Document Type : Research Paper
Authors
1 Department of Soil Science, Faculty of Agriculture, Lorestan University, Iran
2 Department of Soil Science, Faculty of Agriculture, University of Tehran, Iran
10.22059/jrwm.2025.386586.1792
Abstract
The scale of environmental variables is one of the most important features to consider when selecting data. The aim of this study is to improve the accuracy of digital mapping by selecting the optimal scale for predicting six soil properties, For this purpose, 100 surface soil samples (0-30 cm depth) were collected based on a random sampling pattern. Environmental variables related to topography and remote sensing were extracted from the digital elevation model (DEM) and Landsat-8 satellite. The optimal environmental variables were selected using the recursive feature elimination method in the Silakhor Plain region. Soil property modeling was conducted using machine learning models such as random forest (RF), Support Vector Regression (SVR), Cubist (CB), and hybrid modeling. The modeling results showed that the RF model performed best for predicting CCE, pH, sand, and silt with R² values of 0.64, 0.65, 0.59, and 0.70, respectively. Additionally, the SVR model showed the highest accuracy for predicting SOC with an R² of 0.62, while the CB model had the highest accuracy for predicting clay with an R² of 0.66. The most suitable cell sizes for CCE, pH, sand, and silt were identified as 30*30 m, for SOC as 60*60m, and for clay as 90*90m. The most important environmental variables for SOC, pH, silt, sand, and clay were valley depth, differential vegetation index, and modified vegetation index, respectively. Overall, the results indicated that in the study areas, the use of intermediate scales (cell sizes of 30 to 90 m) led to higher accuracy in predicting soil properties. This is because using larger cell sizes introduces noise that hinders accuracy.
Keywords
Amirian Chekan, A., Taghizadeh-Mehrjardi, R., Sarmadian, F., & Heydari, A. (2017). Three-dimensional mapping of soil texture using spline depth functions and artificial neural networks. Iranian Journal of Soil and Water Research.48(1),113-123. 10.22059/IJSWR.2017.61346. (In Persian).
Adeniyi, O. D., & Maerker, M. (2024). Explorative analysis of varying spatial resolutions on a soil type classification model and it's transferability in an agricultural lowland area of Lombardy, Italy. Geoderma Regional, 37, e00785. https://doi.org/10.1016/j.geodrs.2024.e00785.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. http://dx.doi.org/10.1023/A:1010933404324.
Behrens, T., Schmidt, K., Zhu, A. X., & Scholten, T. (2010). The ConMap approach for terrain‐based digital soil mapping. European Journal of Soil Science, 61(1), 133-143. https://doi.org/10.1111/j.1365-2389.2009.01205.x.
Behrens, T., Schmidt, K., MacMillan, R. A., & Viscarra Rossel, R. A. (2018). Multi-scale digital soil mapping with deep learning. Scientific reports, 8(1), 15244. https://doi.org/10.1038/s41598-018-33516-6.
Cavazzi, S., Corstanje, R., Mayr, T., Hannam, J., & Fealy, R. (2013). Are fine resolution digital elevation models always the best choice in digital soil mapping? Geoderma, 195, 111-121. https://doi.org/10.1016/j.geoderma.2012.11.020.
Chutia, D., Bhattacharyya, D. K., Sarma, J., & Raju, P. N. L. (2017). An effective ensemble classification framework using random forests and a correlation-based feature selection technique. Transactions in GIS, 21(6), 1165-1178. https://doi.org/10.1111/tgis.12268.
Costa, E. M., Samuel-Rosa, A., & Anjos, L. H. C. D. (2018). Digital elevation model quality on digital soil mapping prediction accuracy. Ciência e Agrotecnologia, 42(6), 608-622. https://doi.org/10.1590/1413-70542018426027418.
Chabrillat, S., Ben-Dor, E., Cierniewski, J., Gomez, C., Schmid, T., & van Wesemael, B. (2019). Imaging spectroscopy for soil mapping and monitoring. Surveys in Geophysics, 40, 361-399. https://doi.org/10.1007/s10712-019-09524-0.
Chen, S., Lin, B., Li, Y., & Zhou, S. (2020). Spatial and temporal changes of soil properties and soil fertility evaluation in a large grain-production area of subtropical plain, China. Geoderma, 357, 113937. https://doi.org/10.1016/j.geoderma.2019.113937.
