Performance evaluation of reflectance spectroscopy for estimation of soil organic carbon content in Zrebar lake watershed, Kurdistan province

Document Type : Complete scientific research article


1 Department of Soil Science and Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

2 Department of Soil Science and Engineering, Faculty of Agriculture, Isfahan University of Technology, Isfahan, Iran


Background and Objectives: Soil organic carbon (SOC), as a great constitute of soil organic matter (SOM), has an important role in chemical, physical and biological processes of soil. SOM or SOC is a key parameter of soil quality and a soil fertility indicator. SOM has an essential role in formation of soil aggregate and its stability, water and nutrients adsorption, water holding capacity, infiltration of air and water, hydraulic conductivity, soil water repellency and carbon sequestration. Various studies have shown that the quantity and quality of SOM can be affected by anthropogenic activities such as farming practices and other economic development activities. It has also been reported a high rate of SOM loss on eroded lands. Hence, monitoring temporal and spatial variation of SOM is essential for evaluating long-term soil productivity management. However, conventional soil sampling and chemical measurement of SOC, especially in large geographic scale, is tedious, time consuming and expensive. Therefore, rapid and precise assessment of SOC content can be useful in long-term management of soil. The objective of this study was to investigate the ability of soil visible-near infrared (Vis-NIR) spectroscopy for estimating SOC in Zrebar lake watershed of Marivan, Kurdistan province, Iran.
Materials and Methods: A total of 100 soil samples were collected from the studied region, with an area about 10718 hectares. The spectral reflectance and physicochemical properties of all soil samples were measured under laboratory controlled conditions. After recording of the spectra, different pre-processing methods were applied and compared. Then, pedo-transfer functions (PTFs) and specto-transfer functions (STFs) were developed to estimate SOC content using stepwise multiple linear regression (SMLR). The accuracy and reliability of the derived PTFs and STFs were evaluated using coefficient of determination (R2), normalized root mean square error (NRMSE), mean error (ME), index of agreement (d), and ratio of performance to deviation (RPD) statistics.
Results: Based on the results, soil organic carbon showed high and significant (significance level of 1%) correlations with spectral reflectance values at wavelengths 858 and 1916 nm. The results indicated that the derived PTFs had the higher accuracy (R2avg=0.83, NRMSEavg = 24.55%) to estimate SOC in comparison with the STFs (R2avg=0.44, NRMSEavg= 44.31%). However, SOC could be also fairly estimated by the derived specto-transfer functions (Ravg2=0.52, RPDavg= 1.44).The results also revealed that the Savitzki–Golay smoothing filter with 1st order derivative was the best spectral pre-processing method to reduce the effect of random noise and improve the calibration models.
Conclusion: Overall, the results indicated that although the performance of STFs was not superior to the corresponding PTFs for estimating SOC, but this approach can be used as a reasonable indirect method in case of unavailability of PTFs.


1.A1ef, K., and Nannipieri, K.P. 1995. Methods in Applied Soil Microbiology and Biochemistry. Academic Press, London, 576p.
2.Abasian, A., Golchin, A., and Sheklabadi, M. 2014. Some enzyme activities of two Histosols and their relationship with soil biological and chemical properties. J. Soil Biol. 2: 2. 111-124. (In Persian)
3.Ahmad Abadi, Z., and Qajar Espanloo, M. 2012. Effect of organic matter application on some of the soil physical properties.J. Water Soil Cons. 19: 2. 99-116.(In Persian)
4.Aichi, H., Fouad, Y., Walter, C., Viscarra Rossel, R.A., Chabaane, Z.L., and Sanaa, M. 2009. Regional predictions of soil organic carbon content from spectral reflectance measurements. Biosystems Engineering. 104: 3. 442-446.
5.Ajami, M., Khormali, F., Ayoubi, S.H., and Omrani, R.A. 2006. Changes in soil quality attributes by conversion of land use on a Loess hillslope in Golestan province, Iran. P 501-504, In: 18th International Soil Meeting (ISM) on Soil Sustaining Life on Earth, Maintaining Soil and Technology Proceedings, Soil Science Society of Turkey.
