Predictive Digital Mapping of Surface Soil Properties using Remote Sensing and Multivariate Statistical Analysis.


  • Kwestan O. Abdalkarim Department of Natural Resource, College of Agricultural Engineering Sciences, Sulaimaniyah University, Sulaimaniyah, Kurdistan Region, Iraq
  • Heman Abdulkhaleq A. Gaznayee Department of Forestry, College of Agricultural Engineering Sciences, Salahaddin University, Erbil, Iraq - Erbil 44003, Kurdistan Region, Iraq
  • Ayad M. F. Al-Quraishi Petroleum and Mining Engineering Department, Faculty of Engineering, Tishk International University, Erbil, 44001, Kurdistan Region, Iraq
  • Zhino O. Abdalla Department of Natural Resource, College of Agricultural Engineering Sciences, Sulaimaniyah University, Sulaimaniyah, Kurdistan Region, Iraq



Landsat 8-OLI, Sulaimaniyah, Soil Map, Spectral response, Surface soil properties.


     Accurate prediction of surface soil properties is crucial for agricultural and environmental purposes. This study aimed to utilize geoinformatics approaches and Landsat OLI-8 data to predict specific physicochemical properties of the surface soil in Sulaimaniyah, Kurdistan Region of Iraq (KRI). It also examined the statistical relationships between these properties and spectral reflectance, vegetation cover, soil/vegetation moisture contents, and elevation. The study made use of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI), as well as seven bands of the OLI image for the statistical analysis. The results demonstrated a statistical connection between organic matter (O.M.) and vegetation cover based on NDVI. It was observed that the northern parts of Sulaimaniyah exhibited dense vegetation, albeit covering a small area. Generally, mountainous regions had a higher proportion of canopy cover compared to other parts of the arid zone, with moisture availability being the most influential factor on vegetation. Moreover, the majority of the research area showed the highest CaCO3 content and a significant negative relationship was found between vegetation (NDVI) and soil moisture (NDMI) with organic matter (O.M.) and clay. Using geoinformatics datasets and techniques proved valuable in identifying, mapping, and investigating specific surface physicochemical properties in the study area


Abuelgasim, A. and Ammad, R. (2019) ‘Mapping soil salinity in arid and semi-arid regions using Landsat 8 OLI satellite data’, Remote Sensing Applications: Society and Environment, 13, pp. 415–425.

Al-Quraishi, A.M.F., Gaznayee, H.A. and Crespi, M. (2021) ‘Drought trend analysis in a semi-arid area of Iraq based on Normalized Difference Vegetation Index, Normalized Difference Water Index and Standardized Precipitation Index’, Journal of Arid Land, 13(4), pp. 413–430

Akbari, M. et al. (2021) ‘Predicting soil organic carbon by integrating Landsat 8 OLI, GIS and data mining techniques in semi-arid region’, Earth Science Informatics, 14(4), pp. 2113–2122.

Al-Quraishi, A., Razvanchy, H. and Gaznayee, H. (2020) ‘A Comparative Study for Performance of Five Landsat-based Vegetation Indices: Their Relations to Some Ecological and Terrain Variables’, Journal of Geoinformatics & Environmental Research, 1(1), pp. 20–37.

Al-Quraishi, A.M.F. and Negm, A.M. (2019) ‘Environmental Remote Sensing and GIS in Iraq , Springer’, (April) SBN-13: 978-3030213435

Al-Quraishi, A.M.F., Sadiq, H.A. and Messina, J.P. (2019) ‘Characterization and modeling surface soil physicochemical properties using Landsat images: A case study in the Iraqi Kurdistan region’, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(2/W16), pp. 21–28.

Asam, S. et al. (2018) ‘Relationship between Spatiotemporal Variations of Climate, Snow Cover and Plant Phenology over the Alps—An Earth Observation-Based Analysis’, Remote Sensing, 10(11), p. 1757.

Babbie, E.R. and Benaquisto, L. (2009) Fundamentals of social research. Cengage Learning.

Buringh, P.D. (1960) ‘Soils and Soil Conditions In Iraq’, The Ministry of Agriculture, 1, pp. 13–72. 10.4236/jwarp.2014.65041

Busing, R.T., White, P.S. and Mackenzie, M.D. (1993) ‘Gradient analysis of old spruce-fir forests of the Great Smoky Mountains circa 1935’, Canadian Journal of Botany, 71(7), pp. 951–958.

Eskandari Dameneh, H. et al. (2021) ‘Desertification of Iran in the early twenty-first century: assessment using climate and vegetation indices’, Scientific Reports, 11(1), pp. 1–18.

Gaznayee, Heman Abdulkhaleq A; Al-Quraishi, A.M.F. (2019) ‘Analysis of agricultural drought, rainfall, and crop yield relationships in erbil province, the kurdistan region of iraq based on landsat time-series msavi2’, Journal of Advanced Research in Dynamical and Control Systems, 11(12 Special Issue), pp. 536–545. 10.5373/JARDCS/V11SP12/20193249

Hartemink, A.E. (2016) ‘The definition of soil since the early 1800s’, Advances in Agronomy, 137, pp. 73–126.

Hayes, D.J. and Cohen, W.B. (2007) ‘Spatial, spectral and temporal patterns of tropical forest cover change as observed with multiple scales of optical satellite data’, Remote Sensing of Environment, 106(1), pp. 1–16.

Herrmann, H. and Bucksch, H. (2014) Soil Science, Dictionary Geotechnical Engineering/Wörterbuch GeoTechnik. doi:10.1007/978-3-642-41714-6_195362.

