Digital Mapping of Top-soil Thickness and Associated Uncertainty Using Machine Learning Approach in Some Part of Arid and Semi-arid Lands of Qazvin Plain

Document Type : Research Paper

Authors

1 Soil science and engineering department, College of agriculture and natural resources, University of Tehran, Karaj, Iran.

2 Soil science department, College of agriculture and natural resources, university of Tehran

3 Remote sensing and Photogrammetry Department, Faculty of Surveying and Spatial Information Engineering, Campus of Technical Colleges, University of Tehran, Tehran, Iran.

Abstract

The present study was carried out to model topsoil thickness using machine learning models (MLM) including random forest (RF) and artificial neural network (ANN) in around 60,000 hectares of Qazvin plain lands (intermediate of Abyek and Nazarabad) with an observational density of 278 profiles during 2016 until 2020, and 17 environmental covariates extracted from Landsat 8 satellite images, primary and secondary derivatives from Digital elevation model, climate data, land use and geology maps. Boruta supervised algorithm and expert knowledge were used to select the best relevant environmental covariates. Two functions include "nnet" and "random forest" (RF) by "caret" package in the R software were used. Modeling of topsoil thickness carried out based on 80% of the data in the calibration subset and 20% of the data was used for model validation. The uncertainty of the output maps was quantified using two methods of “bootstrapping and k-fold”. A number of 10 environmental covariates selected among 17 variables, and the relative importance introduced the greenness index, wind effect, diffused radiation, and Mrvbf as the most important covariates, respectively. The validation results indicate that the RF model with R2 of 0.8 and RMSE less than 3 cm and the bias is 0.63 cm in compare to the ANN, With R2, RMSE, and Bias 0.43, 0.05, and.004, respectively was outperform. Also, the CCC for the RF model increased by 50% compared to the ANN. The uncertainty estimated by the bootstrapping method was 7 cm lower compared to k-fold in the regions with 10-15 cm thickness and both of two methods show the same spatial pattern in other parts. The RF model along with selected covariates environmental variables and quantified uncertainties of output maps can be used to model the topsoil thickness and management decision making in areas similar to this study in future studies.

Keywords

References

Adhikari, K., Owens, P. R., Ashworth, A. J., Sauer, T. J., Libohova, Z., Richter, J. L., & Miller, D. M. (2018). Topographic controls on soil nutrient variations in a silvopasture system. Agrosystems, Geosciences & Environment, 1(1), 1-15.
Arrouays, D., Grundy, M. G., Hartemink, A. E., Hempel, J. W., Heuvelink, G. B., Hong, S. Y., ... & Zhang, G. L. (2014). GlobalSoilMap: Toward a fine-resolution global grid of soil properties. Advances in agronomy, 125, 93-134.
Baltensweiler, A., Walthert, L., Hanewinkel, M., Zimmermann, S., & Nussbaum, M. (2021). Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland. Geoderma Regional, 27, e00437.
Chen, S., Richer-de-Forges, A. C., Mulder, V. L., Martelet, G., Loiseau, T., Lehmann, S., & Arrouays, D. (2021). Digital mapping of the soil thickness of loess deposits over a calcareous bedrock in central France. Catena, 198, 105062.
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., ... & Böhner, J. (2015). System for automated geoscientific analyses (SAGA) v. 2.1. 4. Geoscientific Model Development, 8(7), 1991-2007.
Dharumarajan, S., Hegde, R., & Lalitha, M. (2021). Modelling of soil depth and hydraulic properties at regional level using environmental covariates-A case study in India. Geoderma Regional, 27, e00439.
Gessler, P. E., Moore, I. D., McKenzie, N. J., & Ryan, P. J. (1995). Soil-landscape modelling and spatial prediction of soil attributes. International journal of geographical information systems, 9(4), 421-432.
Han, X., Liu, J., Mitra, S., Li, X., Srivastava, P., Guzman, S. M., & Chen, X. (2018). Selection of optimal scales for soil depth prediction on headwater hillslopes: A modeling approach. Catena, 163, 257-275.
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., ... & Kempen, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2), e0169748.
Horst-Heinen, T. Z., Dalmolin, R. S. D., ten Caten, A., Moura-Bueno, J. M., Grunwald, S., de Araújo Pedron, F., ... & da Silva-Sangoi, D. V. (2021). Soil depth prediction by digital soil mapping and its impact in pine forestry productivity in South Brazil. Forest Ecology and Management, 488, 118983.
Kursa, M.B.; Rudnicki, W.R. Feature selection with the Boruta package. J. Stat. Softw. 2010, 36, 1–13.
Lalitha, M., Dharumarajan, S., Suputhra, A., Kalaiselvi, B., Hegde, R., Reddy, R. S., ... & Dwivedi, B. S. (2021). Spatial prediction of soil depth using environmental covariates by quantile regression forest model. Environmental Monitoring and Assessment, 193(10), 1-10.
Leenaars, J. G., Claessens, L., Heuvelink, G. B., Hengl, T., González, M. R., van Bussel, L. G., ... & Cassman, K. G. (2018). Mapping rootable depth and root zone plant-available water holding capacity of the soil of sub-Saharan Africa. Geoderma, 324, 18-36.
Li, A., Tan, X., Wu, W., Liu, H., & Zhu, J. (2017). Predicting active-layer soil thickness using topographic variables at a small watershed scale. Plos one, 12(9), e0183742.
Li, X., Luo, J., Jin, X., He, Q., & Niu, Y. (2020). Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods. Remote Sensing, 12(21), 3609.
Liddicoat, C., Maschmedt, D., Clifford, D., Searle, R., Herrmann, T., Macdonald, L. M., & Baldock, J. (2015). Predictive mapping of soil organic carbon stocks in South Australia’s agricultural zone. Soil Research, 53(8), 956-973.
McBratney, A. B., Minasny, B., & Stockmann, U. (Eds.). (2018). Pedometrics. Springer.
Mehnatkesh, A., Ayoubi, S., Jalalian, A., & Sahrawat, K. L. (2013). Relationships between soil depth and terrain attributes in a semi arid hilly region in western Iran. Journal of Mountain Science, 10(1), 163-172.
Moore, I. D., Gessler, P. E., Nielsen, G. A. E., & Peterson, G. A. (1993). Soil attribute prediction using terrain analysis. Soil science society of America journal, 57(2), 443-452.
Mousavi, S. R., Sarmadian, F., & Rahmani, A. (2020). Modelling and Prediction of Soil Classes Using Boosting Regression Tree and Random Forests Machine Learning Algorithms in Some Part of Qazvin Plain. Iranian Journal of Soil and Water Research, 50(10), 2525-2538. (In Farsi).
MOUSAVI, S. R., Sarmadian, F., Omid, M., & Bogaert, P. (2021). Digital Modeling of Three-Dimensional Soil Salinity Variation Using Machine Learning Algorithms in Arid and Semi-Arid lands of Qazvin Plain. Iranian Journal of Soil and Water Research, 52(7), 1915-1929. (In Farsi).
Mousavi, S., Sarmadian, F., Alijani, Z., & Taati, A. (2017). Land suitability evaluation for irrigating wheat by geopedological approach and geographic information system: A case study of Qazvin plain, Iran. Eurasian Journal of Soil Science, 6(3), 275-284.
Rahmani, A. Sarnadian, F. Mousavi, S.R. 2020. Application of geomorphometric features in digital soil mapping using fuzzy logic and machine learning. Journal of Rangeland and Watershed Management. Vol 73, (1):105-124. (In Farsi).
Rahmani, A., Sarmadian, F., Mousavi, S. R., & Khamoshi, S. E. (2019). Digital soil mapping using geomorphometric analysis and case-based fuzzy logic approach. In: Proceedings of 5th the International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 863-866.
Rasaei, Z., Rossiter, D. G., & Farshad, A. (2020). Rescue and renewal of legacy soil resource inventories in Iran as an input to digital soil mapping. Geoderma Regional, 21, e00262.
Rossel, R. V., Chen, C., Grundy, M. J., Searle, R., Clifford, D., & Campbell, P. H. (2015). The Australian three-dimensional soil grid: Australia’s contribution to the GlobalSoilMap project. Soil Research, 53(8), 845-864.
Sepahvand, M., Khormali, F., Kiani, F., & Eftekhari, K. (2017). Modeling the Relationship between Soil Depth and Topography Characteristics in order to Predict Soil Depth in Rimleh Basin of Lorestan Province. Journal of Soil Research. 31 (4), 601-611. (In Farsi).
Shrestha, D. L., & Solomatine, D. P. (2006). Machine learning approaches for estimation of prediction interval for the model output. Neural Networks, 19(2), 225-235.
Staff, S.S., 2014. Keys to Soil Taxonomy, 12th Edn Washington. DC: Natural Resources Conservation Service, United States Department of Agriculture.
Szatmári, G., & Pásztor, L. (2019). Comparison of various uncertainty modelling approaches based on geostatistics and machine learning algorithms. Geoderma, 337, 1329-1340.
Tesfa, T. K., Tarboton, D. G., Chandler, D. G., & McNamara, J. P. (2009). Modeling soil depth from topographic and land cover attributes. Water Resources Research, 45(10).
Thompson, A. L., Gantzer, C. J., & Anderson, S. H. (1991). Topsoil depth, fertility, water management, and weather influences on yield. Soil Science Society of America Journal, 55(4), 1085-1091.
Tibshirani, R. J., & Efron, B. (1993). An introduction to the bootstrap. Monographs on statistics and applied probability, 57, 1-436.
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.
Vanwinckelen, G., & Blockeel, H. (2012). On estimating model accuracy with repeated cross-validation. In BeneLearn 2012: Proceedings of the 21st Belgian-Dutch conference on machine learning (pp. 39-44).
Vickers, N. J. (2017). Animal communication: when i’m calling you, will you answer too?. Current biology, 27(14), R713-R715.
Minasny, B., McBratney, A. B., & Lark, R. M. (2008). Digital soil mapping technologies for countries with sparse data infrastructures. Digital soil mapping with limited data, 15-30.
Wang, F., Sahana, M., Pahlevanzadeh, B., Pal, S. C., Shit, P. K., Piran, M. J., ... & Mosavi, A. (2021). Applying different resampling strategies in machine learning models to predict head-cut gully erosion susceptibility. Alexandria Engineering Journal, 60(6), 5813-5829.
Wang, Q., Wu, B., Stein, A., Zhu, L., & Zeng, Y. (2018). Soil depth spatial prediction by fuzzy soil-landscape model. Journal of soils and sediments, 18(3), 1041-1051.
Wilson, J. (2018). Environmental applications of digital terrain modeling. John Wiley & Sons. 359pp.
Zahedi, p. Shahedi, K. Nejad Roshan, H. & Soleimani, K. (2017a). Estimation of soil depth using environmental variables obtained from digital elevation model and remote sensing data. Journal of Soil and Water Sciences, 21 (4), 111-127. (In Farsi).
Zahedi, S., Shahedi, K., Rawshan, M. H., Solimani, K., & Dadkhah, K. (2017b). Soil depth modelling using terrain analysis and satellite imagery: the case study of Qeshlaq mountainous watershed (Kurdistan, Iran). Journal of Agricultural Engineering, 48(3), 167-174.
Zeraatpisheh, M., Jafari, A., Bodaghabadi, M. B., Ayoubi, S., Taghizadeh-Mehrjardi, R., Toomanian, N., and Xu, M. (2020). Conventional and digital soil mapping in Iran: Past, present, and future. Catena, 188, 104424.
Zhao, R., & Wu, K. (2021). Soil Health Evaluation of Farmland Based on Functional Soil Management—A Case Study of Yixing City, Jiangsu Province, China. Agriculture, 11(7), 583.
Zhu, A. X., Hudson, B., Burt, J., Lubich, K., & Simonson, D. (2001). Soil mapping using GIS, expert knowledge, and fuzzy logic. Soil Science Society of America Journal, 65(5), 1463-1472.
Zinck, J. A., Metternicht, G., Bocco, G., & Del Valle, H. F. (Eds.). (2015). Geopedology: An integration of geomorphology and pedology for soil and landscape studies. Springer.
 
Iranian Journal of Soil and Water Research
Volume 53, Issue 3
June 2022
Pages 585-602

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