Comparison of spatial interpolation methods for soil moisture and its application for monitoring drought

  • Hui Chen
  • Li Fan
  • Wei Wu
  • Hong-Bin LiuEmail author


Soil moisture data can reflect valuable information on soil properties, terrain features, and drought condition. The current study compared and assessed the performance of different interpolation methods for estimating soil moisture in an area with complex topography in southwest China. The approaches were inverse distance weighting, multifarious forms of kriging, regularized spline with tension, and thin plate spline. The 5-day soil moisture observed at 167 stations and daily temperature recorded at 33 stations during the period of 2010–2014 were used in the current work. Model performance was tested with accuracy indicators of determination coefficient (R 2), mean absolute percentage error (MAPE), root mean square error (RMSE), relative root mean square error (RRMSE), and modeling efficiency (ME). The results indicated that inverse distance weighting had the best performance with R 2, MAPE, RMSE, RRMSE, and ME of 0.32, 14.37, 13.02%, 0.16, and 0.30, respectively. Based on the best method, a spatial database of soil moisture was developed and used to investigate drought condition over the study area. The results showed that the distribution of drought was characterized by evidently regional difference. Besides, drought mainly occurred in August and September in the 5 years and was prone to happening in the western and central parts rather than in the northeastern and southeastern areas.


Geographic information systems Climate change Spatial interpolation Complex topography Drought 



The authors thank the National Climate Center, China Meteorological Administration (CMA) for providing the meteorological data.


  1. Aguilar, F. J., Aguera, F., Aguilar, M. A., & Carvajal, F. (2005). Effects of terrain morphology, sampling density, and interpolation methods on grid DEM accuracy. Photogrammetric Engineering and Remote Sensing, 71, 805–816.CrossRefGoogle Scholar
  2. Alvarez, O., Guo, Q., Klinger, R. C., Li, W., & Doherty, P. (2014). Comparison of elevation and remote sensing derived products as auxiliary data for climate surface interpolation. International Journal of Climatology, 34(7), 2258–2268.CrossRefGoogle Scholar
  3. Anderson, W. B., Zaitchik, B. F., Hain, C. R., & Anderson, M. C. (2012). Towards an integrated soil moisture drought monitor for East Africa. Hydrology and Earth System Sciences, 16(8), 2893–2913.CrossRefGoogle Scholar
  4. Baskan, O., Erpul, G., & Dengiz, O. (2009). Comparing the efficiency of ordinary kriging and cokriging to estimate the Atterberg limits spatially using some soil physical properties. Clay Minerals, 44(2), 181–193.CrossRefGoogle Scholar
  5. Benavides, R., Montes, F., Rubio, A., & Osoro, K. (2007). Geostatistical modelling of air temperature in a mountainous region of Northern Spain. Agricultural and Forest Meteorology, 146(3–4), 173–188.CrossRefGoogle Scholar
  6. Bhunia, G. S., Shit, P. K., & Maiti, R. (2016). Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon (SOC). Journal of the Saudi Society of Agricultural Sciences.
  7. Boer, E., Beurs, K., & Hartkamp, A. (2001). Kriging and thin plate splines for mapping climate variables. International Journal of Applied Earth Observation and Geoinformation, 3(2), 146–154.CrossRefGoogle Scholar
  8. Breiman, L. (2001). Random forest. Machine Learning, 45, 5–32.CrossRefGoogle Scholar
  9. Brus, D. J., Gruijter, J. J., Marsman, B. A., Visschers, R., Bregt, A. K., & Breeuwsma, A. (1996). The performance of spatial interpolation methods and choropleth maps to estimate properties at points: a soil survey case study. Environmetrics, 7, 1–16.CrossRefGoogle Scholar
  10. Burrough, P. A. (1986). Principles of geographical information systems for land resources assessment. New York: Oxford University Press.Google Scholar
  11. Chaplot, V., Darboux, F., Bourennane, H., Leguédois, S., Silvera, N., & Phachomphon, K. (2006). Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density. Geomorphology, 77(1–2), 126–141.CrossRefGoogle Scholar
  12. Chen, C., & Li, Y. (2012). A robust method of thin plate spline and its application to DEM construction. Computers & Geosciences, 48(9), 9–16.CrossRefGoogle Scholar
  13. Chen, H., Wu, W., & Liu, H. B. (2017). Assessing the utility of meteorological drought indices in monitoring summer drought based on soil moisture in Chongqing, China. Theoretical and Applied Climatology.
