A comparison between mixed support kriging and block cokriging for modelling and combining spatial data with different support
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The paper proposes a geostatistical framework to solve the issues of heterogeneous support for spatial estimation. Apparent soil electrical conductivity (ECa) was measured in a field cropped with San Marzano tomato using a multiple frequency electromagnetic profiler with six operating frequencies. Mixed support kriging (MSK) was used to estimate ECa taking into account the change of support. The method includes punctual kriging with the error being the dispersion variance associated with each frequency. The mixed support kriging approach was compared with traditional block cokriging (BCOK) through cross validation. Block cokriging compared to MSK was more computationally intensive to fit the multivariate model of spatial dependence, and in estimating ECa, mixed support kriging outperformed at some frequencies whereas BCOK was more accurate at others. The two approaches were also compared in terms of field-delineation which differed in spatial continuity.
KeywordsPrecision agriculture GEM300 Data fusion Change of support Field delineation
Financial support for this work comes from the Project “M2Q” PON03PE_00180_1 co-funded by the National Operational Program for Research and Competitiveness (PON R&C) 2007–2013, through the European Regional Development Fund (ERDF) and national resource (Revolving Fund—Cohesion Action Plan MIUR). D. M. MIUR n. 738/05.03.2014. The authors thank the reviewers of this paper and Dr. John Stafford for providing constructive comments, which have contributed to the improvement of the published version.
- Adamchuk, V. I. (2011). Sensor fusion for precision agriculture. In R. A. V. Rossel (Ed.), (Ch. 2). Rijeka, Croatia: IntechOpen. https://doi.org/10.5772/19983.
- Bush, E. D. (2010). An overview of the estimation of kimberlite diamond deposits. Southern African Institute of Mining and Metallurgy: Diamonds—source to use 2010 (pp. 73–84). Johannesburg, S Africa: The Southern African Institute of Mining and Metallurgy.Google Scholar
- Castrignanò, A., Buttafuoco, G., Quarto, R., Parisi, D., Viscarra Rossel, R. A., Terribile, F., et al. (2018). A geostatistical sensor data fusion approach for delineating homogeneous management zones in precision agriculture. Catena, 167, 293–304. https://doi.org/10.1016/j.catena.2018.05.011.CrossRefGoogle Scholar
- Castrignanò, A., Buttafuoco, G., Quarto, R., Vitti, C., Langella, G., Terribile, F., et al. (2017). A combined approach of sensor data fusion and multivariate geostatistics for delineation of homogeneous zones in an agricultural field. Sensors, 17(12), 2794. https://doi.org/10.3390/s17122794.CrossRefGoogle Scholar
- Castrignanò, A., Giugliarini, L., Risaliti, R., & Martinelli, N. (2000). Study of spatial relationships among some soil physico-chemical properties of a field in central Italy using multivariate geostatistics. Geoderma, 97(1–2), 39–60. https://doi.org/10.1016/S0016-7061(00)00025-2.CrossRefGoogle Scholar
- Daniels, J. J., Vendl, M., Ehsani, M. R., & Allred, B. (2008). Electromagnetic induction methods. In B. Allred, J. J. Daniels, M. R. Ehsani, J. J. Daniels, & M. R. Ehsani (Eds.), Handbook of agricultural geophysics (pp. 109–128). Boca Raton, FL, USA: CRC Press. https://doi.org/10.1201/9781420019353.Google Scholar
- Deutsch, C. V. (2007). A review of geostatistical approaches to data fusion. In Geophysical monograph series (Vol. 171, pp. 7–18). American Geophysical Union (AGU). https://doi.org/10.1029/171gm03.
- Hall, D. L., & McMullen, S. A. H. (2004). Mathematical techniques in multisensor data fusion. Library (2nd ed., Vol. 2). Norwood, MA, USA: Artech House, Inc. https://doi.org/10.1186/1475-925x-4-23.
- Jackson, J. E. (2003). User’s guide to principal components. New York, USA: Wiley.Google Scholar
- Journel, A. G., & Huijbregts, C. J. (1978). Mining geostatistics. London, UK: Academic Press.Google Scholar
- Lajaunie, C. (1996). Documentation of the mixed support kriging programs. Fontainebleau, France: Ecole Nationale Superieure des Mines de Paris.Google Scholar
- Matheron, G. (1971). The theory of regionalized variables and its applications Les Cahiers du Centre de Morphlogie Mathematique (Vol. 5). Fontainebleau, France: Ecole Nationale Superieure des Mines de Paris.Google Scholar
- Matheron, G. (1982). Pour une analyse krigeante des données regionalisées (For a factorial kriging analysis of regionalized data). Techinal Report n. 732. Centre de Geostatistique. Fontainebleau, France: Ecole Nationale Superieure des Mines de Paris.Google Scholar
- McBratney, A. B., Minasny, B., & Whelan, B. (2011). Defining proximal soil sensing. In V. I. Adamchuk & R. A. ViscarraRossel (Eds.), The second global workshop on proximal soil sensing (pp. 144–146). Montreal, Canada: McGill University.Google Scholar
- Mulla, D. J., & Schepers, J. S. (1997). Key processes and properties for site specific soil and crop management. In F. J. Pierce & E. J. Sadler (Eds.), The state of site specific management for agriculture (pp. 1–18). Madison, WI, USA: American Society of Agronomy.Google Scholar
- Rivoirard, J. (1994). Introduction to disjunctive kriging and non-linear geostatistics. Oxford, UK: Clarendon Press.Google Scholar
- Soil Survey Staff (2014). Keys to Soil Taxonomy, 2010. In Change (Vol. 11 ed.). Washington, DC, USA. https://doi.org/10.1109/tip.2005.854494.
- Stenberg, B., Viscarra Rossel, R. A., Mouazen, A. M., & Wetterlind, J. (2010). Visible and near infrared spectroscopy in soil science. In Advances in agronomy (Vol. 107, pp. 163–215). Burlington, USA: Academic Press. https://doi.org/10.1016/s0065-2113(10)07005-7.
- Webster, R. (1991). Local disjunctive kriging of soil properties with change of support. Journal of Soil Science, 42(2), 301–318. https://doi.org/10.1111/j.1365-2389.1991.tb00411.x.CrossRefGoogle Scholar