Soil texture mapping: a novel approach combining interpolation techniques and decision tree classifiers

نوع مقاله : Special Issue: New Approaches to Water and Soil Management and Modeling

نویسندگان

1 Centre de Recherche Scientifique et Technique sur les Régions Arides, CRSTRA, Biskra, Algeria

2 Centre de Recherche Scientifique et Technique sur les Régions Arides, CRSTRA, Biskra, Algeria, and Department of Agronomic Sciences, Laboratory of Saharan Bioresources: Preservation and Valorization, Kasdi Merbah University, Ouargla, Algeria

چکیده

This study proposes a reproducible and GIS-based methodology for digital soil texture mapping by integrating geostatistical interpolation with deterministic decision tree classifiers (DTCs) derived from the United States Department of Agriculture (USDA) soil texture classification system. A total of 68 topsoil samples (0–20 cm) were collected across the irrigated area of northern Biskra province (southeastern Algeria) and analyzed for sand, silt, and clay contents. Among the most commonly applied interpolation techniques, ordinary kriging (OK), simple kriging (SK), and inverse distance weighting (IDW) were tested to generate continuous spatial distribution maps of soil particle fractions. Since the objective of this research was methodological demonstration rather than comprehensive benchmarking of interpolation algorithms, the method showing slightly better cross-validation (LOOCV) performance was selected. OK produced marginally lower RMSE values (15.93% for sand and 13.11% for silt) and satisfactory coefficients of determination (R²=0.758 for sand and 0.687 for silt) and was therefore adopted. To preserve the compositional constraint (sand + silt + clay=100%), clay content was derived from interpolated sand and silt maps. Four deterministic DTCs were implemented within the GIS environment to convert particle fraction rasters into continuous USDA texture classes. The final texture map demonstrated an almost perfect agreement with observed classifications (Kappa coefficient=0.898). The proposed framework emphasizes methodological simplicity, transparency, and applicability under moderate sampling density without reliance on auxiliary environmental covariates or complex machine learning models. Although interpolation uncertainty may influence classification near texture boundaries, the approach provides a practical and scientifically robust solution for soil texture mapping in data-limited regions.