  14. Cheng, X. F., & Xie, Y. (2009). Spatial distribution of soil organic carbon density in Anhui Province based on GIS. Scientia Geographica Sinica, 29(4), 540–544.Google Scholar
  15. Daly, C. (2006). Guidelines for assessing the suitability of spatial climate data sets. International Journal of Climatology, 26(6), 707–721.CrossRefGoogle Scholar
  16. Di, W., Zhao, Y., & Pei, Y. S. (2010). Numerical simulation and evaluation of regional climate change in Southwest China by a regional climate model. Procedia Environmental Sciences, 2(1), 1540–1554.CrossRefGoogle Scholar
  17. Ding, Y., Wang, Y., & Miao, Q. (2011). Research on the spatial interpolation methods of soil moisture based on GIS. International Conference on Information Science and Technology, 709–711.Google Scholar
  18. Dodson, R., & Marks, D. (1997). Daily air temperature interpolated at high spatial resolution over a large mountainous region. Climate Research, 8, 1–20.CrossRefGoogle Scholar
  19. Dripps, W. R., & Bradbury, K. R. (2007). A simple daily soil-water balance model for estimating the spatial and temporal distribution of groundwater recharge in temperate humid areas. Hydrogeology Journal, 15(15), 433–444.CrossRefGoogle Scholar
  20. Dubrule, O. (1984). Comparing splines and kriging. Computers & Geosciences, 10(2), 327–338.CrossRefGoogle Scholar
  21. ESRI. (2001). Using ArcGIS geostatistical analyst. Redlands: ESRI Press.Google Scholar
  22. Fang, K. K., Li, H. K., Wang, Z. K., Du, Y. F., & Wang, J. (2016). Comparative analysis on spatial variability of soil moisture under different land use types in orchard. Scientia Horticulturae, 207, 65–72.CrossRefGoogle Scholar
  23. Feng, H., & Liu, Y. (2015). Combined effects of precipitation and air temperature on soil moisture in different land covers in a humid basin. Journal of Hydrology, 531, 1129–1140.CrossRefGoogle Scholar
  24. Ford, T. W., & Quiring, S. M. (2014). Comparison and application of multiple methods for temporal interpolation of daily soil moisture. International Journal of Climatology, 34(8), 2604–2621.CrossRefGoogle Scholar
  25. Geach, M. R., Stokes, M., Telfer, M. W., Mather, A. E., Fyfe, R. M., & Lewin, S. (2014). The application of geospatial interpolation methods in the reconstruction of Quaternary landform records. Geomorphology, 216, 234–246.CrossRefGoogle Scholar
  26. Govaerts, A., & Vervoort, A. (2010). Geostatistical interpolation of soil properties in boom clay in Flanders. GeoENV VII-Geostatistics for Environmental Applications, 16, 219–230.CrossRefGoogle Scholar
  27. Grayson, R., Western, A., & Chiew, F. (1997). Preferred states in spatial soil moisture patterns: local and nonlocal controls. Water Resources Research, 12(33), 2897–2908.CrossRefGoogle Scholar
  28. Gumiere, S. J., Lafond, J. A., Hallema, D. W., Périard, Y., Caron, J., & Gallichand, J. (2014). Mapping soil hydraulic conductivity and matric potential for water management of cranberry: characterisation and spatial interpolation methods. Biosystems Engineering, 128, 29–40.CrossRefGoogle Scholar
  29. Hao, C., Zhang, J., & Yao, F. (2015). Combination of multi-sensor remote sensing data for drought monitoring over Southwest China. International Journal of Applied Earth Observation and Geoinformation, 35, 270–283.CrossRefGoogle Scholar
  30. Holsten, A., Vetter, T., Vohland, K., & Krysanova, V. (2009). Impact of climate change on soil moisture dynamics in Brandenburg with a focus on nature conservation areas. Ecological Modelling, 220(17), 2076–2087.CrossRefGoogle Scholar
  31. Isaaks, E., & Srivastava, R. (1989). An introduction to applied geostatistics. New York: Oxford University Press.Google Scholar
