Spatial Prediction Some of the Surface Soil Properties Using Interpolation and Machine Learning Models

Document Type : Complete scientific research article

Authors

1 Department o soil science and engineering, College of Agriculture and Natural resources, University of Tehran

2 PhD student of soil resource management, Faculty of agriculture, University of shahidchamran Ahvaz.

3 Soil Science engineering,College of Agriculture and Natural resources, University of Tehran.

4 Scientific Staff of Agricultural and Natural Resources Research center of Kurdisran.

5 Mohammad Kuhsar Bostani, Soil and water expert, Kurdistan Agricultural Research Center

Abstract

Background and objectives: Accurate and detailed spatial soil information over the landscape is essential for the precision monitoring of land resources, hydrological applications, land use management. The present study aimed to predict the spatial prediction of SOC, CCE, Clay, Silt, and Sand in the Qorve-Dehgolan region, Kurdestan province.
Qorve-Dehgolan region, with mean annual temperature and precipitation of 12 C° and 348 mm (20-year statistical period), has soil moisture and temperature regimes xeric and thermic, respectively. A total of 145 samples were collected from the topsoil (0-30 cm) based on a random sampling pattern. Then, all of the soil samples were transferred to a soil laboratory for physicochemical analysis. Random forest (RF) as a nonparametric model and Ordinary kriging (OK) and inverse distance weighting (IDW) as an interpolation method were used for modeling the soil properties and their spatial autocorrelation. All steps of modeling for RF and interpolation methods (OK and IDW) were performed in RStudio, ArcGIS and, GS+ software, respectively. A total of 30 environmental covariates, including the Digital Elevation Model (DEM) derivatives in the SAGA GIS 7.3 software and Landsat 8 satellite reflective band data in the ERDAS IMAGINE software, were developed as environmental variables. All of the environmental covariates were resampled at resolution-30 m. The most appropriate covariates were selected according to the variance inflation factor (VIF). Modeling of soil properties was performed according to 80% and 20% of data sets, respectively for calibration and validation, and two statistics of root mean square error (RMSE) and determination coefficient (R2) was used to determine the accuracy of the models.
Results: Seven variables including SAVI, EVI1, GNDVI, RVI1, DEM, Channel Network, and TPI were selected from the 30 variables prepared as the most appropriate auxiliary variables based on the variance inflation index. Four remote sensing variables include the adjusted soil vegetation index (SAVI), the greenness of the normalized differential vegetative index (GNDVI), the relative vegetation index (RVI) and the Enhanced vegetation index (EVI), and three geomorphometric attributes including, digital elevation model (DEM), Vertical distance to channel network and the topographic position index (TPI) were the most important parameters. The results of modeling showed that RF model for soil organic carbon variable (R2 = 0.5 and %RMSE= 0.4), calcium carbonate equivalent (R2 = 0.4 and %RMSE = 11.61), clay variable (R2= 0.21 and %RMSE=5.65), the Silt variable (R2 = 0.15 and %RMSE= 7.24) and, Ordinary kriging methods for sand variables with (R2 = 0.14 and %RMSE = 10.26) was the most accurate than RF and IDW models. Among the semi-variogram models, the exponential model had the best performance for soil organic carbon, clay, silt, and sand percentage, with the exception of CCE which follows the spherical model. The results of spatial autocorrelation showed that for both variables CCE and Sand had a strong class and, the rest had a moderate class. The highest values of the semi-variogram sill were related to the calcium carbonate equivalent and clay, and the lowest values were related to the soil organic carbon and sand contents. These results indicate that, the existence of a random pattern or weak spatial structure in the samples that used to calculate the experimental semi-variogram. Among the seven environmental covariates were used for spatial modeling of top-soil organic carbon, calcium equivalent carbonate, and clay, the geomorphometric attributes such as digital height model, topographic position index and vertical distance to channel network are of the most important and NDVI, SAVI and RVI covariates were more important in predicting of sand and silt properties.

