Skip to main content
SpringerLink
Log in

Digital mapping of soil-texture classes in Batifa, Kurdistan Region of Iraq, using machine-learning models

  • RESEARCH
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

Soil texture is a key physical property that has a large effect on several other soil properties that are important for managing and planning agriculture. In the Batifa region (Iraq), there is currently no digital soil map on a moderate scale available. In this regard, remotely sensed data can aid in mapping soil-texture fractions. The purpose of this study was to evaluate the performance of three different machine-learning (ML) models (random forest [RF], support vector regression [SVR], and extreme gradient boosting [XGBoost]) to spatially estimate soil-texture classes using Landsat 8 and a digital elevation model (DEM). To this end, 96 soil samples with a surface layer 0–30-cm deep were collected to estimate their soil-texture fractions using 19 variables. These comprised Landsat Spectral Bands 1–7, 10, and 11, the Normalized Difference Soil Index, Enhanced Vegetation Index, Simple Soil Ratio Clay Index, Brightness Index, Grain Size Index, Normalized Difference Vegetation Index, Normalized Difference Sand Index, Soil Adjusted Vegetation Index, Landsat Bareness Index, and DEM. High coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) values were used to select the best ML model. The results of this study showed that the XGBoost model estimated the soil-texture fractions (clay: R2 = 90%, silt: R2 = 85%, and sand: R2 = 91%) better than the RF (clay: R2 = 86%, silt: R2 = 66%, and sand: R2 = 86%) and SVR (clay: R2 = 72%, silt: R2 = 56%, and sand: R2 = 86%) models. The best estimators of soil-texture fractions were the GSI for clay, the DEM for silt, and Band 10 for sand, followed by other variables based on satellite data. In general, however, the DEM is considered a good estimator for all soil-texture fractions. These findings will help support techniques for managing soil in places where the surface soil has different textures.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.

References

  • Akpa SIC, Odeh IOA, Bishop TFA, Hartemink AE (2014) Digital Mapping of Soil Particle-Size Fractions for Nigeria. Soil Sci Soc Am J 78:1953–1966. https://doi.org/10.2136/sssaj2014.05.0202

    Article  Google Scholar 

  • Al-Khafagi QDE, Ahmed HM (2021) The relationship between iron forms and some soil properties under different vegetation cover in Duhok Governorate-Northern Iraq. Kirkuk University. J Agric Sci 12(2):111–119

  • Ameen HA, Salem SMA (2016) Assessment of soil quality indicators on different slope aspects in Duhok’s highlands (Kurdistan region–Iraq). J Zankoy Sulaimani (JZS), 18(2): 209–220

  • Amjad M, Ahmad I, Ahmad M, Wróblewski P, Kamiński P, Amjad U (2022) Prediction of pile bearing capacity using XGBoost algorithm: modeling and performance evaluation. Appl Sci 12:2126

    Article  Google Scholar 

  • Asfaw E, Suryabhagavan KV, Argaw M (2018) Soil salinity modeling and mapping using remote sensing and GIS: The case of Wonji sugar cane irrigation farm, Ethiopia. J Saudi Soc Agric Sci 17:250–258. https://doi.org/10.1016/j.jssas.2016.05.003

    Article  Google Scholar 

  • Biney JKM, Vašát R, Bell SM, Kebonye NM, Klement A, John K, Borůvka L (2022) Prediction of topsoil organic carbon content with Sentinel-2 imagery and spectroscopic measurements under different conditions using an ensemble model approach with multiple pre-treatment combinations. Soil Tillage Res 220:105379

    Article  Google Scholar 

  • Bodaghabadi MB, Martínez-Casasnovas J, Salehi MH, Mohammadi J, Borujeni IE, Toomanian N, Gandomkar A (2015) Digital soil mapping using artificial neural networks and terrain-related attributes. Pedosphere 25:580–591

    Article  Google Scholar 

  • Boehmke B, Greenwell B (2019) Hands-on machine learning with R. Boca Raton, FL, USA: CRC Press, 2019