کلیدواژه‌ها

موضوعات

مراجع

Abzalov, M. (2016). Variography. In: Applied Mining Geology. Modern Approaches in Solid Earth Sciences, vol 12. Springer, Cham. doi: 10.1007/978-3-319-39264-6_18
Ahmadi, S.H., & Sedghamiz, A. (2007). Geostatistical Analysis of Spatial and Temporal Variations of Groundwater Level. Environmental Monitoring and Assessment, 129, 277–294. doi: 10.1007/s10661-006-9361-z
Anthony, T., Nkwunonwo, U., Emmanuel, A., & Ganiyu O. (2025). Environmental and geostatistical modelling of soil properties toward precision agriculture. Discover Soil, 2, 59. doi: 10.1007/s44378-025-00083-y
Armstrong, M., & Boufassa, A. (1988). Comparing the robustness of ordinary kriging and lognormal kriging: Outlier resistance. Mathematical Geology, 20, 447–457. doi: 10.1007/BF00892988
Arslan, H. (2012). Spatial and temporal mapping of groundwater salinity using ordinary kriging and indicator kriging: The case of Bafra Plain, Turkey. Agricultural Water Management, 113, 57–63. doi: 10.1016/j.agwat.2012.06.015
Bajjali, W. (2023). ArcGIS Pro and ArcGIS Online Applications in Water and Environmental Sciences. Springer Nature, Switzerland. doi : 10.1007/978-3-031-42227-0
Benchetrit, M. (1956). Les sols d’Algérie. Revue de Géographie Alpine, 44, 749–761. doi : 10.3406/rga.1956.1790
Bernoux, M., Arrouays, D., Cerri, C.E.P., & Cerri, C.C. (2007). Regional organic carbon storage maps of the western Brazilian Amazon based on prior soil maps and geostatistical interpolation. In: Developments in Soil Science. Elsevier B.V., pp 497–507. doi: 10.1016/S0166-2481(06)31037-9
Bidkhani, G.O.N., & Mobasheri, M.R. (2018). Influence of soil texture on the estimation of bare soil moisture content using MODIS images. European Journal of Remote Sensing, 51, 911–920. doi: 10.1080/22797254.2018.1514986
Boudibi, S. (2021). Modeling the Impact of Irrigation Water Quality on Soil salinieation in an Arid Region, Case of Biskra. 176p. doi : 10.13140/RG.2.2.12406.93768
Bradaï, A., Douaoui, A., Bettahar, N., & Yahiaoui, I. (2016). Improving the Prediction Accuracy of Groundwater Salinity Mapping Using Indicator Kriging Method. Journal of Irrigation and Drainage Engineering, 142, 11. doi: 10.1061/(ASCE)IR.1943-4774.0001019
Cambardella, C. A., Moorman, T. B., Novak, J. M., Parkin, T. B., Karlen, D. L., Turco, R. F.,  & Konopka, A. E. (1994). Field-Scale Variability of Soil Properties in Central Iowa Soils. Soil Science Society of America Journal, 58, 1501–1511. doi: 10.2136/sssaj1994.03615995005800050033x
Chau, J.F., Bagtzoglou, A.C., & Willig, M.R. (2011). The Effect of Soil Texture on Richness and Diversity of Bacterial Communities. Environment Forensics, 12, 333–341. doi: 10.1080/15275922.2011.622348
De Caires, S. A., Martin, C. S., Atwell, M. A., Kaya, F., Wuddivira, G. A., & Wuddivira, M. N. (2025). Advancing soil mapping and management using geostatistics and integrated machine learning and remote sensing techniques: a synoptic review. Discover Soil, 2(53), 1–29. doi: doi: 10.1007/s44378-025-00082-z
Durand, M.J.H., & Barbut, M.M. (1938). Carte de reconnaissance des sols d’Algérie : Biskra. Service Geographique de l’Armée.
ESRI., (2024). Using cross validation to assess interpolation results.
Grunwald, S. (2009). Geoderma multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma, 152, 195–207. doi: 10.1016/j.geoderma.2009.06.003
Han, J., Kamber, M., Pei, J. (2012). Data Mining: Concepts and Techniques, 3rd edn. Elsevier Inc, Waltham. doi: 10.1016/B978-0-12-381479-1.00022-8
Hateffard, F., Dolati, P., Heidari, A., & Zolfaghari, A.A. (2019). Assessing the performance of decision tree and neural network models in mapping soil properties. Journal of Mountain Science, 16, 1833-1847. doi: 10.1007/s11629-019-5409-8
Johnston, K., Ver Hoef, J.M., Krivoruchko, K., & Lucas, N. (2001). Using ArcGIS Geostatistical Analyst. ESRI, Redlands (California).
Kaya F., Başayiğit L., Keshavarzi A., & Francaviglia R. (2022). Digital mapping for soil texture class prediction in northwestern Türkiye by different machine learning algorithms. Geoderma Regional, 31, e00584. doi: 10.1016/j.geodrs. 2022.e00584
Kundel, H.L., & Polansky, M. (2003). Measurement of Observer Agreement. Radiology, 228,303–308. doi: 10.1148/radiol.2282011860
Lagacherie, P., McBratney, A.B., & Voltz, M. (2007). Digital Soil Mapping: An Introductory Perspective, 1st edn. Elsevier B.V., Kidlington
Landis, J.R., & Koch, G.G. (1977). The Measurement of Observer Agreement for Categorical Data. Biometrics, 33, 159–174. doi: 10.2307/2529310
Liao, K., Xu, S., Wu, J., & Zhu, Q. (2013). Spatial estimation of surface soil texture using remote sensing data. Soil Science and Plant Nutrition, 59, 488–500. doi: 10.1080/00380768.2013.802643
