127
Views
5
CrossRef citations to date
0
Altmetric
Original Articles

Space-Time Modeling of the Electrical Conductivity of Soil in a Geothermal Zone

, , , &
Pages 1107-1118 | Received 27 Jul 2017, Accepted 01 Mar 2018, Published online: 29 Mar 2018

References

  • Allbed, A., L. Kumar, and P. Sinha. 2014. Mapping and modelling spatial variation in soil salinity in the Al Hassa Oasis based on remote sensing indicators and regression techniques. Remote Sensing 6 (2):1137–57. doi:10.3390/rs6021137.
  • Bahadur, B. 2006. Mixed-effets modeling of shortleaf pine (Pinus echinata mil). Growht data. Thesis for Doctor of Philosophy. Oklahoma State University, Oklahoma, USA.
  • Brouder, S., B. Hofmann, and H. Reetz Jr. 2001. Evaluating spatial variability of soil parameters for input management. Better Crops 85:8–11. https://ipni.net/ppiweb/bcrops.nsf/webindex/D783345EA24222F585256A630072F3C7/file/00-2p08.pdf.
  • Burgess, T., and R. Webster. 1980. Optimal interpolation and isarithmic mapping of soil properties. I. The semi-variogram and punctual kriging. European Journal of Soil Science 31:315–31. doi:10.1111/j.1365-2389.1980.tb02085.x.
  • Chilés, J., and P. Delfiner. 1999. Geostatistics: Modeling spatial uncertainty. Nueva York, USA: John Wiley y Sons.
  • Corwin, L., M. Lesh, D. Oster, and R. Kaffka. 2006. Monitoring management-induced spatio-temporal changes in soil quality through soil sampling directed by apparent electrical conductivity. Geoderma 131:369–87. doi:10.1016/j.geoderma.2005.03.014.
  • Cressie, N. 1989. Geostatistics. The American Statistical 43:197–202. doi:10.1080/00031305.1989.10475658.
  • Cruz-Cárdenas, G., L. López-Mata, C. A. Ortiz-Solorio, J. L. Villaseñor, E. Ortiz, J. T. Silva, and F. Estrada-Godoy. 2014. Interpolation of Mexican soil properties at a scale of 1: 1,000,000. Geoderma 213:29–35. doi:10.1016/j.geoderma.2013.07.014.
  • Cruz-Cárdenas, G., J. T. Silva, S. Ochoa-Estrada, F. Estrada-Godoy, and J. Nava-Velázquez. 2017. Delineation of Environmental Units by Multivariate Techniques in the Duero River Watershed, Michoacán, Mexico. Environmental Modeling & Assessment 22 (3):257–66. doi:10.1007/s10666-016-9534-2.
  • Elmetwalli, A., A. Tyler, P. Hunter, and C. Salt. 2012. Detecting and distinguishing moisture- and salinity-induced stress in wheat and maize through in situ spectroradiometry measurements. Remote Sensing Letters 3:363–72. doi:10.1080/01431161.2011.599346.
  • Fang, Z., and R. Bailey. 2001. Nonlinear mixed effects modeling for slash pine dominant height growth following intensive silvicultural treatments. Forest Science 47:287–300.
  • Friedman, S. 2005. Soil properties influencing apparent electrical conductivity: A review. Computers and Electronics in Agriculture 46:45–70. doi:10.1016/j.compag.2004.11.001.
  • Gaston, L. A., M. A. Locke, R. M. Zablotowicz, and K. N. Reddy. 2001. Spatial variability of soil properties and weed populations in the Mississippi Delta. Soil Science Society of America Journal 65:449–59. doi:10.2136/sssaj2001.652449x.
  • Grunwald, S. 2009. Multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma 152:195–207. doi:10.1016/j.geoderma.2009.06.003.
  • Heil, K., and U. Schmidhalter. 2012. Characterisation of soil texture variability using the apparent soil electrical conductivity at a highly variable site. Computers and Geosciences 39:98–110. doi:10.1016/j.cageo.2011.06.017.
  • Hengl, T., G. B. Heuvelink, and D. G. Rossiter. 2007. About regression-kriging: From equations to case studies. Computers & Geosciences 33 (10):1301–15. doi:10.1016/j.cageo.2007.05.001.
  • Hengl, T., G. B. Heuvelink, and A. Stein. 2004. A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma 120 (1–2):75–93. doi:10.1016/j.geoderma.2003.08.018.
  • Hengl, T., G. B. M. Heuvelink, B. Kempen, J. G. B. Leenaars, M. G. Walsh, K. D. Shepherd, A. Sila, R. A. MacMillan, J. Mendes De Jesus, L. Tamene, and J. E. Tondoh. 2015. Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions. Plos One 10:e0125814. doi:10.1371/journal.pone.0125814.
  • Hengl, T., A. Sierdsema, A. Radovi, and A. Dilo. 2009. Spatial prediction of species’ distributions from occurrence-only records: Combining point pattern analysis, ENFA and regression-Kriging. Ecological Modelling 220:3499–511. doi:10.1016/j.ecolmodel.2009.06.038.
  • Hernández, A., A. Lugo, A. M. Garcés, and E. Arends. 2003. Variabilidad espacial edáfica en el sistema tradicional de conucos en el Amazonas de Venezuela. Investigación Agraria Sistemas De Recursos Forestales 12:43–54.
  • INEGI. 2015. Conjunto de Datos Vectorial Edafológico, Escala 1:250 000 Serie II (Continuo Nacional). Accessed January 15, 2015. http://www.inegi.org.mx/geo/contenidos/recnat/edafologia/vectorial_serieii.aspx