  • Bousbih S, Zribi M, Pelletier C, Gorrab A, Lili-Chabaane Z, Baghdadi N, Ben Aissa N, Mougenot B (2019) Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2. Remote Sensing 11:1520

    Article  Google Scholar 

  • Breiman L (2001) Random Forests. Mach Learn 45:5–32

    Article  Google Scholar 

  • Burges CJ, Schölkopf B (1996) Improving the accuracy and speed of support vector machines. Advances in Neural Information Processing Systems 9: Proc. 1996 Conf., pp. 375–381

  • Chagas CdS, de Carvalho JW, Bhering SB, Calderano Filho B (2016) Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions. Catena 139:232–240. https://doi.org/10.1016/j.catena.2016.01.001

    Article  Google Scholar 

  • Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM. pp 785–794

  • Efron B, Tibshirani R (1986) Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy. Stat Sci 1(54–75):22

    Google Scholar 

  • Hameed H (2013) Water harvesting in Erbil Governorate, Kurdistan region, Iraq: detection of suitable sites using geographic information system and remote sensing. Master’s Thesis, Lund University, Lund, Sweden

  • Haobo L, Jindi W, Suhong L, Yonghua Q, Huawei W (2005) Studies on urban areas extraction from landsat TM images. Proceedings 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005 IGARSS '05. pp 3826–3829

  • Hengl T, Mendes de Jesus J, Heuvelink GB, Ruiperez Gonzalez M, Kilibarda M, Blagotić A, Shangguan W, Wright MN, Geng X, Bauer-Marschallinger B (2017) SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12:e0169748

    Article  Google Scholar 

  • Heung B, Ho HC, Zhang J, Knudby A, Bulmer CE, Schmidt MG (2016) An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping. Geoderma 265:62–77. https://doi.org/10.1016/j.geoderma.2015.11.014

    Article  Google Scholar 

  • Horn R, Taubner H, Wuttke M, Baumgartl T (1994) Soil physical properties related to soil structure. Soil and Tillage Res 30:187–216. https://doi.org/10.1016/0167-1987(94)90005-1

    Article  Google Scholar 

  • Huang C, Davis L, Townshend J (2002) An assessment of support vector machines for land cover classification. Int J Remote Sens 23:725–749

    Article  Google Scholar 

  • Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25:295–309. https://doi.org/10.1016/0034-4257(88)90106-X

    Article  Google Scholar 

  • Kaya F, Başayiğit L (2022) Spatial Prediction and Digital Mapping of Soil Texture Classes in a Floodplain Using Multinomial Logistic Regression. In: Kahraman C, Cebi S, CevikOnar S, Oztaysi B, Tolga AC, Sari IU (eds) Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation. Springer International Publishing, Cham, pp 463–473

    Chapter  Google Scholar 

  • Kaya F, Başayiğit L, Keshavarzi A, Francaviglia R (2022) a) Digital mapping for soil texture class prediction in northwestern Türkiye by different machine learning algorithms. Geoderma Regional 31:e00584. https://doi.org/10.1016/j.geodrs.2022.e00584

    Article  Google Scholar 

  • Khan NM, Rastoskuev VV, Shalina EV, Sato Y (2001) Mapping salt-affected soils using remote sensing indicators—a simple approach with the use of GIS IDRISI. In: Proceedings of the 22nd Asian Conference on Remote Sensing, 5(9).

  • Kuhn M (2008) Building Predictive Models in R Using the caret Package. J Stat Softw 28:1–26. https://doi.org/10.18637/jss.v028.i05

    Article  Google Scholar 

  • Ma Y, Minasny B, Malone BP, Mcbratney AB (2019) Pedology and digital soil mapping (DSM). Eur J Soil Sci 70:216–235

    Article  Google Scholar 

  • Martín MÁ, Pachepsky YA, García-Gutiérrez C, Reyes M (2018) On soil textural classifications and soil-texture-based estimations. Solid Earth 9:159–165. https://doi.org/10.5194/se-9-159-2018

    Article  Google Scholar 

  • Mather P, Tso B (2016) Classification methods for remotely sensed data. CRC Press

