Mapping soil organic carbon under erosion processes using remote sensing

  • Azamat Suleymanov Laboratory of soil science, Ufa Institute of Biology, Ufa Federal Research Centre, Russian Academy of Sciences, Ufa, Russia https://orcid.org/0000-0001-7974-4931
  • Ilyusya Gabbasova Laboratory of soil science, Ufa Institute of Biology, Ufa Federal Research Centre, Russian Academy of Sciences, Ufa, Russia
  • Ruslan Suleymanov Laboratory of soil science, Ufa Institute of Biology, Ufa Federal Research Centre, Russian Academy of Sciences, Ufa, Russia ; Department of Geodesy, Cartography and Geographic Information Systems, Bashkir State University, Ufa, Russia https://orcid.org/0000-0002-7754-0406
  • Evgeny Abakumov Faculty of Biology, Department of Applied Ecology, Saint Petersburg State University, Saint Petersburg, Russia ; Laboratory of Microbiological Monitoring and Bioremediation of Soils, All-Russia Insitute for Agricultural Microbiology, St. Petersburg, Russia https://orcid.org/0000-0002-5248-9018
  • Vyacheslav Polyakov Faculty of Biology, Department of Applied Ecology, Saint Petersburg State University, Saint Petersburg, Russia
  • Peter Liebelt Martin Luther University Halle-Wittenberg, Halle, Germany
Keywords: Soil organic carbon, remote sensing, sentinel, erosion, humic acids, 13C-NMR

Abstract

This study aimed to map soil organic carbon under erosion processes on an arable field in the Republic of Bashkortostan (Russia). To estimate the spatial distribution of organic carbon in the Haplic Chernozem topsoil, we applied Sentinel-2A satellite data and the linear regression method. We used 13 satellite bands and 15 calculated spectral indices for regression modelling. A regression model with an average prediction level has been created (R2 = 0.58, RMSE = 0.56, RPD = 1.61). Based on the regression model, cartographic materials for organic carbon content have been created. Water flows and erosion processes were determined using the calculated Flow Accumulation model. The relationship between organic carbon, biological activity, and erosion conditions is shown. The 13C-NMR spectroscopy method was used to estimate the content and nature of humic substances of different soil samples. Based on the 213C-NMR analysis, a correlation was established with the spectral reflectivity of eroded and non-eroded soils. It was revealed that the effect of soil organic carbon on spectral reflectivity depends not only on the quantity but also on the quality of humic substances and soil formation conditions.

References

Abakumov, E.V., Cajthaml, T., Brus, J. and Frouz, J. 2013. Humus accumulation, humification, and humic acid composition in soils of two post-mining chronosequences after coal mining. Journal of Soils and Sediments 13. (3): 491-500. https://doi.org/10.1007/s11368-012-0579-9

Abu-Hamdeh, N.H. 2003.Thermal properties of soils as affected by density and water content. Biosystems Engineering 86. 97-102. https://doi.org/10.1016/S1537-5110(03)00112-0

Angelopoulou, T., Tziolas, N., Balafoutis, A., Zalidis, G. and Bochtis, D. 2019. Remote sensing techniques for soil organic carbon estimation: A review. Remote Sensing 11. (6):676. https://doi.org/10.3390/rs11060676

Arinushkina, E.V. 1970. Soil Chemical Analysis Guide. Moscow, Moscow State University. (in Russian)

Bartholomeus, H., Schaepman, M.E., Kooistra, L., Stevens, A., Hoogmoed, W.B. and Spaargaren, O.C. 2008. Spectral reflectance based indices for soil organic carbon quantification. Geoderma 145. (1-2): 28-36. https://doi.org/10.1016/j.geoderma.2008.01.010

Baude, M., Meyer, B.C. and Schindewolf, M. 2019. Land use change in an agricultural landscape causing degradation of soil based ecosystem services. Science of the Total Environment 659. 1526-1536. https://doi.org/10.1016/j.scitotenv.2018.12.455

Ben-Dor, E., Inbar, Y. and Chen, Y. 1997. The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400-2500 nm) during a controlled decomposition process. Remote Sensing of Environment 61. (1): 1-15. https://doi.org/10.1016/S0034-4257(96)00120-4

