistically significant relations (p-value  0.05) with SWC relative to the MF-EMI. Multiple linear regression (MLR) models were also shown to be more effective in representing SWC variations (higher coefficient of determination and lower root mean square error) than simple linear regression models. MC-EMI sensor provided better SWC predictions compared to the MF-EMI sensor, possibly due to larger sampling depths differences between time domain reflectometry measured SWC (SWCTDR) and MF-EMI sensor than those between SWCTDR and MC-EMI sensor. Lastly, cokriging of measured SWC was found to offer more accurate maps than cokriging of predicted SWC obtained from MLR across different land use conditions. The study has shown that EMI may not be highly effective for shallow depths, and ECa can be affected by various soil properties, making it difficult to extrapolate other parameters. However, EMI still shows promise as a reliable method for predicting SWC in boreal podzolic soils. Research into EMI's usefulness for this purpose has yielded promising results, as indicated in this study. Further investigation is needed to fully harness the potential of this promising technique.

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Comparison of interpolation methods for soil moisture prediction on China's Loess Plateau

Cited by 21 publications

References 45 publications

Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods

Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods

Application GIS and remote sensing for soil organic carbon mapping in a farm-scale in the hilly area of central Vietnam

Estimation of soil water content using electromagnetic induction sensors under different land uses

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