    Book  Google Scholar 

  • Matsushita B, Yang W, Chen J, Onda Y, Qiu G (2007) Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-density Cypress Forest. Sensors 7:2636–2651

    Article  Google Scholar 

  • Mirzaeitalarposhti R, Shafizadeh-Moghadam H, Taghizadeh-Mehrjardi R, Demyan MS (2022) Digital Soil Texture Mapping and Spatial Transferability of Machine Learning Models Using Sentinel-1, Sentinel-2, and Terrain-Derived Covariates. Remote Sensing 14:5909

    Article  Google Scholar 

  • Muhaimeed AS, Saloom A, Saliem K, Alani K, Muklef W (2014) Classification and distribution of Iraqi soils. Int J Agric Innov Res 2:997–1002

    Google Scholar 

  • Mustafa YT, Ismail DR (2019) Land Use Land Cover Change in Zakho District, Kurdistan Region, Iraq: Past, Current and Future. 2019 International Conference on Advanced Science and Engineering (ICOASE). pp 141–146

  • Nguyen KA, Chen W (2021) DEM- and GIS-Based Analysis of Soil Erosion Depth Using Machine Learning. ISPRS Int J Geo Inf 10:452

    Article  Google Scholar 

  • Pan X, Zhu X, Yang Y, Cao C, Zhang X, Shan L (2018) Applicability of downscaling land surface temperature by using normalized difference sand index. Sci Rep 8:1–14

    Article  Google Scholar 

  • Rouse Jr JW, Haas RH, Deering D, Schell J, Harlan JC (1974) Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetation. NASA Goddard Sp. Flight Cent

  • Ruiz HA (2005) Incremento da exatidão da análise granulométrica do solo por meio da coleta da suspensão (silte+ argila). Rev Bras Ciênc Solo 29:297–300

    Article  Google Scholar 

  • Samat A, Li E, Du P, Liu S, Xia J (2021) GPU-Accelerated CatBoost-Forest for Hyperspectral Image Classification Via Parallelized mRMR Ensemble Subspace Feature Selection. IEEE J Sel Top Appl Earth Obs Remote Sens 14:3200–3214. https://doi.org/10.1109/JSTARS.2021.3063507

    Article  Google Scholar 

  • Sumfleth K, Duttmann R (2008) Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators. Ecol Ind 8:485–501. https://doi.org/10.1016/j.ecolind.2007.05.005

    Article  Google Scholar 

  • Sun F, Wang R, Wan B, Su Y, Guo Q, Huang Y, Wu X (2019) Efficiency of Extreme Gradient Boosting for Imbalanced Land Cover Classification Using an Extended Margin and Disagreement Performance. ISPRS Int J Geo Inf 8:315

    Article  Google Scholar 

  • Taati A, Sarmadian F, Mousavi A, Pour CTH, Shahir AHE (2015) Land use classification using support vector machine and maximum likelihood algorithms by Landsat 5 TM images. Walailak J Sci Technol (WJST) 12:681–687

    Google Scholar 

  • Taghizadeh-Mehrjardi R, Emadi M, Cherati A, Heung B, Mosavi A, Scholten T (2021) Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions. Remote Sensing 13:1025

    Article  Google Scholar 

  • Van der Linden S, Rabe A, Held M, Wirth F, Suess S, Okujeni A, Hostert P (2014) imageSVM Classification, Manual for Application: imageSVM version 3.0. Humboldt-Universität zu Berlin, Germany

    Google Scholar 

  • Wang D-C, Zhang G-L, Zhao M-S, Pan X-Z, Zhao Y-G, Li D-C, Macmillan B (2015) Retrieval and mapping of soil texture based on land surface diurnal temperature range data from MODIS. PLoS ONE 10:e0129977

    Article  Google Scholar 

  • Wilding L (1985) Spatial variability: its documentation, accomodation and implication to soil surveys. Soil spatial variability, Las Vegas NV, 30 November-1 December 1984. pp 166–194