Casa, R., Pignatti, S., Pascucci, S., Huang, W., & Pepe, M. (2020, September). Effect of spatial resolution on soil properties retrieval from imaging spectroscopy: an assessment of the hyperspectral CHIME mission potential. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium (pp. 4906-4909). https://doi.org/ 10.1109/IGARSS39084.2020.9323268
Chen, S., Arrouays, D., Mulder, V. L., Poggio, L., Minasny, B., Roudier, P., ... & Walter, C. (2022). Digital mapping of GlobalSoilMap soil properties at a broad scale: A review. Geoderma, 409, 115567. https://doi.org/10.1016/j.geoderma.2021.115567.
Charman, P.E.V., & Roper, M.M. (2007). Soil Organic Matter. In: Charman, P.E.V. and Murphy, B.W., Eds., Soils—Their Properties and Management, 3rd Edition, Oxford University Press, Melbourne, 276-285.
Burges, C. J., & Schölkopf, B. (1996). Improving the accuracy and speed of support vector machines. Advances in neural information processing systems, 9. (NIPS 1996).
Dharumarajan, S., Hegde, R., & Singh, S. K. (2017). Spatial prediction of major soil properties using Random Forest techniques-A case study in semi-arid tropics of South India. Geoderma Regional, 10, 154-162. https://doi.org/10.1016/j.geodrs.2017.07.005.
Dornik, A., Cheţan, M. A., Drăguţ, L., Dicu, D. D., & Iliuţă, A. (2022). Optimal scaling of predictors for digital mapping of soil properties. Geoderma, 405, 115453. https://doi.org/10.1016/j.geoderma.2021.115453.
Eskandari, S., nabiollahi, K. and Taghizade-Mehrjardi, R. (2018). Digital Mapping of Soil Organic Carbon (Case Study: Marivan, Kurdistan Province). Water and Soil, 32(4), 737-750. doi: 10.22067/jsw.v32i4.68318. (In Persian).
Esmailpour, S., Mahmoudabadi, E., Ganjehie, M. G., & Karimi, A. (2024). Different pixel sizes of topographic data for prediction of soil salinity. PloS one, 19(12), e0315807. https://doi.org/10.1371/journal.pone.0315807.
Farina, Almo. (2022). "Human-Dependent Landscapes Around the World–An Ecological Perspective." In Principles and Methods in Landscape Ecology: An Agenda for the Second Millennium, pp. 339-399. Cham: Springer International Publishing, 2022.
Gee, G. W., & Bauder, J. W. (1986). Particle-size analysis 1. Methods of soil analysis: part 1—physical and mineralogical methods, (methodsofsoilan1).
Guo, L., Shi, T., Linderman, M., Chen, Y., Zhang, H., & Fu, P. (2019). Exploring the influence of spatial resolution on the digital mapping of soil organic carbon by airborne hyperspectral VNIR imaging. Remote Sensing, 11(9), 1032. https://doi.org/10.3390/rs11091032.
Jackson, M. L. (1969). Soil chemical analysis-advanced course. Prentice Hall Inc. Osmania University.521 pages.
John, K., Afu, S. M., Isong, I. A., Aki, E. E., Kebonye, N. M., Ayito, E. O., ... & Penížek, V. (2021). Mapping soil properties with soil-environmental covariates using geostatistics and multivariate statistics. International Journal of Environmental Science and Technology, 1-16. https://doi.org/10.1007/s13762-020-03089-x.
Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling, 4614-6849. Springer New York, NY. https://doi.org/10.1007/978-1-4614-6849-3.
Keskin, H., & Grunwald, S. (2018). Regression kriging as a workhorse in the digital soil mapper's toolbox. Geoderma, 326, 22-41. https://doi.org/10.1016/j.geoderma.2018.04.004.
Kingsley, J., Lawani, S. O., Esther, A. O., Ndiye, K. M., Sunday, O. J., & Penížek, V. (2019). Predictive mapping of soil properties for precision agriculture using geographic information system (GIS) based geostatistics models. Modern Applied Science, 13(10), 60-77. https://doi.org/10.5539/mas.v13n10p60
Khaledian, Y., & Miller, B. A. (2020). Selecting appropriate machine learning methods for digital soil mapping. Applied Mathematical Modelling, 81, 401-418. https://doi.org/10.1016/j.apm.2019.12.016.
Khosravani, P., Baghernejad, M., Taghizadeh-Mehrjardi, R., Mousavi, S. R., Moosavi, A. A., Fallah Shamsi, S. R., ... & Scholten, T. (2024). Assessing the role of environmental covariates and pixel size in soil property prediction: a comparative study of various areas in Southwest Iran. Land, 13(8), 1309. https://doi.org/10.3390/land13081309
Lacoste, M., Minasny, B., McBratney, A., Michot, D., Viaud, V., & Walter, C. (2014). High resolution 3D mapping of soil organic carbon in a heterogeneous agricultural landscape. Geoderma, 213, 296-311. https://doi.org/10.1016/j.geoderma.2013.07.002.