Bhunia, G.S., Shit, P.K. and Pourghasemi, H.R. 2019. Soil organic carbon mapping using remote sensing techniques and multivariate regression model. Geocarto International 34. (2): 215-226. https://doi.org/10.1080/10106049.2017.1381179

Castaldi, F., Chabrillat, S., Chartin, C., Genot, V., Jones, A.R. and van Wesemael, B. 2018. Estimation of soil organic carbon in arable soil in Belgium and Luxembourg with the LUCAS topsoil database. European Journal of Soil Science 69. (4): 592-603. https://doi.org/10.1111/ejss.12553

Castaldi, F., Hueni, A., Chabrillat, S., Ward, K., Buttafuoco, G., Bomans, B., Vreys, K., Brell, M. and van Wesemael, B. 2019. Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands. ISPRS Journal of Photogrammetry and Remote Sensing 147. 267-282. https://doi.org/10.1016/j.isprsjprs.2018.11.026

Chang, C.-W., Laird, D., Mausbach, M. and Hurburgh, C. 2001. Near-infrared reflectance spectroscopy - principal components regression analyses of soil properties. Soil Science Society of America Journal 65. (2): 480-490. https://doi.org/10.2136/sssaj2001.652480x

Chen, D., Chang, N., Xiao, J., Zhou, Q. and Wu, W. 2019. Mapping dynamics of soil organic matter in croplands with MODIS data and machine learning algorithms. Science of the Total Environment 669. 844-855. https://doi.org/10.1016/j.scitotenv.2019.03.151

Chukov, S.N., Ejarque, E. and Abakumov, E.V. 2017. Characterization of humic acids from tundra soils of northern Western Siberia by electron paramag netic resonance spectroscopy. Eurasian Soil Science 50. (1): 30-33. https://doi.org/10.1134/S1064229317010057

Chukov, S.N., Lodygin, E.D. and Abakumov, E.V. 2018. Application of 13C NMR spectroscopy to the study of soil organic matter: A review of publica tions. Eurasian Soil Science 51. (8): 889-900. https://doi.org/10.1134/S1064229318080021

Ciric, V., Manojlovic, M., Nesic, Lj. and Belic, M. 2012. Soil dry aggregate size distribution: effects of soil type and land use. Journal of Soil Science and Plant Nutrition 12. (4): 689-703. https://doi.org/10.4067/S0718-95162012005000025

Conte, P., Di Stefano, C., Ferro, V., Laudicina, V.A. and Palazzolo, E. 2017. Assessing hydrological connectivity inside a soil by fast-field-cycling nuclear magnetic resonance relaxometry and its link to sediment delivery processes. Environmental Earth Sciences 76. (15):526. https://doi.org/10.1007/s12665-017-6861-9

Desprats, J.F., Raclot, D., Rousseau, M., Cerdan, O., Garcin, M., Bissonnais, Y.L., Slimane, A.B., Fouche, J. and Monfort‐Climent, D. 2013. Mapping linear erosion features using high and very high resolution satellite imagery. Land Degradation & Development24. (1): 22-32. https://doi.org/10.1002/ldr.1094

Dou, X., Wang, X., Liu, H., Zhang, X., Meng, L., Pan, Y., Yu, Z. and Cui, Y. 2019. Prediction of soil organic matter using multi-temporal satellite images in the Songnen Plain, China. Geoderma 356. 113896. https://doi.org/10.1016/j.geoderma.2019.113896

Dube, T., Muchena, R., Masocha, M. and Shoko, C. 2018. Estimating soil organic and aboveground woody carbon stock in a protected dry Miombo ecosystem, Zimbabwe: Landsat 8 OLI data applica tions. Physics and Chemistry of the Earth, Parts A/B/C105. 154-160. https://doi.org/10.1016/j.pce.2018.03.007

Escadafal, R. 1989. Remote sensing of arid soil sur face color with Landsat thematic mapper. Advances in Space Research 9. (1): 159-163. https://doi.org/10.1016/0273-1177(89)90481-X

