Prediction of Soil Organic Carbon (SOC) in Semi-Arid Rangeland Using Multivariate Statistical Analysis based on Remotely Sensing Data (Case study: Neyshabur Rangeland, Khorasan-Razavi Province, Iran)
Subject Areas : Remote Sensing (RS)Hamid Reza Matinfar 1 , Ahmad Reza Pilevari 2 , Akbar Sohrabi 3
1 - Department of Soil Science, Collage of Agriculture, Lorestan University, Khoramabad, Iran
2 - Department of Soil Science, Collage of Agriculture, Lorestan University, khoramabad, Iran
3 - Department of Soil Science, Collage of Agriculture, Lorestan University, Khoramabad, Iran
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Al-Abbas, A. H., Swain P. H. and, Baumgardner M. F., 1972. Relating organic matter and clay content to the multispectral radiance of soils, Soil Sci. 114,pp. 477–485.
Aı¨chi H., Fouad Y., Walter C., Viscarra Rossel R.A., Zohra Lili Chabaane, Mustapha Sanaa. 2009. Regional predictions of soil organic carbon content from spectral reflectance measurements. Biosystems Engineering, doi:10.1016/j.biosystemseng.2009.08.002.
Baumgardner, M. F., Kristof S. J., Johansen C. J. and Zachary A. L., 1970. Effect of organic matter in the multispectral properties of soils, Proceedings of the Indiana Academy of Science. 79,pp.413–422.
Ben-Dor, E., Irons, J.R. and Epema, G.F., 1999. Soil Reflectance. In: Rencz, N., Ed., Remote Sensing for the Earth Sciences: Manual of Remote Sensing, John Wiley and Sons, New York,pp. 111-188.
Blackmer T.M., and Schepers J.S., 1996. Aerial photography to detect nitrogen stress in corn, Journal of Plant Physiology, 148 (3-4),pp. 440-444.
Broge, N. H., Greve M. H. and Larsen R., 2005. Estimating topsoil organic matter content in two experimental fields in Denmark using multi-spectral aerial imagery and topographic data, Geografisk Tidsskrift, Danish Journal of Geography. 105(2)pp. 1-14.
Campbell, J. B., 2006. Introduction to remote sensing (4th Ed.). New York, USA: Guilford Press.
Chen, F., Kissel D. E., West L. T. and Adkins W. 2000. Field-scale mapping of surface soil organic carbon using remotely sensed imagery, Soil Sci. Soc. Am. J. 64,pp. 746–753.
Chen, F., West L. T., Kissel D. E., Clark R. and Adkins W., 2008. Field-scale mapping of soil organic carbon in soil-landscape modelling, Proceedings of the 8th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Shanghai, P. R. China, pp.294-301.
Dwivedi, M. R. 2001. Soil resource mapping: A remote sensing perspective. Remote Sensing Reviews 20(2),pp. 89-122.
Fox, G. A. and Sabbagh G. J., 2002. Estimation of soil organic matter from red and near-infrared remotely sensed data using a soil line Euclidean distance technique, Soil Sci. Soc. Am. J. 66,pp. 1922-1929.
Fox G. A., Sabbagh G. J. Searcy, S. W., and Yang C. 2004. An Automated Soil Line Identification Routine for Remotely Sensed Images, Soil Sci. Soc. Am. J. 68,pp.1326–1331.
Fox G. and Metla R., 2005. Soil Property Analysis using Principal Components Analysis, Soil Line, and Regression Models. Soil Science Society of America Journal.69(6),pp. 1782-1788.
Frazier B. and Cheng Y.,1989. Remote sensing of soils in the eastern Palouse region with Landsat Thematic Mapper. Remote Sensing of Environment. 28,pp.317-325.
French A.N., Norman J.M. and Anderson M.C., 2003. A simple and fast atmospheric correction for spaceborne remote sensing of surface temperature, Remote Sensing of Environment 87,pp. 326–333.
Gholami A., Yongqiang Y., Sadoddin A. and Zhang W.2021, Soil Organic Carbon Stock Changes in Response to Land-use Changes in Iran, Journal of Rangeland Science, 2021, 11(3),pp. 247-268.
Guo Z., Adhikari K., Chellasamy M., GreveM. B. Owens P. R. and GreveM. H., 2019. Selection of terrain attributes and its scale dependency on soil organic carbon prediction: Geoderma, 340, pp.303-312.
Henderson, T. L., Baumgardner M. F., Franzmeier D. P., Stott D. E. and Coster D. C.,1992. High dimensional reflectance analysis of soil organic matter, Soil Sci. Soc. Am. J. 56,pp. 865-872.
Hummel J. W., Sudduth K. A. and Hollinger S. E., 2001. Soil moisture and organic matter prediction of surface and subsurface soils using an NIR soil sensor, Comput. Electron. Agric. 32,pp. 149–165.
Jandl, R., Rodeghiero, M., Martinez, C., Cotrufo, M.F., Bampa, F., van Wesemael, B., Harrison, R.B., Guerrini, I.A., deB Richter, D., Jr. and Rustad, L., 2014. Current status, uncertainty and future needs in soil organic carbon monitoring. Sci. Total Environ. 468–469,pp. 376–383.
Kuhn, M., 2020. Caret: Classification and Regression Training. R package version 6.0-85. https://CRAN.R-project.org/package,caret.
Kumar, S, Lal R., 2011. Mapping the organic carbon stocks of surface soils using local spatial interpolator: Journal of Environmental Monitoring, 13(11),pp. 3128-3135.
Isaaks E.H. and Srivastava R.M., 1989. Spatial continuity measures for probabilistic and deterministic geostatistics. Math Geol 20, 313–341, https://doi.org/10.1007/BF00892982.