  32. Jia, J. Y., Han, L. Y., Liu, Y. F., He, N., Zhang, Q., Wan, X., Zhang, Y. F., & Hu, J. M. (2016). Drought risk analysis of maize under climate change based on natural disaster system theory in Southwest China. Acta Ecologica Sinica, 36, 340–349.CrossRefGoogle Scholar
  33. Keshavarz, M. R., Vazifedoust, M., & Alizadeh, A. (2014). Drought monitoring using a Soil Wetness Deficit Index (SWDI) derived from MODIS satellite data. Agricultural Water Management, 132, 37–45.CrossRefGoogle Scholar
  34. Li, F. L., & Bao, W. K. (2014). Elevational trends in leaf size of Campylotropis polyantha in the arid Minjiang River valley, SW China. Journal of Arid Environments, 108, 1–9.CrossRefGoogle Scholar
  35. Li, M., & Ma, Z. (2015). Soil moisture drought detection and multi-temporal variability across China. Science China Earth Sciences, 58(10), 1798–1813.CrossRefGoogle Scholar
  36. Lin, H., Brooks, E., McDaniel, P., & Boll, J. (2008). Hydropedology and surface/subsurface runoff processes. In Anderson M. G. (ed.), Encyclopedia of Hydrological Sciences. Chichester: John Wiley & Sons, Ltd.Google Scholar
  37. Liu, X. F., Zhang, J. Y., Liu, Z. G., Sun, J. S., Wang, J. L., & Sun, L. (2008). Comparative analysis on different spatial interpolation methods of soil moisture in winter wheat field under sprinkler irrigation. Journal of Irrigation and Drainage, 27(4), 116–118 (in Chinese with English abstract).Google Scholar
  38. Liu, X. R., Cheng, B. Y., Yang, Q., Zhang, T. Y., & Deng, B. S. (2009). Changing characteristics of high temperature and drought of Sichuan-Chongqing regions in summer and its analysis of circulation patterns in anomalous years. Plateau Meteorology, 28(2), 78–85.Google Scholar
  39. Lu, E., Cai, W. Y., Jiang, Z. H., Zhang, Q., Zhang, C. J., Higgins, R. W., & Halpert, M. S. (2013). The day-to-day monitoring of the 2011 severe drought in China. Climate Dynamics, 43(1–2), 1–9.Google Scholar
  40. Lu, G. Y., & Wong, D. W. (2008). An adaptive inverse-distance weighting spatial interpolation technique. Computers & Geosciences, 34(9), 1044–1055.CrossRefGoogle Scholar
  41. Makra, L., Mika, J., & Horváth, S. (2005). 20th century variations of the soil moisture content in East-Hungary in connection with global warming. Physics and Chemistry of the Earth, Parts A/B/C, 30(1–3), 181–186.CrossRefGoogle Scholar
  42. Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37, 17–23.CrossRefGoogle Scholar
  43. Pal, J. S., & Eltahir, E. A. B. (2001). Pathways relating soil moisture conditions to future summer rainfall within a model of the land-atmosphere system. Journal of Climate, 14(6), 1227–1242.CrossRefGoogle Scholar
  44. Phillips, D., Dolph, J., & Marks, D. (1992). A comparison of geostatistical procedures for spatial analysis of precipitation in mountainous terrain. Agricultural and Forest Meteorology, 58(1–2), 119–141.CrossRefGoogle Scholar
  45. Ramos, M. C., & Martínez-Casasnovas, J. A. (2010). Effects of precipitation patterns and temperature trends on soil water available for vineyards in a Mediterranean climate area. Agricultural Water Management, 97(10), 1495–1505.CrossRefGoogle Scholar
  46. Sheffield, J., & Wood, E. F. (2008). Global trends and variability in soil moisture and drought characteristics, 1950-2000, from observation-driven simulations of the terrestrial hydrologic cycle. Journal of Climate, 21(3), 432–458.CrossRefGoogle Scholar
  47. Stéfanon, M., Drobinski, P., D’Andrea, F., Lebeaupin-Brossier, C., & Bastin, S. (2014). Soil moisture-temperature feedbacks at meso-scale during summer heat waves over Western Europe. Climate Dynamics, 42(5–6), 1309–1324.CrossRefGoogle Scholar
  48. Sun, L., Ren, F. M., Wang, Z. Y., & Liu, Y. Y. (2012). Analysis of climate anomaly and causation in August 2011. Meteorological Monthly, 38(5), 615–622.Google Scholar