6.Babaei, F., Vaezi, A.R., and Taheri, M. Modeling of soil organic carbon content using topographic indices and soil characteristics in rainfed wheat lands.J. Water Soil Cons. 23: 3.111-129.(In Persian)
7.Babaeian, A., and Jalali, V.R. 2016. Estimating soil organic carbon using hyperspectral data in visible, near-infrared and shortwave-infrared (VIS-NIR-SWIR) range. J. Soil Manage. Sust. Prod. 6: 2. 65-82. (In Persian)
8.Babaeian, A., Homaee, M., and Norouzi, A.A. 2014. Assessing spectro-transfer functions and pedotransfer functions in predicting soil water retentions. J. Water Soil. 3: 2. 25-42.
9.Baumgardner, M.F., and Stoner, E.R. 1981. Soil mineralogical studies by remote sensing. Transactions of the 12th International Congress of Soil Science, Panel Discussion Papers. Pp: 419-444.
10.Baumgardner, M.F., Kristof, S.J., Johannsen, C.J., and Zachary, A.L. 1970. Effects of organic matter on the multispectral properties of soils. In Proceedings of the Indiana Academy of Science. 79: 413-422.
11.Ben-Dor, E. 2002. Quantitative remote sensing of soil properties. Advances in Agronomy. 75: 173-243.
12.Ben-Dor, E., and Banin, A. 1995. Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties. Soil Sci. Soc. Amer. J.59: 2.364-372.
13.Bremner, J.M. 1996. Nitrogen-Total.P 1085-1122, In: D.L. Sparks, A.L. Page, P.A. Helmke and R.H. Loeppert (eds.), Methods of soil analysis part 3. Chemical methods, SSSA Book Ser. 5. SSSA, Madison, WI, USA.
14.Cambardella, C.A., Gajda, A.M., Doran, J.W., Wienhold, B.J., Kettler, T.A., and Lal, R. 2001. Estimation of particulate and total organic matter by weight loss- on-ignition. Assessment methods for soil carbon, Pp: 349-359.
15.Chang, C.W., and Laird, D.A. 2002. Near-infrared reflectance spectroscopic analysis of soil C and N. Soil Science. 167: 2. 110-116.
16.Chen, L., Sheng-lu, Z., Shao-hua, W., Qing, Z. and Qi, D. 2014. Spectral response of different eroded soils in subtropical China: A case study in Changting County, China. J. Mater. Sci. 11: 697-707.
17.Clark, R.N., King, T.V.V., Klejwa, M., Swayze, G.A., and Vergo, N. 1990. High spectral resolution reflectance spectroscopy of minerals. J. Geophysic. Res. Solid Earth. 95: 8. 12653-12680.
18.Dadgar, M., Mahmoudi, Sh., Mahdian, M.H., Masih Abadi, M.H., and Skouti Oskouie, R. 2014. Estimating soil organic carbon using pedo-transfer functions in Damavand Rangelands. Iran. J. Res. Range. Des. 21: 3. 409-415. (In Persian)
19.Ding, G., Novak, J.M., Amarasiriwardena, D., Hunt, P.G., and Xing, B. 2002. Soil organic matter characteristics as affected by tillage management. Soil Sci. Soc. Amer. J.66: 2.421-429.
20.Flint, A.L., and Flint, L.E. 2002. Particle density. P 229-240, In: J.H. Dane and G.C. Topp (eds), Methods of soil analysis. Part 4. Physical methods, SSSA Book Ser. 5. SSSA, Madison, WI, USA.
21.Flint, L.E., and Flint, A.L. 2002. Porosity. P 241-254, In: J.H. Dane and G.C. Topp (eds), Methods of soil analysis. Part 4. Physical methods, SSSA Book Ser. 5. SSSA, Madison, WI, USA.
22.Gee, G.W., and Or, D. 2002. Particle-size analysis. P 255-294, In: JH Dane and GC Topp (eds), Methods of soil analysis. Part 4. Physical methods, SSSA Book Ser. 5. SSSA, Madison, WI, USA.
