To build any spatial soil database, a set of environmental data including digital elevation model (DEM) and satellite images beside geomorphic landscape description are essentials. Such a database, integrates field observations and laboratory analyses data with the results obtained from qualitative and quantitative models. So far, various techniques have been developed for soil data processing. The performance of Artificial Neural Network (ANN) and Decision Tree (DT) models was compared to map out some soil attributes in Alborz Province, Iran. Terrain attributes derived from a DEM along with Landsat 8 ETM+, geomorphology map, and the routine laboratory analyses of the studied area were used as input data. The relationships between soil properties (including sand, silt, clay, electrical conductivity, organic carbon, and carbonates) and the environmental variables were assessed using the Pearson Correlation Coefficient and Principle Components Analysis. Slope, elevation, geomforms, carbonate index, stream network, wetness index, and the band’s number 2, 3, 4, and 5 were the most significantly correlated variables. ANN and DT did not show the same accuracy in predicting all parameters. The DT model showed higher performances in estimating sand (R2=0.73), silt (R2=0.70), clay (R2=0.72), organic carbon (R2=0.71), and carbonates (R2=0.70). While the ANN model only showed higher performance in predicting soil electrical conductivity (R2=0.95). The results showed that determination the best model to use, is dependent upon the relation between the considered soil properties with the environmental variables. However, the DT model showed more reasonable results than the ANN model in this study. The results showed that before using a certain model to predict variability of all soil parameters, it would be better to evaluate the efficiency of all possible models for choosing the best fitted model for each property. In other words, most of the developed models are site-specific and may not be applicable to use for predicting other soil properties or other area.
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Akramkhanov A, Vlek PL (2012) The assessment of spatial distribution of soil salinity risk using neural network. Environmental Monitoring and Assessment 184(4): 2475–2485. https://doi.org/10.1007/s10661-011-2132-5
Akumu CE, Johnson JA, Etheridge D, et al. (2015) GIS-fuzzy logic based approach in modeling soil texture: using parts of the Clay Belt and Hornepayne region in Ontario Canada as a case study. Geoderma 239-240: 13–24. https://doi.org/10.1016/j.geoderma.2014.09.021
Alavi Panah SK (2000) Investigation and evaluation of the use of the soil salinity map, Journal of Desert 5: 1–15.
Ali I, Greifeneder F, Stamenkovic J, et al. (2015) Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data. Remote Sensing 7(12): 16398–16421. https://doi.org/10.3390/rs71215841
Anagu I, Ingwersen J, Utermann J, et al. (2009) Estimation of heavy metal sorption in German soils using artificial neural networks. Geoderma 152(1-2): 104–112. https://doi.org/10.1016/j.geoderma.2009.06.004
Batjes NH (2008) Mapping soil carbon stocks of Central Africa using SOTER. Geoderma 146(1-2): 58–65. https://doi.org/10.1016/j.geoderma.2008.05.006
Behrens T, Scholten T (2006) A comparison of data-mining techniques in predictive soil mapping. Developments in Soil Science 31: 353–617. https://doi.org/10.1016/S0166-2481(06)31025-2
Beucher A, Møller AB, Greve MH (2017) Artificial neural networks and decision tree classification for predicting soil drainage classes in Denmark. Geoderma 320: 30–42. https://doi.org/10.1016/j.geoderma.2017.11.004
Bagheri Bodaghabadi M, Martínez-Casasnovas JA, Borujeni IE, et al. (2016) Database extension for digital soil mapping using artificial neural networks. Arabian Journal of Geosciences 9(18): 701. https://doi.org/10.1007/s12517-016-2732-z
Bagheri Bodaghabadi M, Martínez-Casasnovas J, et al. (2015) Digital soil mapping using artificial neural networks and terrainrelated attributes. Pedosphere 25(4): 580–591. https://doi.org/10.1016/S1002-0160(15)30038-2
