Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Sparks, D.L.: Advances Agronomy, vol. 129, pp. 1–322, Elsevier, New York (2015)
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)
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)
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)
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)
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). https://doi.org/10.3390/su9050754
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Medhioub, E., Bouaziz, M., Bouaziz, S. (2019). Spatial Estimation of Soil Organic Matter Content Using Remote Sensing Data in Southern Tunisia. In: El-Askary, H., Lee, S., Heggy, E., Pradhan, B. (eds) Advances in Remote Sensing and Geo Informatics Applications. CAJG 2018. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-01440-7_50
Download citation
DOI: https://doi.org/10.1007/978-3-030-01440-7_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01439-1
Online ISBN: 978-3-030-01440-7
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)