23.Grossman, R.B., and Reinsch, T.G. 2002. Bulk density and linear extensibility. P 201-228, In: JH Dane and GC Topp (eds), Methods of soil analysis: part 4. Physical methods, SSSA Book Ser. 5. SSSA, Madison, WI, USA.
24.Gupta, P.K. 2000. Soil, plant, water and fertilizer analysis. Agrobios, New Delhi, India.
25.Han, F., Hu, W., Zheng, J., Du, F., and Zhang, X. 2010. Estimating soil organic carbon storage and distribution in a catchment of Loess Plateau, China. Geoderma, 154: 261-266.
26.Hasani, A., Bahrami, H., Noroozi, A.A., and Oustan, Sh. 2014. Visible-near infrared reflectance spectroscopy for assessment of soil ‎properties in gypseous and calcareous soils‎. J. Water. Engin. Manage. 6: 2. 125-138.
27.Ingleby, H.R., and Crowe, T.G.2000. Reflectance models for predicting organic carbon in Saskatchewan soils. Canadian Agricultural Engineering.42: 2. 57-64.
28.Jaggi, W. 1976. Die Bestimmung der CO2-Bildungals MaN der bodenatmung uud der Carbonate im Boden. Zplanzenernaehr bodenkd. 56: 2. 26-38.
29.Janik, L.J., Merry, R.H., and Skjemstad, J.O. 1998. Can mid infrared diffuse reflectance analysis replace soil extractions? Austr. J. Exper. Agric.38: 681-696.
30.Karimi, S.A. 2017. Estimating of soil physical and mechanical properties using soil spectroscopy. Degree of M.Sc. in Soil Physical and Soil Conservation, Faculty of Agriculture, University of Kurdistan. (In Persian)
31.Khayamim, F., khademi, H., Stenberg, B., and Wetterlind, U. 2015. Capability of vis-NIR spectroscopy to predict selected chemical soil properties in Isfahan province. J. Water Soil Sci.19: 72. 81-91. (In Persian)
32.Kühnel, A., and Bogner, C. 2017. In-situ prediction of soil organic carbon by vis-NIR spectroscopy: an efficient use of limited field data. Europ. J. Soil Sci. 68: 5. 689-702.
33.Loeppert, R.H., and Suarez, DL. 1996. Carbonate and Gypsum. P 437-474,In: DL Sparks, AL Page, PA Helmke and RH Loeppert (eds), Methods of soil analysis part 3. Chemical methods, SSSA Book Ser. 5. SSSA, Madison, WI, USA.
34.Marinari, S., Masciandro, B., and Grego, S. 2000. Influence of organic and mineral fertilizer on soil physical properties. Geoderma. 72: 9-17.
35.Martin, P., Malley, D., Manning, G., and Fuller, L. 2002. Determination of soil organic carbon and nitrogen at the field level using near-infrared spectroscopy. Can. J. Soil Sci. 82: 4. 413-422.
36.McBratney A.B., Minasny B., Cattle S.R., and Vervoort R.W. 2002. From pedo-transfer functions to soil inference systems. Geoderma. 109: 41-73.
37.McCoy R.M. 2005. Field methods in remote sensing. A Division of Guilford Publications, Inc. Spring, New York, U.S, Pp: 67-87.
38.Najafi, Z., Golchin, A., and Shafiei, S. 2016. The effects of soil moisture levels on dynamics of organic carbon and nitrogen from alfalfa and barley residues. J. Water Soil Cons. 23: 4. 171-186.(In Persian)
39.Nanni, M.R., and Demattê, J.A.M. 2006. Spectral reflectance methodology in comparison to traditional soil analysis. Soil Sci. Soc. Amer. J. 70: 2. 393-407.‏
40.Nawar, S., and Mouazen, A.M. 2019. On-line vis-NIR spectroscopy prediction of soil organic carbon using machine learning. Soil and Tillage Research,190: 120-127.
41.Nelson, D.W., and Sommers, L.E. 1996. Total carbon, organic carbon and organic matter. P 961-1010, In: D.L. Sparks, A.L. Page, P.A. Helmke and R.H. Loeppert (eds), Methods of soil analysis part 3. Chemical methods, SSSA Book Ser. 5. SSSA, Madison, WI, USA.