  • Xiao J, Shen Y, Tateishi R, Bayaer W (2006) Development of topsoil grain size index for monitoring desertification in arid land using remote sensing. Int J Remote Sens 27:2411–2422. https://doi.org/10.1080/01431160600554363

    Article  Google Scholar 

  • Zeng P, Song X, Yang H, Wei N, Du L (2022) Digital Soil Mapping of Soil Organic Matter with Deep Learning Algorithms. ISPRS Int J Geo Inf 11:299

    Article  Google Scholar 

  • Zeraatpisheh M, Ayoubi S, Mirbagheri Z, Mosaddeghi MR, Xu M (2021) Spatial prediction of soil aggregate stability and soil organic carbon in aggregate fractions using machine learning algorithms and environmental variables. Geoderma Reg 27:e00440

    Article  Google Scholar 

  • Zhang Y, Guo L, Chen Y, Shi T, Luo M, Ju Q, Zhang H, Wang S (2019a) Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province. China Remote Sens 11:1683

    Article  Google Scholar 

  • Zhang Y, Sui B, Shen H, Ouyang L (2019b) Mapping stocks of soil total nitrogen using remote sensing data: A comparison of random forest models with different predictors. Comput Electron Agric 160:23–30

    Article  Google Scholar 

  • Zhang Z, Ding J, Zhu C, Chen X, Wang J, Han L, Ma X, Xu D (2021) Bivariate empirical mode decomposition of the spatial variation in the soil organic matter content: A case study from NW China. Catena 206:105572

    Article  Google Scholar 

  • Zhang M, Shi W (2019) Systematic comparison of five machine-learning methods in classification and interpolation of soil particle size fractions using different transformed data. Hydrol Erth System Sci. https://doi.org/10.5194/hess-2019-648

  • Zhou Y, Wu W, Wang H, Zhang X, Yang C, Liu H (2022) Identification of Soil Texture Classes Under Vegetation Cover Based on Sentinel-2 Data With SVM and SHAP Techniques. IEEE J Sel Top Appl Earth Obs Remote Sens 15:3758–3770. https://doi.org/10.1109/JSTARS.2022.3164140

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank Faculty of science, University of Zakho and Ministry of agricultural and water resources management in Kurdistan for their support in achieving this research.

Author information

Authors and Affiliations

  1. Department of Environmental Sciences, Faculty of Science, University of Zakho, Zakho, Kurdistan Region, Iraq

    Bushra S. Yousif & Yaseen T. Mustafa

  2. Department of Computer Science, College of Science, Nawroz University, Duhok, Kurdistan Region, Iraq

    Yaseen T. Mustafa

  3. Department of Soil and Water Science, College of Agricultural Engineering Sciences, University of Duhok, Duhok, Kurdistan Region, Iraq

    Mohammed A. Fayyadh

Authors
  1. Bushra S. Yousif
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yaseen T. Mustafa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammed A. Fayyadh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, B. Y. (Bushra Shaban Yousif), Y. M. (Yaseen T. Mustafa) and M. F. (Mohammed Ali Fayyadh); methodology, B. Y. and Y. M.; software, Y. M.; validation, B.Y., Y.M. and M.F.; formal analysis, B.Y.; investigation, B. Y. and Y. M.; resources, B. Y.; data curation, B. Y.; writing—original draft preparation, B. Y. and Y. M.; writing—review and editing Y. M. and M. F.; visualization, B. Y., Y. M. and M. F.; supervision, B. Y., Y. M. and M. F.; project administration, Y. M. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Yaseen T. Mustafa.

Ethics declarations

Competing interests

The authors declare no competing interests.

Conflicts of interest

The authors declare no conflict of interest.

Additional information

Communicated by: H. Babaie

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yousif, B.S., Mustafa, Y.T. & Fayyadh, M.A. Digital mapping of soil-texture classes in Batifa, Kurdistan Region of Iraq, using machine-learning models. Earth Sci Inform 16, 1687–1700 (2023). https://doi.org/10.1007/s12145-023-01005-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-023-01005-8

Keywords

Search

Navigation