  • Kravchenko, A. N., and D. G. Bullock. 1999. A comparative study of interpolation methods for mapping soil properties. Agronomy Journal 91:393–400. doi:10.2134/agronj1999.00021962009100030007x.
  • Kumar, L., K. Schmidt, S. Dury, and A. Skidmore. 2001. Imaging spectrometry and vegetation science. Imaging Spectrometry. In Basic principles and prospective applications, eds. V. D. M. Freek and D. Jong, 111–155. Netherlands: Springer.
  • McBratney, A. B., M. D. L. Mendonça Santos, and B. Minasny. 2003. On digital soil mapping. Geoderma 117:3–52. doi:10.1016/S0016-7061(03)00223-4.
  • Metternicht, G., and J. Zinck. 2008. Remote sensing of soil salinization: Impact on land management. New York, USA: CRC Press, Taylor and Francis.
  • Mulla, D., and A. McBratney. 2002. Soil spatial variability. In Soil Physics Companion, ed. Warrick, 343–60. New York, USA: CRC Press, Taylor and Francis..
  • NOM-021-RECNAT-2000. Februay, 2014. 2002: Norma Oficial Mexicana Que establece las especificaciones de fertilidad, salinidad y clasificación de suelos. http://dof.gob.mx/nota_detalle.php?codigo=717582&fecha=31/12/2002.
  • Omuto, C., and R. Vargas. 2015. Re-tooling of regression kriging in R for improved digital mapping of soil properties. Geosciences Journal 19:157–65. doi:10.1007/s12303-014-0023-9.
  • Paz, A., M. Gómez, and M. T. Taboada. 1996. Análisis geoestadístico de las propiedades generales de un suelo de cultivo. Investigación Agraria: Producción De Protección De Cultivos 11:133–60.
  • Pérez, M., and M. García. 2013. Aplicaciones de la teledetección en degradación de suelos. Boletín De La Asociación De Geografos Españoles 61:285–308.
  • Rosas, E. R., F. J. Urrutía, and R. Maciel. 1989. Geología del extremo oriental del graben de Chapala, breve discusión. Revista Mexicana De Geociencias 5:3–18.
  • Schmid, T., M. Koch, and J. Gumuzzio. 2005. Multisensor approach to determine changes of wetland characteristics in semiarid environments (central Spain). IEEE Transactions on Geoscience and Remote Sensing 43:2516–25. doi:10.1109/TGRS.2005.852082.
  • Scudiero, E., T. H. Skaggs, and D. L. Corwin. 2015. Regional-scale soil salinity assessment using Landsat ETM+ canopy reflectance. Remote Sensing of Environment 169:335–43. doi:10.1016/j.rse.2015.08.026.
  • SERMANAT. 2010. Salinidad del suelo. Accessed Februay, 2014. http://www.cofupro.org.mx/cofupro/images/contenidoweb/indice/publicaciones-nayarit/FOLLETOS%20Y%20MANUALES/FOLLETOS%20IMTA%202009/folleto%206%20salinidaddelsuelo.pdf.
  • Shahid, S., M. Abdelfattah, and F. Taha. 2013. Developments in soil salinity assessment and reclamation. London, New York: Springer.
  • Silva, J. T., S. Estrada, D. Cristóbal-Acevedo, and F. Estrada-Godoy. 2006. Calidad química del agua subterránea de la ciénega de Chapala como factor de degradación del suelo. Terra Latinoamericana 24:503–13.
  • Solie, J., W. Raun, and M. Stone. 1999. Submeter spatial variability of selected soil and bermudagrass production variables. Soil Science Society of America Journal 63:1724–33. doi:10.2136/sssaj1999.6361724x.
  • Sun, W., B. Minasny, and A. B. McBratney. 2012. Analysis and prediction of soil properties using local regression-kriging. Geoderma 171:16–23. doi:10.1016/j.geoderma.2011.02.010.
  • Taghizadeh-Mehrjardi, R., B. Minasny, F. Sarmadian, and B. P. Malone. 2014. Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma 213:15–28. doi:10.1016/j.geoderma.2013.07.020.
  • Tanji, K. 2002. Salinity in the soil environment. In Salinity: Environment-plant-molecules, eds. Lauchi, and Luttge, 21–51. New York, USA: Springer.
  • Thompson, J. A., S. Roecker, S. Grunwald, and P. R. Owens. 2012. Digital soil mapping: Interactions with and applications for hydropedology. In Hydropedology synergistic integration of soil science and hydrology, ed. H. Lin, 665–709. London: Academic Press.
  • Van Reeuwijk, L. P. 2002. Procedures for soil analysis, 6th ed. Wageningen, The Netherlands: Tech. Pap., vol. 9 ISRIC.
  • Villafañe, R., and I. Pla. 1994. Efectos del riego y la lluvia sobre el desplazamiento vertical de sales en un suelo arcilloso de Venezuela. Agronomía Tropical 44:707–29.
  • Webster, R., and M. Oliver. 2001. Geostatistics for environmental science. Toronto, Canadá: John Wiley and Sons.
  • Zhang, T., S. Zeng, Y. Gao, Z. Ouyang, B. Li, C. Fang, and B. Zhao. 2011. Using hyperspectral vegetation indices as a proxy to monitor soil salinity. Ecological Indicators 11:1552–62. doi:10.1016/j.ecolind.2011.03.025.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.