  49. Thomas, A. C., Reager, J. T., Famiglietti, J. S., & Rodell, M. (2014). A GRACE-based water storage deficit approach for hydrological drought characterization. Geophysical Research Letters, 41(5), 1537–1545.CrossRefGoogle Scholar
  50. Vieira, S. R., Nielsen, D. R., & Biggar, J. W. (1981). Spatial variability of field-measured infiltration rate. Soil Science Society of America Journal, 47, 175–184.Google Scholar
  51. Wang, A., Lettenmaier, D. P., & Sheffield, J. (2011). Soil moisture drought in China, 1950-2006. Journal of Climate, 24(13), 3257–3271.CrossRefGoogle Scholar
  52. Wang, D., Zhang, B., Zhang, T. F., Zhao, Y. F., Li, X. Y., & Yin, H. X. (2013). Temporal and spatial distributions of droughts in southwestern China in 1960-2011. Bulletin of Soil and Water Conservation, 33(6), 152–158.Google Scholar
  53. Wang, M., Shi, S., Lin, F., Hao, Z., Jiang, P., & Dai, G. (2012). Effects of soil water and nitrogen on growth and photosynthetic response of Manchurian Ash (Fraxinus mandshurica) seedlings in Northeastern China. PLoS One, 7(2), e30754.CrossRefGoogle Scholar
  54. Wang, W., Wang, W., Li, J., Wu, H., Xu, C., & Liu, T. (2010). The impact of sustained drought on vegetation ecosystem in Southwest China based on Remote Sensing. Procedia Environmental Sciences, 2, 1679–1691.CrossRefGoogle Scholar
  55. Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists. Chichester: John Wiley and Sons.CrossRefGoogle Scholar
  56. Western, A. W., Grayson, R. B., Günter, B., Willgoose, C. R., & McMahon, T. A. (1999). Observed spatial organization of soil moisture and its relation to terrain indices. Water Resources Research, 3(35), 797–810.CrossRefGoogle Scholar
  57. Wu, W., Tang, X., Ma, X., & Liu, H. (2016). A comparison of spatial interpolation methods for soil temperature over a complex topographical region. Theoretical and Applied Climatology, 125(3–4), 657–667.CrossRefGoogle Scholar
  58. Xia, Y., Fabian, P., Winterhalter, M., & Zhao, M. (2001). Forest climatology: estimation and use of daily climatological data for Bavaria, Germany. Agricultural and Forest Meteorology, 106(2), 87–103.CrossRefGoogle Scholar
  59. Xie, Y. F., Chen, T. B., Lei, M., Yang, J., Guo, Q. J., Song, B., & Zhou, X. Y. (2011). Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: accuracy and uncertainty analysis. Chemosphere, 82(3), 468–476.CrossRefGoogle Scholar
  60. Yang, Y. T., Shang, S. H., & Li, C. (2010). Correcting the smoothing effect of Ordinary Kriging estimates in soil moisture interpolation. Advances in Water Science, 21(2), 208–213 (in Chinese with English abstract).Google Scholar
  61. Yao, X., Fu, B., Lü, Y., Sun, F., Wang, S., & Liu, M. (2013a). Comparison of four spatial interpolation methods for estimating soil moisture in a complex terrain catchment. PLoS One, 8(1), e54660.CrossRefGoogle Scholar
  62. Yao, X. L., Fu, B. J., Lü, Y. H., Sun, F. X., & Guo, X. J. (2013b). The soil moisture interpolation method based on GIS and statistical models in Loess Plateau Region. Journal of Soil and Water Conservation, 27(6), 93–96 (in Chinese with English abstract).Google Scholar
  63. Zhang, M. J., He, J. Y., Wang, B. L., Wang, S. J., Li, S. S., Liu, W. L., & Ma, X. N. (2013). Extreme drought changes in Southwest China from 1960 to 2009. Journal of Geographical Sciences, 23(1), 3–16.CrossRefGoogle Scholar
  64. Zhu, G. F., Shi, P. J., Pu, T., He, Y. Q., Zhang, T., Wang, P. Z., & Pan, M. H. (2013). Changes of surface soil relative moisture content in Hengduan Mountains, China, during 1992-2010. Quaternary International, 298(7), 161–170.Google Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.College of Resources and EnvironmentSouthwest UniversityChongqingChina
  2. 2.Chongqing Institute of Meteorological ScienceChongqingChina
  3. 3.College of Computer and Information ScienceSouthwest UniversityChongqingChina

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