Bagheri Bodaghabadi M, Salehi MH, Martínez-Casasnovas JA, et al. (2011) Using Canonical Correspondence Analysis (CCA) to identify the most important DEM attributes for digital soil mapping applications. Catena 86: 66–74. https://doi.org/10.1016/j.catena.2011.02
Boettinger JL, Ramsey RD, Bodily JM, et al. (2008) Landsat spectral data for digital soil mapping. In Digital soil mapping with limited data. Springer, Dordrecht: 193–202. https://doi.org/10.1007/978-1-4020-8592-5_16
Breiman L, Friedman J, Olshen R, et al (1984) Classification and Regression Trees. Belmont, CA: Wadsworth International Group 40(3): 874. https://doi.org/10.2307/2530946
Brevik EC, Calzolari C, Miller BA, et al. (2016) Soil mapping, classification, and pedologic modeling: History and future directions. Geoderma 264: 256–274. https://doi.org/10.1016/j.geoderma.2015.05.017
Bui LV, Stahr K, Clemens G (2017) A fuzzy logic slope-form system for predictive soil mapping of a landscape-scale area with strong relief conditions. Catena 155: 135–146. https://doi.org/10.1016/j.catena.2017.03.001
Bünemann E K, Bongiorno G, Bai Z, et al. (2018) Soil qualitya critical review. Soil Biology and Biochemistry 120: 105–125. https://doi.org/10.1016/j.soilbio.2018.01.030
Costantino F, Di Gravio G, Nonino F (2015) Project selection in project portfolio management: An artificial neural network model based on critical success factors. International Journal of Project Management 33(8): 1744–1754. https://doi.org/10.1016/j.ijproman.2015.07.003
Dai F, Zhou Q, Lv Z, et al. (2014) Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau. Ecological Indicators 45: 184–194. https://doi.org/10.1016/j.ecolind.2014.04.003
Danesh M, Hosin Ali B, Alavi Panah SK, et al (2009) Simultaneous Geometric analysis of soil carbonate and particle diameters using remote sensing data (Case Study: Southwest of Lorestan, Pole dokhtar). Iranian Journal of Geology: 25–36, (publish in Persian).
Debella-Gilo M, Etzelmüller B (2009) Spatial prediction of soil classes using digital terrain analysis and multinomial logistic regression modeling integrated in GIS: Examples from Vestfold County, Norway. Catena 77(1): 8–18. https://doi.org/10.1016/j.catena.2008.12.001
Dolati P, Heidari A (2016) Physiographic structure of Alborz Province and its relationship with geopedologic properties. Master thesis, University of Tehran. (In Persian).
Drzewiecki W, Wezyk P, Pierzchalski M, et al. (2014). Quantitative and qualitative assessment of soil erosion risk in Małopolska (Poland), supported by an object-based analysis of high-resolution satellite images. Pure and Applied Geophysics 171(6): 867–895. https://doi.org/10.1007/s00024-013-0669-7
Du F, Zhu AX, Band L, et al. (2015) Soil property variation mapping through data mining of soil category maps. Hydrological processes 29(11): 2491–2503. https://doi.org/10.1002/hyp.10383
Fatehi SH, Mohamadi J, Salehi MH et al. (2016) Downscaling digital maps of some soil surface properties (Case Study: Merck Watershed, Kermanshah Province). Journal of Water and Soil Conservation Studies 23: 23–43. (In Persian).
Gallant JC, Dowling TI (2003) A multiresolution index of valley bottom flatness for mapping depositional areas, Water Resources Research 39(12): 1347–1359. https://doi.org/10.1029/2002WR001426
Giasson E, Figueiredo SR, Tornquist CG, et al. (2008) Digital soil mapping using logistic regression on terrain parameters for several ecological regions in Southern Brazil. Digital soil mapping with limited data, Springer. pp 225–232. https://doi.org/10.1007/978-1-4020-8592-5_19
Grinand C, Arrouays D, Laroche B, et al. (2008) Extrapolating regional soil landscapes from an existing soil map: sampling intensity, validation procedures, and integration of spatial context. Geoderma 143(1-2): 180–190. https://doi.org/10.1016/j.geoderma.2007.11.004
Hengl T, Rossiter DG, Stein A (2004) Soil sampling strategies for spatial prediction by correlation with auxiliary maps. Soil Research 41(8): 1403–1422. https://doi.org/10.1071/SR03005
