Spatial Estimation of Soil Organic Matter Content Using Remote Sensing Data in Southern Tunisia

  • Emna MedhioubEmail author
  • Moncef Bouaziz
  • Samir Bouaziz
Conference paper
Part of the Advances in Science, Technology & Innovation book series (ASTI)


Learning the spatial distribution of soil organic matter content is essential for the planning of land use and environmental protection. Because laboratory measurement of soil samples is time-consuming and costly, a good alternative is required to estimate spatial content of soil organic matter. This problem can be solved by using remote sensing and GIS techniques. In this study, soil organic matter content was estimated from remote sensing data derived from LandSat8 satellite image by generating a multi linear regression model using the backward regression technique. The multiple regression equation between SOM and remote sensing data was significant with R = 0.678. The resulting multi linear regression equation was then used for the spatial prediction for the entire study area. The predicted SOM derived from remote sensing data was used as auxiliary variable using cokriging spatial interpolation technique. Integrate remote sensing data with cokriging method improves significantly the estimates of surface soil organic matter content.


Soil organic matter Remote sensing Spatial estimation Multi linear regression Cokriging 


  1. 1.
    Sparks, D.L.: Advances Agronomy, vol. 129, pp. 1–322, Elsevier, New York (2015)Google Scholar
  2. 2.
    Huang, B., Sun, W.X., Zhao, Y.C., Zhu, J., Yang, R.Q., Zou, Z., Ding, F., Su, J.P.: Temporal and spatial variability of soil organic matter and total nitrogen in an agricultural ecosystem as affected by farming practices. Geoderma 139(3–4), 336–345 (2007)Google Scholar
  3. 3.
    Mao, Y.M., Sang, S.X., Liu, S.Q., Jia, J.L.: Spatial distribution of pH and organic matter in urban soils and its implications on site-specific land uses in Xuzhou, China. C. R. Biol. 337(5), 332–337 (2014)Google Scholar
  4. 4.
    Liu, S., An, N., Yang, J., Dong, S., Wang, C., Yin, Y.: Prediction of soil organic matter variability associated with different land use types in mountainous landscape in southwestern Yunnan province, China. Catena 133, 137–144 (2015)Google Scholar
  5. 5.
    Zhu, H.H., Wu, J.H., Guo, S.L., Huang, D.Y., Zhu, Q.H., Ge, T.D., Lei, T.W.: Land use and topographic position control soil organic C and N accumulation in eroded hilly watershed of the Loess Plateau. Catena 120, 64–72 (2014)Google Scholar
  6. 6.
    Song, Y.Q., Yang, L.A., Li, B., Hu, Y.M., Wang, A.L., Zhou, W., Cui, X.S., Liu, Y.L.: Spatial prediction of soil organic matter using a hybrid geostatistical model of an extreme learning machine and ordinary kriging. Sustainability 9, 754 (2017).
  7. 7.
    Mirzaee, S., Ghorbani-Dashtaki, S., Mohammadi, J., Asadi, H., Asadzadeh, F.: Spatial variability of soil organic matter using remote sensing data. Catena 145, 118–127 (2016)Google Scholar
  8. 8.
    Tian, Y., Wang, Y., Zhang, Y., Knyazikhin, Y., Bogaert, J., Myneni, R.B.: Radiative transfer-based scaling of LAI/FPAR retrievals from reflectance data of different resolutions. Remote Sens. Environ. 84, 143–159 (2002)Google Scholar
  9. 9.
    Ray, S.S., Singh, J.P., Das, G., Panigrahy, S.: Use of high resolution remote sensing data for generating site specific soil management plan. The international archives of the photogrammetry, remote sensing and spatial information sciences organic matter using remote sensing data. Catena 145, 118–127 (2004)Google Scholar
  10. 10.
    Xiao, J., Shen, Y., Tateishi, R., Bayaer, W.: Development of topsoil grain size index for monitoring desertification in arid land using remote sensing. Int. J. Remote Sens. 27(12), 2411–2422 (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Laboratoire 3E, Ecole Nationale d’Ingénieurs de SfaxUniversité de SfaxSfaxTunisia
  2. 2.Faculty of Environmental Sciences, Institute of GeographyTU-DresdenDresdenGermany

Personalised recommendations