Predicting and Mapping of Soil Organic Carbon Stock Using Machin Learning Algorithm

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Soil Science, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.

2 Professor, Department of Soil Science, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.

3 Professor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.

Abstract

 
Investigation of soil organic carbon stock (SOCS) in agricultural lands and the role of factors affecting its variability and digital modeling are important for predicting possible scenarios of future carbon stock. The purpose of this study was to investigate the spatial variability and to estimate SOCS at 0 to 100 cm depth based on two generation of machine learning approaches in a part of Qazvin plain. SOCS of about 211 legacy soil data were prepared. The environmental variables including 11 geomorphometric variables and 25 spectral indices with 10-meter spatial resolution were used. Further, the dataset was divided into two parts: 70% of data were chosen as training and 30% of data for model validation. Two algorithm were used for SOCS modeling in the study area. Validation results indicated that the QRF had a higher coefficient of determination than the RF. According to the results of the relative importance of environmental variables, DEM and Valley depth parameters are more important in the spatial modeling of SOCS than other variables. Generally, it is suggested to investigate hybrid models in the process of modeling secondary soil characteristics.

Keywords

References

Adhikari, K., Owens, P. R., Libohova, Z., Miller, D. M., Wills, S. A., & Nemecek, J. (2019). Assessing soil organic carbon stock of Wisconsin, USA and its fate under future land use and climate change. Science of the Total Environment, 667, 833-845.‏
Ahlström, A., Raupach, M. R., Schurgers, G., Smith, B., Arneth, A., Jung, M., ... & Zeng, N. (2015). The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science, 348(6237), 895-899.
Bangroo, S. A., Najar, G. R., Achin, E., & Truong, P. N. (2020). Application of predictor variables in spatial quantification of soil organic carbon and total nitrogen using regression kriging in the North Kashmir forest Himalayas. Catena, 193, 104632.‏
Batjes, N. H. (2014). Total carbon and nitrogen in the soils of the world. European Journal of Soil Science, 65(1), 10-21.‏
Behrens, T., & Scholten, T. (2006). Digital soil mapping in Germany—a review. Journal of Plant Nutrition and Soil Science, 169(3), 434-443.‏
Biau, G., Scornet, E., (2016). A random forest guided tour. Test 25, 197–227.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.‏
Canadell, J. G., Pataki, D. E., Gifford, R., Houghton, R. A., Luo, Y., Raupach, M. R., ... & Steffen, W. (2007). Saturation of the terrestrial carbon sink. In Terrestrial ecosystems in a changing world (pp. 59-78). Springer, Berlin, Heidelberg.
Dharumarajan, S., Kalaiselvi, B., Suputhra, A., Lalitha, M., Hegde, R., Singh, S. K., & Lagacherie, P. (2020). Digital soil mapping of key GlobalSoilMap properties in Northern Karnataka Plateau. Geoderma Regional, 20, e00250.‏
Funes, I., Savé, R., Rovira, P., Molowny-Horas, R., Alcañiz, J. M., Ascaso, E., ... & Vayreda, J. (2019). Agricultural soil organic carbon stocks in the north-eastern Iberian Peninsula: Drivers and spatial variability. Science of the Total Environment, 668, 283-294.‏
Gomes, L. C., Faria, R. M., de Souza, E., Veloso, G. V., Schaefer, C. E. G., & Fernandes Filho, E. I. (2019). Modelling and mapping soil organic carbon stocks in Brazil. Geoderma, 340, 337-350.
Grimm, R., Behrens, T., Märker, M., & Elsenbeer, H. (2008). Soil organic carbon concentrations and stocks on Barro Colorado Island—Digital soil mapping using Random Forests analysis. Geoderma, 146(1-2), 102-113.‏
Hengl, T., Heuvelink, G. B., Kempen, B., Leenaars, J. G., Walsh, M. G., Shepherd, K. D., ... & Tondoh, J. E. (2015). Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions. PloS one, 10(6), e0125814.‏
Hounkpatin, K. O., Stendahl, J., Lundblad, M., & Karltun, E. (2021). Predicting the spatial distribution of soil organic carbon stock in Swedish forests using a group of covariates and site-specific data. Soil, 7(2), 377-398.‏