Hertz JA (2018) Introduction to the theory of neural computation. CRC Press. https://doi.org/10.1201/9780429499661
Heung B, Bulmer CE, Schmidt MG (2014) Predictive soil parent material mapping at a regional-scale: A Random Forest approach. Geoderma 241: 141–154. https://doi.org/10.1016/j.geoderma.2013.09.016
Heung B, Ho HC, Zhang J, et al. (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
Huang X, Senthilkumar S, Kravchenko A, et al. (2007) Total carbon mapping in glacial till soils using near-infrared spectroscopy, Landsat imagery and topographical information. Geoderma 141(1-2): 34–42. https://doi.org/10.1016/j.geoderma.2007.04.023
Jafari A, Finke PA, de Wauw JV, et al. (2012) Spatial prediction of USDA- great soil groups in the arid Zarand region, Iran: comparing logistic regression approaches to predict diagnostic horizons and soil types. European Journal of Soil Science 63(2): 284–298. https://doi.org/10.1111/j.1365-2389.2012.01425.x
Jafari A, Khademi H, Finke PA, et al. (2014) Spatial prediction of soil great groups by boosted regression trees using a limited point dataset in an arid region, southeastern Iran. Geoderma 232: 148–163. https://doi.org/10.1016/j.geoderma.2014.04.029
Kalambukattu JG, Kumar S, Raj RA (2018) Digital soil mapping in a Himalayan watershed using remote sensing and terrain parameters employing artificial neural network model. Environmental Earth Sciences 77(5): 203. https://doi.org/10.1007/s12665-018-7367-9
Kheir RB, Greve MH, Bøcher PK, et al. (2010) Predictive mapping of soil organic carbon in wet cultivated lands using classification-tree based models: The case study of Denmark. Journal of Environmental Management 91(5): 1150–1160. https://doi.org/10.1016/j.jenvman.2010.01.001
Khodadadi M, Sarmadian F, Refahi H, et al. (2009) Saline and alkaline soil mapping using ASTER data in the Qazvin plain. Journal of the Iranian Natural Resources 61: 1143–1156 (In Persian).
Li Q, Yue TX, Wang CQ, et al. (2013) Spatially distributed modeling of soil organic matter across China: an application of artificial neural network approach. Catena 104: 210–218. https://doi.org/10.1016/j.catena.2012.11.012
Luoto M. and Hjort J (2005) Evaluation of current statistical approaches for predictive geomorphological mapping, Geomorphology 67(3-4): 299–315. https://doi.org/10.1016/j.geomorph.2004.10.006
Ma Y, Minasny B, Wu C (2017) Mapping key soil properties to support agricultural production in Eastern China. Geoderma Regional 10: 144–153. https://doi.org/10.1016/j.geodrs.2017.06.002
Mahmoudabadi E, Karimi A, Haghnia GH, et al. (2017) Digital soil mapping using remote sensing indices, terrain attributes, and vegetation features in the rangelands of northeastern Iran. Environmental monitoring and assessment 189(10): 500. https://doi.org/10.1007/s10661-017-6197-7
Malone BP, McBratney AB, Minasny B et al. (2009) Mapping continuous depth functions of soil carbon storage and available water capacity. Geoderma 154(1-2): 138–152. https://doi.org/10.1016/j.geoderma.2009.10.007
Malone BP, Minasny B, McBratney AB (2017) Using digital soil mapping to update, harmonize and disaggregate legacy soil maps. In Using R for Digital Soil Mapping, Springer. pp 221–230. https://doi.org/10.1007/978-3-319-44327-0_8
Mansuy N, Thiffault E, Paré D, et al. (2014) Digital mapping of soil properties in Canadian managed forests at 250 m of resolution using the k-nearest neighbor method. Geoderma 235: 59–73. https://doi.org/10.1016/j.geoderma.2014.06.032
Martínez-Murillo JF, Hueso-González P, Ruiz-Sinoga JD (2017) Topsoil moisture mapping using geostatistical techniques under different Mediterranean climatic conditions. Science of the Total Environment 595: 400–412. https://doi.org/10.1016/j.scitotenv.2017.03.291
Masbah Zadeh T, Ahmadi H, Sarmadian F, et al. (2015) Soil Surface salinity mapping using Landsat Satellit images (Case Study: Boein Zahra). Range and Watershed Management 67(4): 631–640. (In Persian).