Maroufpoor, S., Fakhri-Fard, A., & Shiri, J. (2017). Study of the spatial distribution of groundwater quality using soft computing and geostatistical models. ISH Journal of Hydraulic Engineering, 25, 232–238. doi: 10.1080/09715010.2017.1474389
Minasny, B., & Mcbratney, A.B. (2015). Geoderma Digital soil mapping: A brief history and some lessons. Geoderma, 264, 301–311. doi: 10.1016/j.geoderma.2015.07.017
Oliver, M.A., & Webster, R. (2014). A tutorial guide to geostatistics : Computing and modelling variograms and kriging. Catena 113, 56–69. doi: 10.1016/j.catena.2013.09.006
Pannatier, Y. (1996). VARIOWIN: Software for Spatial Data Analysis in 2D. Springer, New York. doi: 10.1007/978-1-4612-2392-4
Qu, L., Lu, H., Tian, Z., Schoorl, J.M., Huang, B., Liang, Y., Qiu, D., & Liang, Y.  (2024). Spatial prediction of soil sand content at various sampling density based on geostatistical and machine learning algorithms in plain areas. Catena, 234, 107572. doi: 10.1016/j.catena.2023.107572
Karp, F.H.S., Adamchuk, V., Dutilleul, P., & Melnitchouck, A. (2024). Comparative study of interpolation methods for low-density sampling. Precision Agriculture. doi: 10.1007/s11119-024-10141-0
Rahmati, O., Falah, F., Naghibi, S. A., Biggs, T., Soltani, M., Deo, R. C., Cerdà, A., Mohammadi, F., & Bui, D. T. (2019). Land subsidence modelling using tree-based machine learning algorithms. Science of the total environment, 672, 239-252. doi: 10.1016/j.scitotenv.2019.03.496
Ravikumar, V. (2022) Sprinkler and Drip Irrigation: Theory and Practice. Springer. Singapore. doi: 10.1007/978-981-19-2775-1
Santra, P., Kumar, M., & Panwar, N. (2017). Digital soil mapping of sand content in arid western India through geostatistical approaches. Geoderma Regional, 9,56–72. doi: 10.1016/j.geodrs.2017.03.003
Seyedmohammadi, J., Navidi, M.N., & Esmaeelnejad, L. (2019). Geospatial modeling of surface soil texture of agricultural land using fuzzy logic, geostatistics and GIS techniques. Communications in Soil Science and Plant Analysis, 50, 1452–1464. doi: 10.1080/00103624.2019.1626870
Song, Q., Gao, X., Song, Y., Li, Q., Chen, Z., Li, R., Zhang, H., & Cai, S. (2023). Estimation and mapping of soil texture content based on unmanned aerial vehicle hyperspectral imaging. Scientific Reports, 13, 1–17. doi: 10.1038/s41598-023-40384-2
Şen, Z. (2020). Earth Systems Data Processing and Visualization Using MATLAB. Springer Nature, Cham. doi: 10.1007/978-3-030-01542-8
Szczerbicki, E. (2001). Management of Complexity and Information Flow. In: Gunasekaran A (ed) agile manufacturing: the 21 st century competitive strategy. Elsevier Ltd, Kidlington, p 810. doi: 10.1016/B978-008043567-1/50013-9Taghizadeh-Mehrjardi, R., Sarmadian, F., Minasny, B., Triantafilis, J., & Omid, M. (2014). Digital Mapping of Soil Classes Using Decision Tree and Auxiliary Data in the Ardakan Region, Iran. Arid Land Research and Management, 28, 37–41. doi: 10.1080/15324982.2013.828801
Taghizadeh-Mehrjardi, R., Mahdianpari, M., Mohammadimanesh, F., Behrens, T., Toomanian, N., Scholten, T., & Schmidt, K. (2020). Multi-task convolutional neural networks outperformed random forest for mapping soil particle size fractions in central Iran. Geoderma, 376, 114552. doi: 10.1016/j.geoderma.2020.114552
USDA. (1987). Soil Mechanics: USDA Soil Textural Classification Study Guide. USDA Soil Conservation Service, Washington DC.
Wadoux, A.M.J-C., Minansy, B., & McBratney, A.B. (2020). Machine learning for digital soil mapping: Applications, challenges and suggested solutions. Earth-Science Reviews, 210, 103359. doi: 10.1016/j.earscirev.2020.103359
Wani, O. A., Sharma, V., Kumar, S. S., Malik, A. R., Pandey, A., Devi, K., Kumar, V., Gairola, A., Yadav, D., Valente, D., Petrosillo, I., & Babu, S. (2024). Geostatistical modelling of soil properties towards long-term ecological sustainability of agroecosystems. Ecological Indicators, 166, 112540. doi: 10.1016/j.ecolind.2024.112540
Webster, R., & Oliver, M.A. (2007). Geostatistics for Environmental Scientists. John Wiley & Sons, Southern Gate
Wu, G., Shen, D., & Sabuncu, M.R. (2016). Machine Learning and Medical Imaging. Academic Press. Amsterdam
Yousif, B.S., Mustafa, Y.T., & Fayyadh, M.A. (2023). Digital mapping of soil-texture classes in Batifa, Kurdistan Region of Iraq, using machine-learning models. Earth Science Informatics, 16, 1687–1700. doi: 10.1007/s12145-023-01005-8
Zhao, C.X., Jia, L.H., Wang, Y.F., Wang, M. L., McGiffen, Jr.M.E. (2015). Effects of Different Soil Texture on Peanut Growth and Development and Development. Communications in Soil Science and Plant Analysis, 46, 2249–2257. doi: 10.1080/00103624.2015.1059845.
مدل سازی و مدیریت آب و خاک
دوره 6، Special Issue
Guest Editor: Dr. Raoof Mostafazadeh
خرداد 1405
صفحه 287-302

فایل ها

سابقه مقاله

  • تاریخ دریافت: 11 دی 1404
  • تاریخ بازنگری: 27 فروردین 1405
  • تاریخ پذیرش: 28 فروردین 1405

هم رسانی

ارجاع به این مقاله

آمار