Keywords

References

1.Adhikari, K., Owens, P.R., Ashworth, A.J., Sauer, T.J., Libohova, Z., Richter, J.L., and Miller, D.M. 2018. Topographic controls on soil nutrient variations in a silvopasture system. Agrosystems, Geosciences & Environment. 1: 1. 1-15.
2.Birth, G.S., and McVey, G.R. 1968. Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal. 60: 6. 640-643.
3.Bostani, A., Salahedin, M., Rahman, M.M., and Namdar Khojasteh, D. 2017. Spatial mapping of soil properties using geostatistical methods in the Ghazvin Plains of Iran. Modern Applied Science. 11: 23-37.
4.Breiman, L. 2001. Random forests. Machine learning. 45: 1. 5-32.
5.Breiman, L., and Cutler, A. 2004. Random Forests. Department of Statistics, University of Berkeley.
6.Brungard, C.W., Boettinger, J.L., Duniway, M.C., Wills, S.A., and Edwards Jr, T.C. 2015. Machine learning for predicting soil classes in three semi-arid landscapes. Geoderma. 239-240: 68-83.
7.Cambardella, C.A., Moorman, T.B., Parkin, T.B., Karlen, D.L., Novak, J.M., Turco, R.F., and Konopka, A.E. 1994. Field-scale variability of soil properties in central Iowa soils. Soil Science Society American Journal. 58: 5. 1501-1511.
8.Ceddia, M.B., Vieira, S.R., Villela, A.L.O., Mota, L.D.S., Anjos, L.H.C.D., and Carvalho, D.F.D. 2009. Topography and spatial variability of soil physical properties. Scientia Agricola.66: 3. 338-352.
9.Dormann, C.F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carre, G., and Munkemuller, T. 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography. 36: 1. 27-46.
10.Efron, B. 1979. Bootstrap methods: Another look at the jackknife. The Annals of Statistics. 7: 1. 1-26.
11.ESRI, 2011. ArcGIS Desktop: Release 10. Environmental Systems Research Institute, Redlands, CA.
12.Falahatkar, S., Hosseini, S.M., Ayoubi, S., and Salmanmahiny, A. 2016. Predicting soil organic carbon density using auxiliary environmental variables in northern Iran. Archives of Agronomy and Soil Science. 62: 3. 375-393.
13.Flynn, T., de Clercq, W., Rozanov, A., and Clarke, C. 2019. High-resolution digital soil mapping of multiple soil properties: an alternative to the traditional field survey. South African Journal of Plant and Soil. pp. 237-247.
14.Gee, G.W., and Bauder, J.W. 1986. Particle-size analysis .Methods of soil analysis: Part 1- Physical and mineralogical methods. methods of soil analysis. pp. 383-411.
15.Genuer, R., Poggi, J.M., and Tuleau-Malot, C. 2010. Variable selection using random forests. Pattern Recognition Letters, 31: 14. 2225-2236.
16.Gitelson, A.A., Kaufman, Y.J., and Merzlyak, M.N. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote sensing of Environment. 58: 3. 289-298.
17.Golmohamadi, F., Nabiollahi, K., Taghizadeh-Mehrjardi, R., and Davari, M. 2017. Digital mapping of soil erodibility (Case study: Dehgolan, Kurdistan province). Journal of Water and Soil Conservation. 24: 87-103.
18.Grunwald, S. 2009. Multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma, 152: 195-207.
19.Guo, Z., Adhikari, K., Chellasamy M., Greve, M.B., Owens, P.R., and Greve, M.H. 2019. Selection of terrain attributes and its scale dependency on soil organic carbon prediction. Geoderma. 340: 303-312.
20.Hartemink, A.E., McBratney, A., and Mendonca-Santos, M.D.L. 2008. Digital Soil Mapping Technologies for Countries with Sparse Data Infrastructures. Digital Soil Mapping with Limited Data Springer. pp. 15-30.
21.Hassani Pak, A.A. 2013. Geostatistical. University of Tehran press. (Translated in Persian)
22.Hook, P.B., and Burke, I.C. 2000. Biogeochemistry in a shortgrass landscape: control by topography, soil texture, and microclimate. Ecology.81: 10. 2686-2703.
23.Huete, A.R. 1988. Soil Adjusted Vegetation Index (SAVI). Remote Sensing of Environment. 25: 295-309.
24.Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., and Ferreira, L.G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment. 83: 195-213.
25.Jafari, M., Asgari, M., Moazami, M., Biniaz, M., and Tahmores, M. 2008. Spatial variation of some soil properties using geostatistical methods, Research and Construction in Agriculture and Horticulture. 80: 177-185. (In Persian)
26.Jiachun, S., Hazin, W., Jianming, X., Jinjun, W., Xingmei, L., Haiping, Z., and Shunlan, J. 2006. Spatial distribution of heavy metal in soil: A case of Changing, China. Environmental Geology. 10: 245-264.
27.Johnson, N., Ravnborg, H.M., Westermann, O., and Probst, K.2002. User participation in watershed management and research. Water policy. 3: 6. 507-520.
28.Junior, L.A.Z., Lana, R.M.Q., and Guimaraes, E.C. 2007. Variabilidade espacial do pH, teores de matéria organica e micronutrients em profundities de amostragem num Latossolo Vermelho sob semeadura direta. Ciência Rural. 37: 4. 1000-1007.
