Assessment and mapping of soil salinity using electromagnetic induction and Landsat 8 OLI remote sensing data in an irrigated olive orchard under semi-arid conditions

Gharsallah M.E., Aichi H., Stambouli T., Ben Rabah Z., Ben Hassine H. (2022): Assessment and mapping of soil salinity using electromagnetic induction and Landsat 8 OLI remote sensing data in an irrigated olive orchard under semi-arid conditions. Soil & Water Res., 17: 15−28.

download PDF

Salinisation threatens the sustainability of irrigated olive orchards in Tunisia. Electromagnetic induction measurements and soil spectral index calculations could help to survey the soil salinity. This study aimed to map changes in the soil salinity spatial pattern using geostatistical techniques and soil spectral index regression. The study area is located in Sminja, Tunisia. It is a 665 ha olive orchard, landscaped in ridges and furrows and managed following a very high-density planting system (1.5 × 4 m2). Electromagnetic readings measured in situ with an electromagnetic device (EM38) that was fitted, in turn, to the electrical conductivity of the saturated paste of five soil depths namely: 0–20, 20–40, 40–60, 60–80 and 80–100 cm and to the average electrical conductivity of the saturated paste of the 0–100 cm soil depth. Both the ordinary kriging and universal kriging performed similarly and well in mapping the soil salinity. (R2= 0.86 and 0.89 for the 0–20 cm and the 0–100 cm depths, respectively). Our results prove that mapping the soil salinity based on electromagnetic induction and kriging methods is an effective approach, which allows one to monitor the soil salinity within permanent croplands in semi-arid conditions. Salinisation that reaches intolerable values by olive trees, is especially accumulated on the top of the ridges, where the drippers are installed. Furthermore, based on two Landsat 8 images acquired on April 30, 2019 and May 16, 2019, respectively, we calculated seven soil spectral indices. Nevertheless, multiple regression models between the electromagnetic readings and various combinations of soil spectral indices were poor. In the coming investigations, under permanent land cover, spectral index regression models should integrate not only the soil, but also vegetation indices.