Emadi, M., Taghizadeh-Mehrjardi, R., Cherati, A., Danesh, M., Mosavi, A. and Scholten, T. 2020. Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran. Remote Sensing 12. (14):2234. https://doi.org/10.3390/rs12142234

Frankl, A., Prêtre, V., Nyssen, J. and Salvador, P.-G. 2018. The success of recent land management efforts to reduce soil erosion in northern France. Geomorphology 303. 84-93. https://doi.org/10.1016/j.geomorph.2017.11.018

Gabbasova, I.M., Suleimanov, R.R., Khabirov, I.K., Komissarov, M.A., Fruehauf, M., Liebelt, P., Garipov, T.T., Sidorova, L.V. and Khaziev, F.Kh.2016. Temporal changes of eroded soils depending on their agricultural use in the southern Cis-Ural region. Eurasian Soil Science 49. (10): 1204-1210. https://doi.org/10.1134/S1064229316100070

Gholizadeh, A., Žižala, D., Saberioon, M. and Borůvka, L. 2018. Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging. Remote Sensing of Environment 218. 89-103. https://doi.org/10.1016/j.rse.2018.09.015

Gitelson, A.A., Kaufman, Y.J. and Merzlyak, M.N. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment 58. (3): 289-298. https://doi.org/10.1016/S0034-4257(96)00072-7

Golosov, V., Yermolaev, O., Rysin, I., Vanmaercke, M., Medvedeva, R. and Zaytseva, M. 2018. Mapping and spatial-temporal assessment of gully density in the Middle Volga region, Russia. Earth Surface Processes and Landforms 43. (13): 2818-2834. https://doi.org/10.1002/esp.4435

Gomez, C., Coulouma, G. and Lagacherie, P. 2012. Regional predictions of eight commonsoil properties and their spatial structures from hyperspectral Vis-NIR data. Geoderma 189-190. 176-185. https://doi.org/10.1016/j.geoderma.2012.05.023

Gopp, N.V., Nechaeva, T.V., Savenkov, O.A., Smirnova, N.V. and Smirnov, V.V. 2017. Indicative capacity of NDVI in predictive mapping of the properties of plow horizons of soils on slopes in the south of Western Siberia. Eurasian Soil Science 50. 1332-1343. https://doi.org/10.1134/S1064229317110060

Guo, B., Yang, G., Zhang, F., Han, F. and Liu, C. 2018. Dynamic monitoring of soil erosion in the upper Minjiang catchment using an improved soil loss equation based on remote sensing and geographic information system. Land Degradation & Development 29. (3): 521-533. https://doi.org/10.1002/ldr.2882

Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. and Ferreira, L.G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment39. (1): 195-213. https://doi.org/10.1016/S0034-4257(02)00096-2

Huete, A.R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25. (3): 295-309. https://doi.org/10.1016/0034-4257(88)90106-X

IUSS Working Group WRB, 2014. World reference base for soil resources. In: International Soil Classification System for Naming Soils and Creating Legends for Soil Maps. World Soil Resources Reports No. 106. Rome, FAO.

Jaber, S.M., Lant, C.L. and Al-Qinna, M.I. 2011. Estimating spatial variations in soil organic carbon using satellite hyperspectral data and map alge bra. International Journal of Remote Sensing 32. (18): 5077-5103. https://doi.org/10.1080/01431161.2010.494637

Jenson, S.K. and Domingue, J.O. 1988. Extracting topographic structure from digital elevation data for geographic information system analysis. Photogrammetric Engineering and Remote Sensing 54. (11): 1593-1600.

Kattsov, V.M. (ed.) 2017. Report on Climate Risks in the Russian Federation. Saint Petersburg, Saint Petersburg State University. (in Russian)

Khan, J., Aelst, S.V., and Zamar, R. 2010. Fast robust estimation of prediction error based on resampling. Computational Statistics & Data Analysis 54. 3121-3130. https://doi.org/10.1016/j.csda.2010.01.031

Khaziev, F.Kh. (ed.) 1995. Soils of Bashkortostan. Vol. 1. Ecologic-Genetic and Agro-productive Characterization. Ufa, Gilem. (in Russian).