29.Keskin, H., Grunwald S., and Harris, W.G. 2019. Digital mapping of soil carbon fractions with machine learning. Geoderma. 339: 40-58.
30.Kidd, D., Webb, M., Malone, B., Minasny, B., and McBratney, A. 2015. Eighty-metre resolution 3D soil-attribute maps for Tasmania, Australia. Soil Research. 53: 8. 932-955.
31.Liu, J., Shi, B., Jiang, H., Bae, S., and Huang, H. 2009. Improvement of water-stability of clay aggregates admixed with aqueous polymer soil stabilizers. Catena. 77: 3. 175-179.
32.Lin, H. 2010. Earth's critical zone and hydropedology: concepts, characteristics, and advances. Hydrology and Earth System Sciences. 14: 25-45.
33.Ma, Y., Minasny, B., and Wu, Ch. 2017. Mapping key soil properties to support agricultural production in Eastern China. Geoderma Regional. 10: 144-153.
34.Mahmoudabadi, E., Karimi, A., Haghnia, G.H., and Sepehr, A. 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.
35.Malone, B.P., McBratney, A., Minasny, B., and Laslett, G. 2009. Mapping continuous depth functions of soil carbon storage and available water capacity. Geoderma. 154: 138-152.
36.McBratney, A.B., Odeh, I.O., Bishop, T.F., Dunbar, M.S., and Shatar, T.M. 2000. An overview of pedometric techniques for use in soil survey. Geoderma. 97: 3-4. 293-327.
37.McBratney, A.B., Mendonça-Santos, M.L., Minasny, B. 2003. On digital soil mapping. Geoderma. 117: 3-52.
38.Mehnatkesh, A., Ayoubi, S., Jalalian, A., and Sahrawat, K.L. 2013. Relationships between soil depth and terrain attributes in a semiarid hilly region in western Iran. Journal of Mountain Science. 10: 1. 163-172.
39.Minasny, B., and McBratney, A.B. 2016. Digital soil mapping: A brief history and some lessons. Geoderma. 264: 301-311.
40.Mousavi, S.R., Sarmadian, F., Dehghani, S., Sadikhani, M.R., and Taati, A. 2017. Evaluating inverse distance weighting and kriging methods in estimation of some physical and chemical properties of soil in Qazvin Plain. Eurasian Journal of Soil Science. 6: 4. 327-336.
41.Nelson, R.E. 1982. Carbonate and gypsum. P 181-179. In: A.L. Page (ed.) Methods of soil analysis. American Society of Agronomy, Madison.
42.Neter, J., Wasserman, W., and Kutner, M.H. 1989. Applied linear regression models. Chicago. 3rd. )ed.( 720: 519. 536.
43.Olaya, V. 2004. A gentle introduction to SAGA GIS. The SAGA User Group eV, Gottingen, Germany. 208p.
44.PALSAR, A. 2016. Japan aerospace exploration agency. Available from: https://www.asf.alaska.edu/sar-data/ palsar/.
45.Pouladi, N., Moller, A.B., Tabatabai, S., and Greve, M.H. 2019. Mapping soil organic matter contents at field level with Cubist, Random Forest and kriging. Geoderma. 342: 85-92.
46.Soil Survey Staff. 2014. Keys to soil taxonomy. 12th ed. USDANatural Resources Conservation Service, Washington, DC.
47.Taghizadeh-Mehrjardi, R. 2016. Modern concepts in Soil Science (Pedometric). Ardakan Univ. Press. 311p. (In Persian)
48.Van Wambeke, A.R. 2000. The Newhall simulation model for estimating soil moisture and temperature regimes. Department of Crop and Soil Sciences, Cornell University, Ithaca, NY USA.pp. 1-9.
49.Veronesi, F., and Schillaci, C. 2019. Comparison between geostatistical and machine learning models as predictors of topsoil organic carbon with a focus on local uncertainty estimation. Ecological Indicators. 101: 1032-1044.
50.Liaw, A., and Wiener, M. 2002. Classification and regression by Random forest. R news. 2: 3. 18-22.
51.Viscarra Rossel, R.A., Webster, R., and Kidd, D. 2014. Mapping gamma radiation and its uncertainty from weathering products in a Tasmanian landscape with a proximal sensor and random forest kriging. Earth Surface Processes and Landforms. 39: 6. 735-748.
52.Walkley, A., and Black, I.A. 1934. An examination of the digestion method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Science.37: 29-38.
53.Webster, R., and Oliver, M.A.2007. Geostatistics for environmental scientists: John Wiley and Sons. 330p.
54.Wilford, J., De Caritat, P., and Bui, E. 2015. Modelling the abundance of soil calcium carbonate across Australia using geochemical survey data and environmental predictors. Geoderma. 259: 81-92.
55.Wilding, L. 1985. Spatial variability: its documentation, accommodation and implication to soil surveys. pp. 166-194.
56.Witten, I.H., Frank, E., and Hall, M.A. 2011. Data Mining: Practical machine learning tools and techniques: Morgan Kaufmann. pp. 1-525.
57.Zeraatpisheh, M., Ayoubi, S., Jafari, A., Tajik, S., and Finke, P. 2019. Digital mapping of soil properties using multiple machines learning in a semi-arid region, central Iran. Geoderma. 338: 445-452.
Journal of Soil Management and Sustainable Production
Volume 10, Issue 3
December 2021
Pages 27-49

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