McBratney AB, Santos MM, Minasny B (2003) On digital soil mapping. Geoderma 117(1-2): 3–52. https://doi.org/10.1016/S0016-7061(03)00223-4
Metternicht G, Zinck JA (2003) Remote sensing of soil salinity: potentials and constraints. Remote Sens. Environment 85(1): 1–20. https://doi.org/10.1016/S0034-4257(02)00188-8
Minasny B, and McBratney AB (2016) Digital soil mapping: A brief history and some lessons. Geoderma 264: 301–311. https://doi.org/10.1016/j.geoderma.2015.07.017
Mondal A, Khare D, Kundu S et al. (2017) Spatial soil organic carbon (SOC) prediction by regression kriging using remote sensing data. The Egyptian Journal of Remote Sensing and Space Science 20(1): 61–70. https://doi.org/10.1016/j.ejrs.2016.06.004
Moonjun R, Farshad A, Shrestha DP et al (2010) Artificial neural network and decision tree in predictive soil mapping of Hoi Num Rin sub-watershed, Thailand. Digital Soil Mapping: 151–164. https://doi.org/10.1007/978-90-481-8863-5_13
Mosleh Z, Salehi MH, Jafari A, et al. (2016) The effectiveness of digital soil mapping to predict soil properties over low-relief areas. Environmental monitoring and assessment 188(3): 195. https://doi.org/10.1007/s10661-016-5204-8
Nabiollahi K, Haidari A, Taghizadeh MR (2014) Digital Mapping of Soil Texture Using Regression Tree and Artificial Neural Network in Bijar, Kurdistank 28(5): 1025–1036.
Nield SJ, Boettinger JL, Ramsey RD (2007) Digitally mapping gypsic and natric soil areas using Landsat ETM data. Soil Science Society of America Journal 71(1): 245–252. https://doi.org/10.2136/sssaj2006-0049
Okin GS, aPainter TH (2004) Effect of grain size on remotely sensed spectral reflectance of sandy desert surfaces. Remote Sensing of Environment 89(3): 272–280. https://doi.org/10.1016/j.rse.2003.10.008
Oldeman LR, Hakkeling RTA, Sombroek WG (2017) World map of the status of human-induced soil degradation: an explanatory note. http://wedocs.unep.org/bitstream/handle/20.500.11822/19660/ExplanNote_1.pdf?sequence=1. https://doi.org/10.1081/e-ess3-120041260
Otto JC, Smith MJ (2013) Geomorphological Techniques. Geomorphological mapping. Chap. 2., Sec. 6., British Society of Geomorphology.
Pahlavan-Rad MR, Khormali F, Toomanian N, et al. (2016) Legacy soil maps as a covariate in digital soil mapping: A case study from Northern Iran. Geoderma 279: 141–148. https://doi.org/10.1016/j.geoderma.2016.05.014
Pinto LC, de Mello CR, Norton LD, et al. (2016) Spatial prediction of soilwater transmissivity based on fuzzy logic in a Brazilian headwater watershed 143: 26–34. https://doi.org/10.1016/j.catena.2016.03.033
Pradhan B, and Jebur MN (2017) Spatial Prediction of Landslide-Prone Areas Through k-Nearest Neighbor Algorithm and Logistic Regression Model Using High Resolution Airborne Laser Scanning Data. In Laser Scanning Applications in Landslide Assessment, Springer. pp 151–165. https://doi.org/10.1007/978-3-319-55342-9_8
Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers and Geosciences 51: 350–365. https://doi.org/10.1016/j.cageo.2012.08.023
Rossel RV, Walvoort DJJ, McBratney AB, et al. (2006) Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131(1-2): 59–75. https://doi.org/10.1016/j.geoderma.2005.03.007
Rossiter DG (2016) Knowledge is power: where geopedologic insights are necessary for predictive digital soil mapping. Geopedology, Springer. pp 227–237 https://doi.org/10.1007/978-3-319-19159-1_13
Rouse JW, Hass RH, Schell JA, et al. (1973) Monitoring vegetation systems in the Great Plains with ERTS. Third Earth resources Tech 1: 309–317.