Lal, R., Kimble, J.M., Follet, R.F. and Stewart, B.A. (eds.) 2001. Assessment Methods for Soil Carbon. Boca Raton, FL, USA, Lewis Publishers.

Leh, M., Bajwa, S. and Chaubey, I. 2013. Impact of land use change on erosion risk: An integrated remote sensing, geographic information system and model ling methodology. Land Degradation & Development 24. (5): 409-421. https://doi.org/10.1002/ldr.1137

Lodygin, E.D., Beznosikov, V.A. and Vanchikova, E.V. 2001. Functional groups of fulvic acids from gleyic peaty-podzolic soil. Eurasian Soil Science 34. (4): 382-386.

Lodygin, E.D., Beznosikov, V.A. and Vasilevich, R.S. 2014. Molecular composition of humic substances in tundra soils (13C-NMR spectroscopic study). Eurasian Soil Science 47. 400-406. https://doi.org/10.1134/S1064229314010074

Magliulo, P., Russo, F. and Lo Curzio, S. 2020. Detection of permanently eroded land surfaces through multi-temporal analysis of Landsat data: A case study from an agricultural area in southern Italy. Environmental Earth Sciences 79. (3): 73. https://doi.org/10.1007/s12665-020-8814-y

Marsett, R.C., Qi, J., Heilman, P., Biedenbender, S.H., Watson, M.C., Amer, S., Weltz, M., Goodrich, D. and Marsett, R. 2006. Remote sensing for grassland management in the arid Southwest. In Rangeland Ecology and Management 59. (5): 530-540. https://doi.org/10.2111/05-201R.1

Mouazen, A.M., Maleki, M.R., de Baerdemaeker, J. and Ramon, H. 2007. On-line measurement of some selected soil properties using a VIS-NIR sensor. Soil and Tillage Research 93. (1): 13-27. https://doi.org/10.1016/j.still.2006.03.009

Mulder, V.L., de Bruin, S., Schaepman, M.E. and Mayr, T.R. 2011. The use of remote sensing in soil and terrain mapping - A review. Geoderma 162. (1): 1-19. https://doi.org/10.1016/j.geoderma.2010.12.018

Nampak, H., Pradhan, B., Rizeei, H.M. and Park, H.-J. 2018. Assessment of land cover and land use change impact on soil loss in a tropical catchment by using multi-temporal SPOT-5 satellite images and Revised Universal Soil Loss Equation model. Land Degradation & Development 29. (10): 3440-3455. https://doi.org/10.1002/ldr.3112

Nellis, M.D. and Briggs, J.M. 1992. Transformed vegetation index for measuring spatial variation in drought impacted biomass on Konza Prairie, Kansas. Transactions of the Kansas Academy of Science 95. (1-2): 93-99. https://doi.org/10.2307/3628024

Nocita, M., Stevens, A., van Wesemael, B., Aitkenhead, M., Bachmann, M., Barthès, B., Ben Dor, E., Brown, D.J., Clairotte, M., Csorba, A., Dardenne, P., Demattê, J.A. M., Genot, V., Guerrero, C., Knadel, M., Montanarella, L., Noon, C., Ramirez-Lopez, L., Robertson, J., Sakai, H., Soriano-Disla, J.M., Shepherd, K.D., Stenberg, B., Towett, E.K., Vargas, R. and Wetterlind, J. 2015. Soil spectroscopy: An alternative to wet chemistry for soil monitoring. Advances in Agronomy 132. 139-159. https://doi.org/10.1016/bs.agron.2015.02.002

Panagos, P., Borrelli, P. and Meusburger, K. 2015. A new European slope length and steepness factor (LS-Factor) for modelling soil erosion by water. Geosciences 5. (2): 117-126. https://doi.org/10.3390/geosciences5020117

Phinzi, K. and Ngetar, N.S. 2019. The assessment of water-borne erosion at catchment level using GIS-based RUSLE and remote sensing: A review. International Soil and Water Conservation Research 7. (1): 27-46. https://doi.org/10.1016/j.iswcr.2018.12.002