Huang, N.E. and Shen, S.S.P. (2005) ‘Hilbert-huang transform and its applications’, Hilbert-huang Transform And Its Applications, pp. 1–311.

Jin, X.M. et al. (2008) ‘Impact of elevation and aspect on the spatial distribution of vegetation in the Qilian Mountain area with remote sensing data’, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 37(July), pp. 1385–1390.

Kapoor, L.D. (2020) ‘Botanical Studies’, Opium Poppy, 11(1954), pp. 19–40.

Kercival, N. (2015) ‘Assessing Changes in Land Use and Land Cover using Remote Sensing: A Case Study of the Umhlanga Ridge Sub-Place’, (December). Available at:

Lutes, D.C. et al. (2006) ‘FIREMON: Fire effects monitoring and inventory system. Gen. Tech. Rep. USDA Forest Service RMRS-GTR-164-CD’, USDA Forest Service - Research Paper, (June), p. 410.

Mian, I.A. (2011) ‘Characterization Of Rod Kohi Soils Of D . I . Khan , Pakistan Citation ’:, 27(4), pp. 27–30. 10.5772/INTECHOPEN.85889

Najmaddin, P.M., Whelan, M.J. and Balzter, H. (2017) ‘Estimating daily reference evapotranspiration in a semi-arid region using remote sensing data’, Remote Sensing, 9(8).

Nanni, M.R. et al. (2012) ‘Informações espectrais de imagens landsat da superfície do solo como indicativo na discriminação de classes de solos’, Acta Scientiarum - Agronomy, 34(1), pp. 103–112.

Nawar, S., Buddenbaum, H. and Hill, J. (2015) ‘Digital mapping of soil properties using multivariate statistical analysis and ASTER data in an Arid region’, Remote Sensing, 7(2), pp. 1181–1205.

Parent, L.E., Caron, J. and Carter, M.R. (1993) ‘Physical properties of organic soils’, Soil sampling and methods of analysis, pp. 441–458.

Qader, S.H. et al. (2021) ‘The role of earth observation in achieving sustainable agricultural production in arid and semi-arid regions of the world’, Remote Sensing, 13(17), pp. 1–27.

Qi, J. et al. (1994) ‘A modified adjusted vegetation index (MSAVI)’, Remote sensing and environment., 48(2), pp. 119–126.

R. R. Gillies, W.P.K.& K.S.H. (2010) ‘A verification of the ’ triangle ’ method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index ( NDVI ) and surface e’, International Journal of Remote Sensing, (January 2013), pp. 37–41.

Razvanchy, H.A.S. (2008) Modelling Some of the Soil Propertiesin the Iraqi Kurdistan Region using Landsat Datasets and Spectroradiometer.

Red, N. (2006) ‘List of Vegetation Spectral Indices References Baret , F ., G . Guyot , and D . Major . 1989 . TSAVI : a vegetation index which minimizes soil brightness effects on LAI and APAR estimation . 12 th Canadian Symposium on Remote Sensing and IGARSS ’ 90 , p .’, Remote Sensing of Environment, pp. 1994–1996.

Rouse, J.W. et al. (1974) ‘Monitoring the vernal advancements and retrogradation of natural vegetation’, NASA/GSFC, Final Report, Greenbelt, MD, USA, (September 1972), pp. 1–137. Available at:,5%5Cnpapers2://publication/uuid/FB22B85B-B2F9-442E-AF63-58F3517012FC.

Senanayake, S. et al. (2020) ‘A review on assessing and mapping soil erosion hazard using geo-informatics technology for farming system management’, Remote Sensing, 12(24), pp. 1–25.

Shabou, M. et al. (2015) ‘Soil clay content mapping using a time series of Landsat TM data in semi-arid lands’, Remote Sensing, 7(5), pp. 6059–6078.

Shahabfar, A. and Eitzinger, J. (2013) ‘Spatio-temporal analysis of droughts in semi-arid regions by using meteorological drought indices’, Atmosphere, 4(2), pp. 94–112.

USGS (2017) ‘Product guide’, (October), pp. 1–14. doi:10.1016/0042-207X(74)93024-3. IOP Conference Series: Earth and Environmental Science, Volume 338, Southeast Asian Geography Association (SEAGA) 13th Conference 28 November to 1 December 2017, Universitas Indonesia

Vaudour, E. et al. (2015) ‘An overview of the recent approaches to terroir functional modelling, footprinting and zoning’, Soil, 1(1), pp. 287–312.

Vicente Barros and Christopher B. Field (2012) Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. doi:10.1596/978-0-8213-8845-7.

Wodaje, S.T. (2016) ‘Land Degradation Vulnerability Assessment Using GIS and Remote Sensing in Beshilo River Basin, Ethiopia’, (August), pp. 1–81.

Xue, J. and Su, B. (2017) ‘Significant remote sensing vegetation indices: a review of developments and applications’, Journal of sensors, Vol.2017, p. 17p.

Yaghoobi, A. and Zargar, H. (2012) ‘Handling Uncertainty In Hydrologic Analysis and Drought Risk Assessment’, (December), p. 154.



How to Cite

Abdalkarim, K. O., Heman Abdulkhaleq A. Gaznayee, Ayad M. F. Al-Quraishi, & Zhino O. Abdalla. (2023). Predictive Digital Mapping of Surface Soil Properties using Remote Sensing and Multivariate Statistical Analysis. Zanco Journal of Pure and Applied Sciences, 35(6), 189–203.



Agricultural and Environmental Researches