Schoeneberger PJ, Wysocki DA (2012) Geomorphic description system, version 3.3. USDA-NRCS, National Soil Survey Center, Lincoln, NE
Silva SH, de Menezes MD, Owens PR, et al. (2016) Retrieving penologist’s mental model from existing soil map and comparing data mining tools for refining a larger area map under similar environmental conditions in Southeastern Brazil. Geoderma 267: 65–77. https://doi.org/10.1016/j.geoderma.2015.12.025
Sommer M, Gerke HH, Deumlich D (2008) Modelling soil landscape genesis—a “time split” approach for hummocky agricultural landscapes. Geoderma 145(3-4): 480–493. https://doi.org/10.1016/j.geoderma.2008.01.012
Sun W, Zhu H, Guo S (2015). Soil organic carbon as a function of land use and topography on the Loess Plateau of China. Ecological Engineering 83: 249–257. https://doi.org/10.1016/j.ecoleng.2015.06.030
Taghizadeh-Mehrjardi R, Minasny B, Sarmadian F, et al. (2014) Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma 213: 15–28. https://doi.org/10.1016/j.geoderma.2013.07.020
Taghizadeh-Mehrjardi R, Nabiollahi K, Kerry R (2016) Digital mapping of soil organic carbon at multiple depths using different data mining techniques in Baneh region, Iran. Geoderma 266: 98–110. https://doi.org/10.1016/j.geoderma.2015.12.003
Taghizadeh-Mehrjardi R, Nabiollahi K, Minasny B, et al. (2015) Comparing data mining classifiers to predict spatial distribution of USDA-family soil groups in Baneh region, Iran. Geoderma 266: 67–77. https://doi.org/10.1016/j.geoderma.2015.12.003
Tziachris P, Metaxa E, Papadopoulos F, et al. (2017). Spatial modelling and prediction assessment of soil iron using kriging interpolation with pH as auxiliary information. ISPRS International Journal of Geo-Information 6(9): 283. https://doi.org/10.3390/ijgi6090283
Vågen TG, Winowiecki LA, Tondoh JE, et al. (2016) Mapping of soil properties and land degradation risk in Africa using MODIS reflectance. Geoderma 263: 216–225. https://doi.org/10.1016/j.geoderma.2015.06.023
Vaysse K, Lagacherie P (2015) Evaluating digital soil mapping approaches for mapping GlobalSoilMap soil properties from legacy data in Languedoc-Roussillon (France). Geoderma Regional 4: 20–30. https://doi.org/10.1016/j.geodrs.2014.11.003
Wang Q, Wu B, Stein A, et al. (2018) Soil depth spatial prediction by fuzzy soil-landscape model. Journal of Soils and Sediments 18(3):1041-1051. https://doi.org/10.1007/s11368-017-1779-0
Wang S, Wang X, Ouyang Z (2012) Effects of land use, climate, topography and soil properties on regional soil organic carbon and total nitrogen in the Upstream Watershed of Miyun Reservoir, North China. Journal of Environmental Sciences 24(3): 387–395. https://doi.org/10.1016/S1001-0742(11)60789-4
Were K, Bui DT, Dick ØB, et al. (2015) A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators 52: 394–403. https://doi.org/10.1016/j.ecolind.2014.12.028
Yang RM, Zhang GL, Liu F, et al. (2016) Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecological indicators 60: 870–878. https://doi.org/10.1016/j.ecolind.2015.08.036
Yang S, Feng Q, Liang T, et al. (2018) Modeling grassland aboveground biomass based on artificial neural network and remote sensing in the Three-River Headwaters Region. Remote Sensing of Environment 204: 448–455. https://doi.org/10.1016/j.rse.2017.10.011
Zhao Z, Chow TL, Rees HW, et al. (2009) Predict soil texture distributions using an artificial neural network model. Computers and electronics in agriculture 65(1): 36–48. https://doi.org/10.1016/j.compag.2008.07.008
Zhou G, Shi Y, Zhang R, et al. (2014) Co-location decision tree model for extracting exposed carbonate rocks in karst rocky desertification area. In Land Surface Remote Sensing II. International Society for Optics and Photonics 9260: 92600W. https://doi.org/10.1117/12.2066091
Zinck JA, Metternicht G, Bocco G, et al. (2016) Geopedology: An Integration of Geomorphology and Pedology for Soil and Landscape Studies. Springer. https://doi.org/10.1007/978-3-319-19159-1
Zolfaghari AA, Taghizadeh-Mehrjardi R, Moshki AR et al. (2016). Using the nonparametric knearest neighbor approach for predicting cation exchange capacity. Geoderma 265: 111–119. https://doi.org/10.1016/j.geoderma.2015.11.012
Zornoza R, Acosta JA, Bastida F, et al. (2015) Identification of sensitive indicators to assess the interrelationship between soil quality, management practices and human health. Soil 1(1) 173–185. https://doi.org/10.5194/soil-1-173-2015
Zurada JM (1992) Introduction to artificial neural systems. St. Paul: West publishing company 8.
We sincerely appreciate College of Agriculture and Natural Resources, University of Tehran for financial support of the study (Grant No. 7104017/6/24 and 28).
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Hateffard, F., Dolati, P., Heidari, A. et al. Assessing the performance of decision tree and neural network models in mapping soil properties. J. Mt. Sci. 16, 1833–1847 (2019). https://doi.org/10.1007/s11629-019-5409-8
- Digital soil mapping
- soil properties
- environmental variables
- Artificial Neural Network
- Decision Tree