Journal of Mountain Science

, Volume 11, Issue 3, pp 697–707 | Cite as

Spectral response of different eroded soils in subtropical china: A case study in Changting County, China

  • Chen Lin
  • Sheng-lu ZhouEmail author
  • Shao-hua Wu
  • Qing Zhu
  • Qi Dang


Hyper-spectral data is widely used to determine soil properties. However, few studies have explored the soil spectral characteristics as response to soil erosion. This study analysed the spectral response of different eroded soils in subtropical China, and then identify the spectral characteristics and soil properties that better discriminate soils with different erosion degrees. Two methods were compared: direct identification by inherent spectral characteristics and indirect identification by predictions of critical soil properties. Results showed that the spectral curves for different degrees of erosion were similar in morphology, while overall reflectance and characteristics of specific absorption peaks were different. When the first method is applied, some differences among different eroded groups were found by integration of associated indicators. However, the index of such indicators showed apparent mixing and crossover among different groups, which reduced the accuracy of identification. For the second method, the correlation between critical soil properties, such as soil organic matter (SOM), iron and aluminium oxides and reflectance spectra, was analysed. The correlation coefficients for the moderate eroded group were primarily between −0.3 to −0.5, which were worse than the other two groups. However, the maximum value of R 2 was obtained as 0.86 and 0.94 for the non-apparent eroded and the severe group. Furthermore, these two groups also showed some differences in the spectral response of iron complex state (Fep), Aluminium amorphous state (Alo) and the modelling results for soil organic matter (SOM). The study proved that it is feasible to identify different degrees of soil erosion by hyper-spectral data, and that indirect identification by modelling critical soil properties and reflectance spectra is much better than direct identification. These results indicate that hyper-spectral data may represent a promising tool in monitoring and modelling soil erosion.