Kučera, A., Skene, K. R., & Kupec, P. (2020). Soil hydric properties and carbon stock in a semi-arid region of Iraqi Kurdistan: The importance of historical pedogenesis, climate and locality. Ecological Indicators, 119, 106813.
Kuhn, M. (2008). Building predictive models in R using the caret package. Journal of Statistical Software, 28, 1-26.‏
Lagacherie, P. (2008). Digital soil mapping with limited data, Springer: Dordrecht.
Martín, J. R., Álvaro-Fuentes, J., Gonzalo, J., Gil, C., Ramos-Miras, J. J., Corbí, J. G., & Boluda, R. (2016). Assessment of the soil organic carbon stock in Spain. Geoderma, 264, 117-125.
Ma, X., Huete, A., Cleverly, J., Eamus, D., Chevallier, F., Joiner, J., ... & Ponce-Campos, G. (2016). Drought rapidly diminishes the large net CO2 uptake in 2011 over semi-arid Australia. Scientific Reports, 6(1), 1-9.
McBratney, A. B., Santos, M. M., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1-2), 3-52.‏
Meinshausen, N., & Ridgeway, G. (2006). Quantile regression forests. Journal of Machine Learning Research, 7 (6). ‏
Minasny, B., & McBratney, A. B. (2018). Limited effect of organic matter on soil available water capacity. European Journal of Soil Science, 69(1), 39-47.‏
Minasny, B., McBratney, A. B., Malone, B. P., & Wheeler, I. (2013). Digital mapping of soil carbon. Advances in agronomy, 118, 1-47.‏
Nabiollahi, K., Eskandari, S., Taghizadeh-Mehrjardi, R., Kerry, R., & Triantafilis, J. (2019). Assessing soil organic carbon stocks under land-use change scenarios using random forest models. Carbon Management, 10(1), 63-77.
Ottoy, S., De Vos, B., Sindayihebura, A., Hermy, M., & Van Orshoven, J. (2017). Assessing soil organic carbon stocks under current and potential forest cover using digital soil mapping and spatial generalisation. Ecological indicators, 77, 139-150.‏
Rentschler, T., Gries, P., Behrens, T., Bruelheide, H., Kühn, P., Seitz, S., ... & Schmidt, K. (2019). Comparison of catchment scale 3D and 2.5 D modelling of soil organic carbon stocks in Jiangxi Province, PR China. Plos one, 14(8), e0220881.‏
Scholten, T., Goebes, P., Kühn, P., Seitz, S., Assmann, T., Bauhus, J., ... & Schmidt, K. (2017). On the combined effect of soil fertility and topography on tree growth in subtropical forest ecosystems—a study from SE China. Journal of Plant Ecology, 10(1), 111-127.‏
Smith, P. (2012). Agricultural greenhouse gas mitigation potential globally, in E urope and in the UK: what have we learnt in the last 20 years?. Global Change Biology, 18(1), 35-43.‏
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.‏
Taghizadeh-Mehrjardi, R., Schmidt, K., Amirian-Chakan, A., Rentschler, T., Zeraatpisheh, M., Sarmadian, F., ... & Scholten, T. (2020). Improving the spatial prediction of soil organic carbon content in two contrasting climatic regions by stacking machine learning models and rescanning covariate space. Remote Sensing, 12(7), 1095.
Vicente-Vicente, J. L., García-Ruiz, R., Francaviglia, R., Aguilera, E., & Smith, P. (2016). Soil carbon sequestration rates under Mediterranean woody crops using recommended management practices: A meta-analysis. Agriculture, Ecosystems & Environment, 235, 204-214.‏
Wang, B., Waters, C., Orgill, S., Cowie, A., Clark, A., Li Liu, D., ... & Sides, T. (2018). Estimating soil organic carbon stocks using different modelling techniques in the semi-arid rangelands of eastern Australia. Ecological Indicators, 88, 425-438.‏
Were, K., Bui, D. T., Dick, Ø. B., & Singh, B. R. (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.‏
Wiesmeier, M., Barthold, F., Blank, B., & Kögel-Knabner, I. (2011). Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem. Plant and soil, 340(1), 7-24.‏
Žížala, D., Minařík, R., Skála, J., Beitlerová, H., Juřicová, A., Rojas, J. R., ... & Zádorová, T. (2022). High-resolution agriculture soil property maps from digital soil mapping methods, Czech Republic. Catena, 212, 106024.
Iranian Journal of Soil and Water Research
Volume 53, Issue 11
February 2023
Pages 2671-2681

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