Polyakov, V. and Abakumov, E.V. 2020. Humic acids isolated from selected soils from the Russian Arctic and Antarctic: Characterization by two-dimensional 1H-13C HETCOR and 13C CP/Mas NMR spectros copy. Geosciences 10. (1): 15. https://doi.org/10.3390/geosciences10010015

Pouget, M., Madeira, J., Le Floch, E. and Kamal, S. 1990. Caracteristiques spectrales des surfaces sableuses de la region cotiere Nord-Ouest de l'Egypte: Application aux donandes satellitaires SPOT. 2eme JoumCes de Tanddetection: Caracterisation et suivi des milieux terrestres en regions arides et tropicales. 4-6/12/1990. Collection Colloques et Seminaires, Paris, ORSTOM.

Prudnikova, E.Yu. and Savin, I.Yu. 2015. Satellite assessment of dehumification of arable soils in Saratov region. Eurasian Soil Science 48. (5): 533-539. https://doi.org/10.1134/S1064229315050075

Quideau, S.A., Anderson, M.A., Graham, R.C., Chadwick, O.A. and Trumbore, S.E. 2000. Soil organic matter processes: Characterization by 13C NMR and 14C measurements. Forest Ecology and Management 138. (1): 19-27. https://doi.org/10.1016/S0378-1127(00)00409-6

R Development Core Team, 2015. R: A Language and Environment for Statistical Computing. Vienna, R Foundation for Statistical Computing. Available at http://www.Rproject.org/

Rock, B.N., Williams, D.L., and Vogelmann, J.E. 1985. Field and airborne spectral characterization of suspected damage in red spruce (picea rubens) from Vermont. NASA Technical Reports, ID 19860052270. Greenbelt, MD, USA. NASA Goddard Space Flight Center. Available at https://ntrs.nasa.gov/search.jsp?R=19860052270

Rouse, J.W. Jr., Haas, R.H., Schell, J.A. and Deering, D.W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication 351. 309. Greenbelt,MD, USA. NASA Goddard Space Flight Center

Rozhkov, V.A. (ed.) 2006. Soil Science. Moscow, Nauka. (in Russian)

RStudio, 2015. RStudio: Integrated Development Environment for R. Boston, MA. USA, Available at http://www.r-studio.com

Rumpel, C., Chaplot, V., Ciais, P., Chabbi, A., Bouahom, B. and Valentin, C. 2014. Composition changes of eroded carbon at different spatial scales in a tropical watershed suggest enrichment of degraded material during transport. Biogeosciences 11. (12): 3299-3305. https://doi.org/10.5194/bg-11-3299-2014

Savin, I.Yu., Zhogolev, A.V. and Prudnikova, E.Yu.2019. Modern trends and problems of soil mapping. Eurasian Soil Science 52. (5): 471-480. https://doi.org/10.1134/S1064229319050107

Sepuru, T.K. and Dube, T. 2018. An appraisal on the progress of remote sensing applications in soil erosion mapping and monitoring. Remote Sensing Applications: Society and Environment 9. 1-9. https://doi.org/10.1016/j.rsase.2017.10.005

Simpson, M.J., Otto, A. and Feng, X. 2008. Comparison of solid-state Carbon-13 nuclear magnetic resonance and organic matter biomarkers for assessing soil organic matter degradation. Soil Science Society of America Journal 72. (1): 268-276. https://doi.org/10.2136/sssaj2007.0045

Sobol, N.V., Gabbasova, I.M. and Komissarov, M.A. 2015. Impact of climate changes on erosion process es in Republic of Bashkortostan. Arid Ecosystems 5. (4): 216-221. https://doi.org/10.1134/S2079096115040137

Sokolov, A.V. 1975. Agrochemical Methods of Soil Studies. Moscow, Nauka. (in Russian)

Suleymanov, A.R. 2019. Geomorphometric and geoin formation approach to meliorative evaluation of the territory. In Climate Change Impacts on Hydrological Processes and Sediment Dynamics: Measurement, Modelling and Management. Eds.: Chalov, S., Golosov, V., Li, R. and Tsyplenkov, A. Cham, Springer International Publishing, 72-75. https://doi.org/10.1007/978-3-030-03646-1_14