42.Nocita, M., Stevens, A., De Brogniez, D., Bampa, F., Toth, G., Panagos, P., and Montanarella, L. 2012. Prediction of SOC content at European scale by coupling vis-NIR spectroscopy and a modified local PLSR algorithm. In: EGU General Assembly Conference Abstracts, 4247p.
43.Owji, A.R., Landi, A., and Hojati,S. 2017. Effects of grazing management on different forms of organic carbon
in Peneti plain of Izeh area in Khuzestan province. J. Water Soil Cons. 24: 3. 113-129. (In Persian)
44.Rhoades, J.D. 1996. Electrical conductivity and Total Dissolved Solids. P 417-436, In: D.L. Sparks, A.L. Page, P.A. Helmke and R.H. Loeppert (eds), Methods of soil analysis part 3. Chemical methods, SSSA Book Ser. 5. SSSA, Madison, WI, USA.
45.Schaefer, D., Feng, W., and Zou, X. 2009. Plant carbon inputs and environmental factors strongly affect soil respiration in a subtropical forest of southwestern China, Soil Biology and Biochemistry. Pp: 1-8.
46.Shangshi, L., Haihua, Sh., Songchao, C., Xia, Z., Asim, B., Xiaolin, J., Zhou, Sh., and Jingyun, F. 2019. Estimating forest soil organic carbon content using vis-NIR spectroscopy: Implications for large-scale soil carbon spectroscopic assessment. Geoderma, 348: 37-44.
47.Shepherd, K.D., and Walsh, M.G. 2002. Development of reflectance spectral libraries for characterization of soil properties. Soil Sci. Soc. Amer. J.66: 3. 988-998.
48.Shiferaw, A., and Hergarten, Ch. 2014. Visible near infra-red (vis-NIR) spectroscopy for predicting soil organic carbon in Ethiopia. J. Ecol. Natur. Environ. 6: 126-139.
49.Six, J., and Paustian, K. 2014. Aggregate-associated soil organic matter as an ecosystem property and a measurement tool. Soil Biology and Biochemistry. 68: A4-A9.
50.Stenberg, B. 2010. Effects of soil sample pretreatments and standardized rewetting as interacted with sand classes on Vis-NIR predictions of clay and soil organic carbon. Geoderma. 158: 1-2. 15-22.
51.Summers, D., Lewis, M., Ostendorf, B., and Chittleborough, D. 2011. Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties. Ecological Indicators. 11: 123-131.
52.Sun, H., Nelson, M., Chen, F., and Husch, J. 2007. Effect of structural water in clay minerals on the estimation of soil organic matter content by loss-on-ignition analytical method. GSA Denver Ann. Meeting. 39: 6. 218-248.
53.Thomas, J.W. 1996. Soil pH and Soil Acidity. P 475-490, In: D.L. Sparks, A.L. Page, P.A. Helmke and R.H. Loeppert (eds), Methods of soil analysis part 3. Chemical methods, SSSA Book Ser. 5. SSSA, Madison, WI, USA.
54.Viscarra Rossel, R.A. 2008. ParLeS software for chemometric analysis of spectroscopic data. Chemometrics and Intelligent Laboratory Systems. 90: 72-83.
55.Viscarra Rossel, R.A., McGlynn, R., and McBratney, A. 2006. Determining the composition of mineral-organic mixes using UV-vis-NIR diffuse reflectance spectroscopy. Geoderma. 137: 70-82.
56.Viscarra Rossel, R.A., Walter, C., and Fouad, Y. 2003. Assessment of two reflectance techniques for the quantification of field soil organic carbon. P 697-703. In: J. Stafford and A. Werner (Eds.), Precision Agriculture. Fourth European Conference on Precision Agriculture. Wageningen Academic Publishers, Berlin.
57.Walvoort, D.J.J., and McBratney, A.B. 2001. Diffuse reflectance spectrometry as a proximal sensing tool for precision agriculture. P 503-507. In: G. Grenier and S. Blackmore (Eds.), ECPA 2001, Third European Conference on Precision Agriculture, Vol. 1. Agro Montpellier.
58.Willmott, C.J. 1981. On the validation of models. Physical Geography. 2: 184-194.