Levin, G. (2006). Computer vision for artists and designers: pedagogic tools and techniques for novice programmers. AI & SOCIETY, 20, 462-482. https://doi.org/10.1007/s00146-006-0049-2.
Lagacherie, P., McBratney, A., & Voltz, M. (2006). Digital soil mapping: an introductory perspective. Elsevier publisher.
Lianheng, Z., Shuaihao, Z., Dongliang, H., Shi, Z., & Dejian, L. (2018). Quantitative characterization of joint roughness based on semivariogram parameters. International Journal of Rock Mechanics and Mining Sciences, 109, 1-8. https://doi.org/10.1016/j.ijrmms.2018.06.008
Li, S., Xu, L., Jing, Y., Yin, H., Li, X., & Guan, X. (2021). High-quality vegetation index product generation: A review of NDVI time series reconstruction techniques. International Journal of Applied Earth Observation and Geoinformation, 105, 102640. https://doi.org/10.1016/j.jag.2021.102640.
Masoudi, R., Mousavi, S. R., Rahimabadi, P. D., Panahi, M., & Rahmani, A. (2023). Assessing data mining algorithms to predict the quality of groundwater resources for determining irrigation hazard. Environmental monitoring and assessment, 195(2), 319. https://doi.org/10.1007/s10661-022-10909-9.
McLean, E. O. (1982). Soil pH and lime requirement." Methods of soil analysis: Part 2 Chemical and microbiological properties.
Malone, B. P., Styc, Q., Minasny, B., & McBratney, A. B. (2017). Digital soil mapping of soil carbon at the farm scale: A spatial downscaling approach in consideration of measured and uncertain data. Geoderma, 290, 91-99. https://doi.org/10.1016/j.geoderma.2016.12.008.
Mehrabi Gouhori, E., Matinfar, H.R., Taghizadeh-Mehrjardi, R., & Jafari, A. (2019). Investigating the effect of wetness index and spectral data in estimating the percentage of soil particles using neurophasic models, artificial neural network and tree regression. Scientific Research Journal of Irrigation and Water Engineering of Iran. 10(2), 126-105. (In Persian).
Mahmoudzadeh, H., Matinfar, H. R., Taghizadeh-Mehrjardi, R., & Kerry, R. (2020). Spatial prediction of soil organic carbon using machine learning techniques in western Iran. Geoderma Regional, 21, e00260. https://doi.org/ 10.1016/j.geodrs.2020.e00260
Mammadov, E., Nowosad, J., & Glaesser, C. (2021). Estimation and mapping of surface soil properties in the Caucasus Mountains, Azerbaijan using high-resolution remote sensing data. Geoderma Regional, 26, e00411. https://doi.org/10.1016/j.geodrs.2021.e00411.
Matinfar, H. R., Maghsodi, Z., Mousavi, S. R., & Rahmani, A. (2021). Evaluation and Prediction of Topsoil organic carbon using Machine learning and hybrid models at a Field-scale. Catena, 202, 105258. https://doi.org/10.1016/j.catena.2021.105258.
Mousavi, S. R., Sarmadian, F., Omid, M., & Bogaert, P. (2022). Three-dimensional mapping of soil organic carbon using soil and environmental covariates in an arid and semi-arid region of Iran. Measurement, 201, 111706. https://doi.org/10.1016/j.measurement.2022.111706
Macharia, P. M., Ray, N., Gitonga, C. W., Snow, R. W., & Giorgi, E. (2022). Combining school-catchment area models with geostatistical models for analysing school survey data from low-resource settings: Inferential benefits and limitations. Spatial statistics, 51, 100679. https://doi.org/10.1016/j.spasta.2022.100679.
Mattila, T. J., Hagelberg, E., Söderlund, S., & Joona, J. (2022). How farmers approach soil carbon sequestration? Lessons learned from 105 carbon-farming plans. Soil and Tillage Research, 215, 105204. https://doi.org/10.1016/j.still.2021.105204.
Nelson, D. W., & Sommers, L. E. (1980). Total nitrogen analysis of soil and plant tissues. Journal of the Association of Official Analytical Chemists, 63(4), 770-778.
Pahlavan-Rad, M. R., & Akbarimoghaddam, A. (2018). Spatial variability of soil texture fractions and pH in a flood plain (case study from eastern Iran). Catena, 160, 275-281. https://doi.org/10.1016/j.catena.2017.10.002.