Suleymanov, R., Saifullin, I., Komissarov, M., Gabbasova, I., Suleymanov, A. and Garipov, T. 2019. Effect of phosphogypsum and turkey litter on the erodibility of agrochernozems of the southern Cis-Ural (Russia) under artificial heavy rainfall. Soil & Environment 38. (1): 81-89. https://doi.org/10.25252/SE/19/71730

Suleymanov, R., Zaykin, S., Suleymanov, A., Abakumov, E. and Kostecki, J. 2020a. Changes in basic soil physical properties of agrochernozyems under no-till conditions.Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi 30. 963-972. https://doi.org/10.29133/yyutbd.754479

Suleymanov, R., Yaparov, I., Saifullin, I., Vildanov, I., Shirokikh, P., Suleymanov, A., Komissarov, M., Liebelt, P., Nigmatullin, A. and Khamidullin, R. 2020b. The current state of abandoned lands in the northern forest-steppe zone at the Republic of Bashkortostan (Southern Ural, Russia). Spanish Journal of Soil Science 10. (1): 29-44.

Swift, R.S. 1996. Organic matter characterization. In Methods of Soil Analysis, Part 3. Chemical Methods. Eds.: Sparks, D.L., Page, A.L., Helmke, P.A. and Loepert, R.H., SSSA Book Series No 5., Madison, WI, USA, SSSA-ASA, 1011-1069. https://doi.org/10.2136/sssabookser5.3.c35

Tucker, C.J. 1979. Red and photographic infrared lin ear combinations for monitoring vegetation. Remote Sensing of Environment 8. (2): 127-150. https://doi.org/10.1016/0034-4257(79)90013-0

Vaudour, E., Gomez, C., Fouad, Y. and Lagacherie, P. 2019. Sentinel-2 image capacities to predict common topsoil properties of temperate and Mediterranean agroecosystems. Remote Sensing of Environment 223. 21-33. https://doi.org/10.1016/j.rse.2019.01.006

Viscarra Rossel, R.A., Minasny, B., Roudier, P. and McBratney, A.B. 2006a. Colour space models for soil science. Geoderma 133. (3-4): 320-337. https://doi.org/10.1016/j.geoderma.2005.07.017

Viscarra Rossel, R.A., Walvoort, D.J.J., McBratney, A.B., Janik, L.J. and Skjemstad, J.O. 2006b. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131. (1): 59-75. https://doi.org/10.1016/j.geoderma.2005.03.007

Wang, L., Huang, J., Du, Y., Hu, Y. and Han, P. 2013. Dynamic assessment of soil erosion risk using Landsat TM and HJ satellite data in Danjiangkou Reservoir Area, China. Remote Sensing 5. (8): 3826-3848. https://doi.org/10.3390/rs5083826

Xiao, X., Zhang, Q., Braswell, B., Urbanski, S., Boles, S., Wofsy, S., Moore, B. and Ojima, D. 2004. Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sensing of Environment 91. (2): 256-270. https://doi.org/10.1016/j.rse.2004.03.010

Yang, X., Zhang, X., Lv, D., Yin, S., Zhang, M., Zhu, Q., Yu, Q. and Liu, B. 2020. Remote sensing estimation of the soil erosion cover-management factor for China's Loess Plateau. Land Degradation & Development 31. (11): 1-14. https://doi.org/10.1002/ldr.3577

Yermolaev, O.P. 2017. Geoinformation mapping of soil erosion in the Middle Volga region. Eurasian Soil Science 50. (1): 118-131. https://doi.org/10.1134/S1064229317010070

Published
2021-04-06
How to Cite
SuleymanovA., GabbasovaI., SuleymanovR., AbakumovE., PolyakovV., & LiebeltP. (2021). Mapping soil organic carbon under erosion processes using remote sensing. Hungarian Geographical Bulletin, 70(1), 49-64. https://doi.org/10.15201/hungeobull.70.1.4
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Articles