Loveland P. and Webb J., 2003. Is There Critical Level of Organic Matter in the Agricultural Soils of Temperate Regions: Review. Soil Tillage Research, 70,pp.1-18.
Matinfar H. R., Maghsodi Z., Mosavi S.R. and Rahmani A., 2021. Evaluation and Prediction of Topsoil organic carbon using Machine learning and hybrid models at a Field-scale, Catena, 202. 105258.
Mendoza Vega J., Karltun E., and Olsson M.. 2003. Estimations of amounts of soil organic carbon and fine root carbon in land use and land cover classes, and soil types of Chiapas highlands, Mexicoو Forest Ecology and Management, 177,pp. 191-206.
Mishra U., Lal R., Liu D. and Meirvenne M. V., 2010. Predicting the Spatial Variation of the Soil Organic Carbon Pool at a Regional Scale, Soil Sci. Soc. Am. J. 74,pp. 906-914.
Mueller T. G. and Pierce F. J., 2003. Soil carbon maps: Enhancing spatial estimates with simple terrain attributes at multiple scales, Soil Sci. Soc. Am. J. 67,pp.258–267.
Muhaimeed Ahmad S. and Taha Auras M. 2017. Using Remote Sensing and GIS techniques for predicting soil Organic Carbon in Southern Iraq. Global symposium on soil organic carbon, Rome, Italy, pp. 21-23.
Mulder V., De Bruin, S., Schaepman, M., and Mayr, T. 2011. The use of remote sensing in soil and terrain mapping, A review: Geoderma, 162(1-2),pp. 1-19.
Norouzi M., Ayoubi S., Jalalian A., Khademi H. and Dehghani A. A., 2009. Predicting rainfed wheat quality and quantity by artificial neural network using terrain and soil characteristics, Acta Agriculturae Scandinavica Section B- Soil and Plant Science,1)12),pp. 341-352.
Oldfield, E.E., Bradford, M.A. and Wood, S.A. 2019. Global meta-analysis of the relationship between soil organic matter and crop yields. Soil 5,pp. 15–32.
Omran, E.S.E. 2017. Rapid prediction of soil mineralogy using imaging spectroscopy. Eurasian Soil Sci. 50,pp. 597–612.
Pallant, J. 2020. SPSS survival manual: A step by step guide to data analysis using IBM SPSS. Routledge.
Prentice I. C., 2001. The carbon cycle and atmospheric carbon dioxide. In: Climate Change 2001: The Scientific Basis (IPCC). Cambridge: Cambridge University Press,pp. 184–237.
Ray S. S., Singh J. P., Dutta S., and Panigrahy S., 2002. analysis of within field variability of crop and soil using field and spectral information as a precursor to precision crop management. international archives of the photogrammetry, Remote Sensing, 34, pp.302-307.
Richards J. A., 2013. Remote Sensing Digital Image Analysis, Springer Heidelberg New York Dordrecht London.
Rotich H.K., Richard N., Onwonga, Mbau J. S. and Koech O. K. 2020. Projected Changes in Soil Organic Carbon Stocks over a 50-Year Period under Different Grazing Management Systems in SemiArid Grasslands of Kenya, Journal of Rangeland Science, 10(4),p.357-369.
Sainepo Bernice M., Gachene Charles K. and Karuma Anne. 2018. Effects of Land Use and Land Cover changes on Soil Organic Carbon and Total Nitrogen Stocks in the Olesharo Catchment,Narok County, Kenya. Journal of Rangeland Science, 8(3),pp.296-308.
Seybold C., Mausbach M., Karlen D. and Rogers H., 1997. Quantification of soil quality. National Soil Survey Center, Captter7.
Smith, K. A., 1999. After the Kyoto Protocol: can soil scientists make a useful contribution. Soil use and Management 15,pp. 71-75.
Stockmann, U., Padarian, J., McBratney, A., Minasny, B., de Brogniez, D., Montanarella, L., Hong, S.Y., Rawlins, B.G. and Field, D.J. 2015. Global soil organic carbon assessment. Glob. Food Secur. 6,pp. 9–16.
Sullivan D. G., Shaw J. N. and Rickman D., 2005. IKONOS Imagery to estimate surface soil property variability in two alabama physiographies, Soil, Water Manage. Conserv. 69,pp.1789-1798.
Terra J. A., Shaw J. N., Reeves D.W., Raper R.L., van Santen E. and Mask P.L., 2004. Soil carbon relationships with terrain attributes, electrical conductivity, and a soil survey in a coastal plain landscape, Soil Sci. 169,pp. 819-831.
Vågen T. G., Winowiecki L. A., Abegaz,A., and Hadgu K. M., 2013. Landsat-based approaches for mapping of land degradation prevalence and soil functional properties in Ethiopia: Remote Sensing of Environment, 134,pp. 266-27.
Wang B., Waters C., Orgill S., Cowie A., Clark A., Li Liu D., Simpson M., McGowen I., and Sides T., 2018. Estimating soil organic carbon stocks using different modelling techniques in the semi-arid rangelands of eastern Australia: Ecological indicators, 88,pp. 425-438.
Wu C., Wu J., Luo Y., Zhang L. and DeGloria S. D., 2009. Spatial prediction of soil organic matter content using cokriging with remotely sensed data, Soil Sci. Soc. Am. J. 73,pp. 1202-1208.
Yang C. and Everitt J.H., 2002. Relationships Between Yield Monitor Data and Airborne Multidate Multispectral Digital Imagery for Grain Sorghum. Precision Agriculture 3,pp. 373–388.
Zeraatpisheh, M, Ayoubi S., Jafari A., Tajik S. and Finke, P., 2019. Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran: Geoderma, 338, pp. 445-452.
