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

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  • Xueling Yao
  • Bojie Fu
  • Yihe Lü
  • Feixiang Sun
  • Shuai Wang
  • Min Liu

Abstract

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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  • Xueling Yao & Bojie Fu & Yihe Lü & Feixiang Sun & Shuai Wang & Min Liu, 2013. "Comparison of Four Spatial Interpolation Methods for Estimating Soil Moisture in a Complex Terrain Catchment," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-13, January.
    • Handle: RePEc:plo:pone00:0054660
    DOI: 10.1371/journal.pone.0054660
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    1. Choudhury, Malini Roy & Mellor, Vincent & Das, Sumanta & Christopher, Jack & Apan, Armando & Menzies, Neal W. & Chapman, Scott & Dang, Yash P., 2021. "Improving estimation of in-season crop water use and health of wheat genotypes on sodic soils using spatial interpolation techniques and multi-component metrics," Agricultural Water Management, Elsevier, vol. 255(C).
    2. Prashant K. Srivastava & Prem C. Pandey & George P. Petropoulos & Nektarios N. Kourgialas & Varsha Pandey & Ujjwal Singh, 2019. "GIS and Remote Sensing Aided Information for Soil Moisture Estimation: A Comparative Study of Interpolation Techniques," Resources, MDPI, vol. 8(2), pages 1-17, April.
    3. Jesús Barrena-González & Joaquín Francisco Lavado Contador & Manuel Pulido Fernández, 2022. "Mapping Soil Properties at a Regional Scale: Assessing Deterministic vs. Geostatistical Interpolation Methods at Different Soil Depths," Sustainability, MDPI, vol. 14(16), pages 1-20, August.
    4. Yanto & Arwan Apriyono & Purwanto Bekti Santoso & Sumiyanto, 2022. "Landslide susceptible areas identification using IDW and Ordinary Kriging interpolation techniques from hard soil depth at middle western Central Java, Indonesia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(2), pages 1405-1416, January.
    5. Mohamed A. E. AbdelRahman & Yasser M. Zakarya & Mohamed M. Metwaly & Georgios Koubouris, 2020. "Deciphering Soil Spatial Variability through Geostatistics and Interpolation Techniques," Sustainability, MDPI, vol. 13(1), pages 1-13, December.
    6. Beata Ferencz & Jarosław Dawidek, 2021. "Assessment of Spatial and Vertical Variability of Water Quality: Case Study of a Polymictic Polish Lake," IJERPH, MDPI, vol. 18(16), pages 1-13, August.
    7. Yasser M. Zakarya & Mohamed M. Metwaly & Mohamed A. E. AbdelRahman & Mohamed R. Metwalli & Georgios Koubouris, 2021. "Optimized Land Use through Integrated Land Suitability and GIS Approach in West El-Minia Governorate, Upper Egypt," Sustainability, MDPI, vol. 13(21), pages 1-21, November.
    8. Darren Norris & Marie-Josée Fortin & William E Magnusson, 2014. "Towards Monitoring Biodiversity in Amazonian Forests: How Regular Samples Capture Meso-Scale Altitudinal Variation in 25 km2 Plots," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-9, August.
    9. Mulenga Kalumba & Edwin Nyirenda & Imasiku Nyambe & Stefaan Dondeyne & Jos Van Orshoven, 2022. "Machine Learning Techniques for Estimating Hydraulic Properties of the Topsoil across the Zambezi River Basin," Land, MDPI, vol. 11(4), pages 1-22, April.

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