Padarian, J., Minasny, B., & McBratney, A. B. (2019). Using deep learning for digital soil mapping. Soil, 5(1), 79-89. https://doi.org/10.5194/soil-5-79-2019.
Paul, G. C., Saha, S., & Ghosh, K. G. (2020). Assessing the soil quality of Bansloi river basin, eastern India using soil-quality indices (SQIs) and Random Forest machine learning technique. Ecological Indicators, 118, 106804. https://doi.org/ 10.1016/j.ecolind.2020.106804.
Parsaie, F., Farrokhian Firouzi, A., Mousavi, S. R., Rahmani, A., Sedri, M. H., & Homaee, M. (2021). Large-scale digital mapping of topsoil total nitrogen using machine learning models and associated uncertainty map. Environmental Monitoring and Assessment, 193, 1-15. https://doi.org/ 10.1007/s10661-021-08947-w.
Rossel, R. V., & McBratney, A. B. (2008). Diffuse reflectance spectroscopy as a tool for digital soil mapping. In Digital soil mapping with limited data, Springer, Dordrecht. 165-172. Netherlands. https://doi.org/10.1007/978-1-4020-8592-5_13.
Shahriari, M., Delbari, M., Afrasiab, P., & Pahlavan-Rad, M. R. (2019). Predicting regional spatial distribution of soil texture in floodplains using remote sensing data: A case of southeastern Iran. Catena, 182, 104149. https://doi.org/10.1016/j.catena.2019.104149.
Sahu, B., & Ghosh, A. K. (2021). Deterministic and geostatistical models for predicting soil organic carbon in a 60-ha farm on Inceptisol in Varanasi, India. Geoderma Regional, 26, e00413. https://doi.org/10.1016/j.geodrs.2021.e00413.
Saurette, D. D., Berg, A. A., Laamrani, A., Heck, R. J., Gillespie, A. W., Voroney, P., & Biswas, A. (2022). Effects of sample size and covariate resolution on field-scale predictive digital mapping of soil carbon. Geoderma, 425, 116054. https://doi.org/10.1016/j.geoderma.2022.116054.
Su, L., Heydari, M., Jaafarzadeh, M. S., Mousavi, S. R., Rezaei, M., Fathizad, H., & Heung, B. (2024). Incorporating forest canopy openness and environmental covariates in predicting soil organic carbon in oak forest. Soil and Tillage Research, 244, 106220. https://doi.org/10.1016/j.still.2024.106220.
de Sousa, L. M., Poggio, L., Batjes, N. H., Heuvelink, G. B., Kempen, B., Riberio, E., & Rossiter, D. (2020). SoilGrids 2.0: producing quality-assessed soil information for the globe. Soil discussions, 2020, 1-37. https://doi.org/10.5194/soil-7-217-2021.
Taghizadeh-Mehrjardi, R., & Nabiollahi, K. (2016). Determining the most suitable pixel resolution and window size of the height digital model to determine the spatial distribution of soil clay percentage. Soil research (soil and water sciences).30 (1),95-104. https://doi.org/ 20.1001.1.22287124.1395.30.1.9.4. (In Persian).
Wilding, L. P. (1985). Spatial variability: its documentation, accommodation and implication to soil surveys. Conference paper: Soil spatial variability, 1985, 166-189 ref. 15.
Wang, L., & Liu, H. (2006). An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling. International Journal of Geographical Information Science, 20(2), 193-213. https://doi.org/10.1080/13658810500433453.
Wang, Z., & Shi, W. (2017). Mapping soil particle-size fractions: A comparison of compositional kriging and log-ratio kriging. Journal of Hydrology, 546, 526-541. https://doi.org/ 10.1016/j.jhydrol.2017.01.029.
Yuan, Y., Miao, Y., Yuan, F., Ata-UI-Karim, S. T., Liu, X., Tian, Y., ... & Cao, Q. (2022). Delineating soil nutrient management zones based on optimal sampling interval in medium-and small-scale intensive farming systems. Precision Agriculture, 1-21. https://doi.org/10.1007/s11119-021-09848-1.
Zeraatpisheh, M., Ayoubi, S., Jafari, A., Tajik, S., & Finke, P. (2019). Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran. Geoderma, 338, 445-452. https://doi.org/10.1016/j.geoderma.2018.09.006.
Zeraatpisheh, M., Jafari, A., Bodaghabadi, M. B., Ayoubi, S., Taghizadeh-Mehrjardi, R., Toomanian, N., ... & Xu, M. (2020). Conventional and digital soil mapping in Iran: Past, present, and future. Catena, 188, 104424. https://doi.org/10.1016/j.catena.2019.104424.
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