Soil erosion Erosion degree Hyperspectral Aluminium oxides Iron oxide SOM 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Asis D, Omasa K (2007) Estimation of vegetation parameter for modeling soil erosion using linear Spectral Mixture Analysis of Landsat ETM data. ISPRS Journal of Photogrammetry and Remote Sensing 62(4): 309–324. DOI: 10.1016/j.isprsjprs.2007.05.013CrossRefGoogle Scholar
  2. Ben-Dor E (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: 1–15. DOI: 10.1016/S0034-4257(96)00120-4CrossRefGoogle Scholar
  3. Ben-Dor E, Banin A (1995) Near infrared analysis (NIRA) as a rapid method to simultaneously evaluate several soil properties. Soil Science Society of America Journal 59(2): 364–372. DOI: 10.2136/sssaj1995.03615995005900020014xCrossRefGoogle Scholar
  4. Ben-Dor E (2002) Quantitative remote sensing of soil properties. Advances in Agronomy 75: 173–243. DOI:10.1016/S0065-2113(02)75005-0CrossRefGoogle Scholar
  5. Cai ZF, Huang YH (2002) Soil erosion and degeneration in the south area of Fujian Province-Influence of soil erosion on the soil profile construct and texture. Fujian Journal of Agricultural Sciences 17(2): 65–68. (In Chinese)Google Scholar
  6. Chang CW, Laird DA, Mausbach MJ, et al. (2001) Near-infrared reflectance spectroscopy-Principal components regression analyses of aoil properties. Soil Science Society of America Journal 65(2): 480–490. DOI: 10.2136/sssaj2001.652480xCrossRefGoogle Scholar
  7. Das B (2007) Reconstruction of historical productivity using visible-nearinfrared (VNIR) refletance properties from borealand saline lake sediments. Aquatic Ecology. 41: 209–220. DOI: 10.1007/s10452-006-9071-1CrossRefGoogle Scholar
  8. Desprats JF, Raclot D, Rousseau M, et al. (2013) Mapping linear erosion features using high and very high resolution satellite imagery. Land Degradation & Development 24(1): 22–32. DOI: 10.1002/ldr.1094CrossRefGoogle Scholar
  9. Galvão LS, Formaggio AR (2008) Relationships between the mineralogical and chemical composition of tropical soils and topography from hyperspectral remote sensing data. ISPRS Journal of Photogrammetry and Remote Sensing 63(2): 259–271. DOI: 10.1016/j.isprsjprs.2007.09.006CrossRefGoogle Scholar
  10. Hummel JW, Sudduth KA, Hollinger SE (2001) Soil moisture and matter prediction of surface and subsurface soil using NIR soil. Computers and Electronics in Agriculture 32(2):149–165. DOI: 10.1016/S0168-1699(01)00163-6CrossRefGoogle Scholar
  11. Hunt GR, Ashley RP (1979) Spectra of altered rocks in the visible and near infrared. Economic Geology 74(7): 1613–1629. DOI: 10.2113/gsecongeo.74.7.1613CrossRefGoogle Scholar
  12. Huang YF, Liu TH (1989) The relation between spectral reflectance characteristics and soil properties — A case of southern China. Chinese Journal of Soil Science 4(4): 158–160. (In Chinese)Google Scholar
  13. Ji GS, Xu BB (1987) Reflectance of soil clay minerals and its application in pedology. Acta Pedologica Sinica 24(1): 67–76. (In Chinese)Google Scholar
  14. Ji JF, Balsam WL, Chen J, et al. (2002) Rapid and quantitative measurement of hematite and goethite in the Chinese Loess-Paleosol sequence by diffuse reflectance spectroscopy. Clays and Clay Minerals 50: 208–216. DOI: 10.1346/000986002760832801CrossRefGoogle Scholar
  15. Kooistra L, Wehrens R, Leuven RSEW, et al. (2001) Possibilities of visible-near-infrared spectroscopy for the assessment of soil contamination in river floodplains. Analytica Chimica Acta 446: 97–105. DOI: 10.1016/S0003-2670(01)01265-XCrossRefGoogle Scholar
  16. Ladoni M, Bahrami H, Alavipanah SK, et al. (2010). Estimating soil organic carbon from soil reflectance: a review. Precision Agriculture. 11(1): 82–99. DOI: 10.1007/s11119-009-9123-3CrossRefGoogle Scholar
  17. Liu HJ, Zhang YZ, Zhang B (2009) Novel hyperspectral reflectance models for estimating black-soil organic matter in Northeast China. Environmental Monitoring and Assessment 154: 147–154. DOI: 10.1007/s10661-008-0385-4CrossRefGoogle Scholar
  18. Liu WF, Baret F, Gu XF, et al. (2002) Relating soil surface moisture to reflectance. Remote Sensing of Environment 81: 238–246. DOI: PIIS0034-4257(01)00347-9CrossRefGoogle Scholar
  19. Lu RK (2000) Agricultural Chemical Analysis of Soil. China Agricultural Science and Technology Press, Beijing, China. pp 272–282. (In Chinese)Google Scholar
  20. Mathews HL, Cunningham RL, Petersen GW (1973) Spectral reflectance of selected Pennsylvania soils. Soil Science Society of America Journal 37(3): 421–424.CrossRefGoogle Scholar
  21. Nanni MR, Dematte JAM (2006) Spectral reflectance methodology in comparison to traditional soil analysis. Soil Science Society of America Journal 70(2): 393–407. DOI: 10.2136/sssaj2003.0285CrossRefGoogle Scholar
  22. Nduwamungu C, Ziadi N (2009) Near-infrared reflectance spectroscopy prediction of soil properties: Effects of sample cups and preparation. Soil Science Society of America Journal 73(6):1896–1903. DOI: 10.2136/sssaj2008.0213CrossRefGoogle Scholar
  23. Shoshany M (2012) Identifying desert thresholds by mapping inverse erodibility and recovery potentials in patch patterns using spectral and morphological algorithms. Land Degradation & Development 23(4): 331–338. DOI: 10.1002/ldr.2146CrossRefGoogle Scholar
  24. Stoner ER, Baumgardner MF (1981) Characteristic variations in reflectance on surface soils. Soil Science Society of America Journal 45: 1161–1165. DOI: 10.2136/sssaj1981.03615995004500060031xCrossRefGoogle Scholar
  25. Wang WM, Chen MH, Lin JL, et al. (2005) Monitoring soil and water loss dynamics and its management measures in Changting County. Bulletin of Soil and Water Conservation 25(4):73–77. (In Chinese)Google Scholar
  26. Wang XL, Wang Q, Wu CQ, et al. (2012) A method coupled with remote sensing data to evaluate non-point source pollution in the Xin’anjiang catchment of China. Science of the Total Environment 430: 132–143. DOI: 10.1016/j.scitotenv.2012.04.052CrossRefGoogle Scholar
  27. Whiting ML, Li L, Ustin SL (2004) Predicting water content using Gaussian model on soil spectra. Remote Sensing of Environment 89: 535–552. DOI: 10.1016/j.rse.2003.11.009CrossRefGoogle Scholar
  28. Wu YJ, Chen X, Wu Q, et al. (2005) Feasibility of reflectance spectroscopy for the assessment of soil mercury contamination. Environmental Science & Technology 39: 873–878. DOI: 10.1021/es0492642CrossRefGoogle Scholar
  29. Wu YZ, Chen J, Ji JF, et al. (2007) A mechanism study of reflectance spectroscopy for investigating heavy metals in soils. Soil Science Society of America Journal 71(3): 918–926. DOI: 10.2136/sssaj2006.0285CrossRefGoogle Scholar
  30. Xiong Y (1985) Research of Soil Colloids. Science Press, Beijing, China. pp 245–261. (In Chinese)Google Scholar
  31. Xu BB (2000) The reflectance spectrum of soil profile. Soils 6: 281–287. (In Chinese)Google Scholar
  32. Yan SX, Zhang B, Zhao YC, et al. (2003) Summarizing the VISNIR Spectra of Minerals and Rocks. Remote Sensing Technology and Application. 18(4): 191–201. DOI: 10.3969/j.issn.1004-0323.2003.04.002Google Scholar
  33. Zhao QG (2002) Mechanism, Temporal-Spatial Changes and Controlling Countermeasures of Soil Degradation in Hilly Red Soil Region of Southeastern China. Science Press, