Spatial Prediction of Soil Surface Organic Carbon Using Spectral and Non-Spectral Factors (Case Study; Asuran Summer Rangeland, Semnan Province)

Document Type : Research Paper

Authors

1 Assistant Professor, Rangeland Research Division, Research Institute of Forests and Rangelands, Agricultural Research Education and Extension Organization (AREEO), Tehran, Iran

2 Senior Research Expert, Rangeland Research Division, Research Institute of Forests and Rangelands, Agricultural Research Education and Extension Organization (AREEO), Tehran, Iran.

3 Rangeland Research Division, Rangelands and Forests of Institute Research, Agricultural Research Extension Education Organization

Abstract

Soil organic carbon is one of the most important indicators of soil quality. The purpose of this study is to study the spectral and non-spectral behaviors of soil in order to estimate the organic carbon of topsoil using factor analysis and multiple regression methods in the semi-steppe rangelands of Asuran, Semnan province. Soil sampling was performed using stratified random sampling method. After creating a map of homogeneous units in the area, in each homogeneous unit according to its area, several sampling points were selected completely randomly. A total of 145 sampling points were collected. At each sampling point, a composite soil sample (a mixture of 9 observations) was taken. Soil organic carbon was measured using Valkyli-Block titration method. Data of 114 samples were used to calibrate the model and data of 31 samples were used to validate it. The results showed that the correlation of spectral variables obtained from Landsat OLI sensor with surface soil organic carbon is higher than non-spectral variables obtained from 1: 25000 topographic maps. Also, the results of factor analysis by principal component analysis with eigenvalues greater than one showed that the total cumulative variance explained by 14 variables was equal to 90.2%, which was explained by three factors. The regression equation generated by the three extracted factors had suitable potential for predicting surface soil organic carbon (R2 = 0.59). The root mean square error (RMSE) of the proposed model was calculated to be 0.3.

Keywords


[1] Abbas Nejad, B. and Khajedin, S. J. (2013). Effect of urban reforestation on carbon sequestration in arid soils using remote sensing technology. Journal of Applied RS & GIS Techniques in Natural Resource Science, 3(4): 57-71. (In Persian)
[2] Baig, M. H. A., Zhang, L., Shuai, T. and Tong, Q. (2014). Derivation of a tasseled cap transformation based on Landsat 8 at satellite reflectance. Remote Sensing Letters. 5 (5): 423-431.
[3] Bangroo, S.A., Najara, G.R., Achin, E. and Truongc, 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, Journal of Catena, 193: 104632.
[4] Beven, K. and Kirkby, N. (1979). A physically based, variable contributing area model of basin hydrology. Hydrolog. Sci. Bull. 24 n (1), 43–69.
[5] Boettinger, J. L., Ramsey, R. D., Bodily, J. M., Cole, N. J., Kienast_Brown, S., Nield, S. J., Saundes, A.M. and Stum, A. K. (2008). Landsat spectral data for digital soil mapping. 193–203. In: Hartemink, A. E., McBratney, A. B., Mendonca Santos, M. L. (Eds.), Digital Soil Mapping With Limited Data. Springer science, Australia.
[6] Cotrufo, M. F., Conant, R. T. and Paustian, K. (2011). Soil organic matter dynamics: land use, management and global change.Journal of Plant soil, 338:1-3.
[7] Franklin, J., McCullough, P. and Gray, C. (2000). Terrain variables used for predictive mapping of vegetation communities in Southern California. In Wilson J, Gallant J (Eds,) Terrain analysis:  principles and applications. Wiley, New York, Chichester, Torono and Brisbane, 331-353.
[8] Goldasteh, A., Agha Mir Karimi, S., Khoda Rahmi, M., Torabi, M. and Asghari, R. (2000). User guide of SPSS 6.0 for windows. Hami press, 533 p.
[9] Guo, Z., Adhikari, K.,Chellasamy, M., Greve, M.B., Owens, P.R. and Greve, M.G. (2019). Selection of terrain attributes and its scale dependency on soil organic carbon prediction. Journal of Geoderma, 340: 303-312.
[10] Hengle, T., 2009. A Practical Guide to Geostatistical Mapping. Amesterdam University Press, 293 p.
[11] Howard, M. C. (2016). A Review of Exploratory Factor Analysis Decisions and Overview of Current Practices: What We Are Doing and How Can We Improve?, International Journal of Human-Computer Interaction, 32 (1): 51-62.
[12] Jafarian, Z., Tayefeh Seyyed Alikhani, L. and Tamartash, R. (2012). Investigation of Carbon Storage Potential of Artemisia Aucheri, Agropyron elongatum, Stipa barbata in Semi-arid Rangelands of Iran (Case study: Peshert Region, Kiasar). Journal of Range and Watershed Management, Iranian Journal of Natural Resources, 65 (2): 191-202.
[13] Khalifehzadeh, R., Tamartash, R., Tatian, M. R. and Sarajian Maralan, M. R. (2018). An estimation of topsoil organic carbon by combining factor analysis and multiple regression in semi-steppe rangelands of Lazour, Firouzkooh. Iranian Journal of Range and Desert Research, 25 (3):699-712. 
[14] Liang, S., Shuey, C. J., Russ, A. L., Fang, H., Chen, M., Walthall, C. L. and Hunt, R. (2003). Narrowband to broadband conversions of land surface albedo: II. Validation. Remote Sensisng of Environment, 84 (1): 25-41.
[15] Liu, Q., Liu, G., Huang, C., Xie, C. (2015). Comparison of tasselled cap transformations based on the selective bands of Landsat 8 OLI TOA reflectance images. International Journal of Remote Sensing, 36(2): 417-441.
[16] McCoy, R.M. (2005). Field Methods in Remote Sensing, The Guildford press, New York, 159 p.
[17] Mirza Shahi, K. and Bazargan, K. (2015). Management of soil organic matter. Soil and Water Research Institute (SWRI) press, Technical Journal No. 535, 20 p. (in persian).
[18] Mondal, A., Khare, D., Kundu, S., Mondal, S., Mukherjee, S., Mukhopadhyay, A. (2017). Spatial soil organic carbon (SOC) prediction by regression kriging using remote sensing data. The Egyptian Journal of Remote Sensing and Space Sciences, 20(1): 61-70.
[19] Piccini, C., Marchetti, A. and Francaviglia, R. (2014). Estimation of soil organic matter by geostatistical methods: use of auxiliary information in agriculture and environment assessment. Ecol. Ind. 36: 301-314.
[20] Purghaumi, H., Khagehedin, S.J.,  Jaafari, R. and Purghaumi, A. (2013). Mapping Soil Organic Carbon Using IRS-AWIFS Satellite Imagery (Case Study: Dehaghan Rangeland, Isfahan, IRAN). Journal of Rangeland Science, 3(3):
200-212.
[21] Sheidaye Karkaj, A., Sepehri, A., Barani, H. and Motamedi, J. (2017). Soil organic carbon reserve relationship with some soil properties in East Azerbaijan. Journal of Rangeland, 11(2): 125-138.
[22] Tamartash, R., Tatian, M. R. and Yousefian, M. (2012). The effect of the different vegetative species on the carbon sequestration in Miankaleh Plain Rangelands. Journal of Environmental Studies, 38 (62): 45-54.
[23] Tarkalson, D. D., Brown, B., Kok, H. and Bjorneberg, D. L. (2009). Irrigated small-grain residue management effects on soil chemical and physical properties and nutrient cycling. Journal of Soil Science, 174:303-311.
[24] Walkley, A. and Black, I. A. (1934). An examination of the digeston method for determining soil organic matter, and a proposed modification of the chromic acid thtration method. Soil Sciences, 37: 29-38.
[25] Xiao, J., Shen, Y., Tateishi, R. and Bayaer, W. (2006). Development of topsoil grain size index for monitoring desertification in arid land using remote sensing. International Journal of Remote Sensing, 12(27): 2411–2422.
Volume 74, Issue 1
June 2021
Pages 177-188
  • Receive Date: 07 November 2020
  • Revise Date: 08 January 2021
  • Accept Date: 08 January 2021
  • First Publish Date: 22 May 2021