Abbas A., Khan S. (2007): Using remote sensing techniques for appraisal of irrigated soil salinity. In: Oxley L., Kulasiri D. (eds): MODSIM 2007 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, Brighton, Dec 2007: 2632–2638.
Akramkhanov A., Brus D.J., Walvoort D.J.J. (2014): Geostatistical monitoring of soil salinity in Uzbekistan by repeated EMI surveys. Geoderma, 213: 600–607.
Amezketa (2007): Soil salinity assessment using directed soil sampling from a geophysical survey with electromagnetic technology: a case study. Spanish Journal of Agricultural Research, 5: 91–101.
Bannari A., Guedon A.M., El-Harti A., Cherkaoui F.Z., El-Ghmari A. (2008): Characterization of slightly and moderately saline and sodic soils in irrigated agricultural land using simulated data of advanced land imaging (EO-1) sensor. Communications in Soil Science and Plant Analysis, 39: 2795–2811.
Boudabous K., Ben Aissa N., Trifa Y., Sahli A., Slim Amara H. (2016): Soil microorganisms alleviates the negative effect of salinity on morpho-physiological characteristics during growth stages of durum wheat genotypes. Journal of New Sciences, 28: 1622–1630.
Bouksila F., Persson M., Berndtsson R., Bahri A. (2010): Estimating soil salinity over a shallow saline water table in semiarid Tunisia. The Open Hydrology Journal, 4: 91–101.
Chavez P.S.J. (1996): Image-based atmospheric corrections – revisited and improved. Photogrammetric Engineering and Remote Sensing, 62: 1025–1036.
CRUESI (1970): Research and Training in Saltwater Irrigation, 1963–1969. Technical Report-Tunisia, Paris, PNUD-UNESCO. (in French)
Deering D., Rouse J. (1975): Measuring ‘forage production’ of grazing units from Landsat MSS data. In: 10th Int. Symp. Remote Sensing of Environment, ERIM, Ann Arbor, 1975: 1169–1178.
Ding J., Yu D. (2014): Monitoring and evaluating spatial variability of soil salinity in dry and wet seasons in the Werigan-Kuqa Oasis, China, using remote sensing and electromagnetic induction instruments. Geoderma, 235–236: 316–322.
Douaoui A.E.K., Nicolas H., Walter C. (2006): Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data. Geoderma, 134: 217–230.
Farifteh J., Van der Meer F., Atzberger C., Carranza E.J.M. (2007): Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN). Remote Sensing of Environment, 110: 59–78.
Filippi L. (2017): Olive Oil, Tunisia's "Green Gold". Information Bulletin of the National Office of the Oil. Available at (in French)
Hachicha M. (2016): Gestion des ressources en sols sous irrigation avec les eaux salées. National Research Institute in Rural Engineering, Water and Forestry, University of Carthage.
Hillel D., Vlek P. (2005): The sustainability of irrigation. Advances in Agronomy, 87: 55–84.
Kahlaoui B., Hachicha M., Rejeb S., Rejeb M.N., Hanchi B., Misle E. (2011): Effects of saline water on tomato under subsurface drip irrigation: Nutritional and foliar aspects. Journal of Soil Science and Plant Nutrition, 11: 69–86.
Khan N.M., Rastoskuev V., Sato Y., Shiozawa S. (2005): Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators. Agricultural Water Management, 77: 96–109.
Knotters M., Heuvelink G.B.M., Hoogland T., Walvoort D.J.J. (2010): A Disposition of Interpolation Techniques. WOt-Werkdocument 190. Wageningen, Statutory Research Tasks Unit for Nature and the Environment.
Larbi A.S. Vazquez H., El-Jendoubi M., Msallem J., Abadıa A., Morales F. (2015): Canopy light heterogeneity drives leaf anatomical, eco-physiological, and photosynthetic changes in olive trees grown in a high-density plantation. Photosynthesis Research, 123: 141–155.
Larbi A., Msallem M., Mestaoui S., Sai M.B., El Gharous M., Boulal H. (2016): Fertilization practices in Tunisian high-density olive planting systems. Better Crops, 100: 9–11.
Li X.-M., Yang J.-S., Liu M.-X., Liu G.-M., Yu M. (2012): Spatio-temporal changes of soil salinity in arid areas of south Xinjiang using electromagnetic induction. Journal of Integrative Agriculture, 11: 1365–1376.
Liu G., Li J., Zhang X., Wang X., Lv Z., Yang J., Shao H., Yu S. (2016): GIS-mapping spatial distribution of soil salinity for eco-restoring the Yellow River Delta in combination with electromagnetic Induction. Ecological Engineering, 94: 306–314.
Liu G.M., Yang J.S., Ju M.S., Nie J. (2003): Technology of chorometry using electromagnetic induction and its application in agriculture. Soils, 35: 27–29.
Masmoudi C.C., Msallem M., Larbi A., Sai B., Siala S., Kchaou M. (2017): Establishment and Conduct of an Intensive Plantation of Olive Trees. Tunis, Institution de la Recherche et de l’Enseignement Supérieurs Agricoles Institut de l’Olivier -Station du Nord. (in French)
Mesić Kiš I. (2016): Comparison of ordinary and universal kriging interpolation techniques on a depth variable (a case of linear spatial trend), case study of the Šandrovac Field. Rudarsko Geolosko Naftni Zbornik. The Mining-Geology-Petroleum Engineering Bulletin UDC: 528. 9:912.
Metternicht G.I., Zinck J.A. (2003): Remote sensing of soil salinity: Potentials and constraints. Remote Sensing of Environment, 85: 1–20.
Michot D., Walter C., Adam I., Guéro Y. (2013): Digital assessment of soil-salinity dynamics after a major flood in the Niger River valley. Geoderma, 208: 193–204.
Oliver M., Webster R. (2014): A tutorial guide to geostatistics: Computing and modelling variograms and kriging. Catena, 113: 56–69.
Ouji A., El-Bok S., Mouelhi M., Ben Younes M., Kharrat M. (2015): Effect of salinity stress on germination of five Tunisian lentil (Lens culinaris L.) genotypes. European Scientific Journal, 11: 63–75.
Radinovsky L. (2019): Worldwide Olive Oil Production
Estimates Compared. Available at (accessed
March 3, 2019).
Richards L.A. (1954): Diagnosis and Improvement of Saline and Alkali Soils. Handbook No. 60, Washington, U.S. Salinity Laboratory Staff.
Shrestha R.P. (2006): Relating soil electrical conductivity to remote sensing and other soil properties for assessing soil salinity in northeast Thailand. Land Degradation and Development, 17: 677–689.
Taghizadeh-Mehrjardi R., Minasny B., Sarmadian F., Malone B.P. (2014): Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma, 213: 15–28.
Tucker C.J. (1979): Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8: 127–150.
Webster R., Oliver M.A. (2007): Geostatistics for Environmental Scientists. 2nd Ed. Chichister, John Wiley and Sons.
Wu Y.K., Yang J.S., Li X.M. (2009): Study on spatial variability of soil salinity based on spectral indices and EM38 readings. Spectroscopy and Spectral Analysis, 29: 1023–1027.
Yao R.J., Yang J.S., Zou P., Liu G.M., Yu S.P. (2008): Quantitative evaluation of the field soil salinity and its spatial distribution based on electromagnetic induction instruments. Scientia Agricultura Sinica, 41: 460–469.
Yao R.J., Yang J.S., Wu D.H., Xie W.P., Cui S.Y., Wang X.P., Yu S.P., Zhang X. (2015): Determining soil salinity and plant biomass response for a farmed cropland using the electromagnetic induction method. Computers and Electronics in Agriculture, 19: 241–253.
Zhang T.J., Yang J.S., Liu G.M., Yao R.J. (2009): Interpretation of salinity characteristics of normal profile in estuarine region by using electromagnetic induction. Transactions of the Chinese Society of Agricultural Engineering, 25: 109–113.
download PDF

© 2022 Czech Academy of Agricultural Sciences | Prohlášení o přístupnosti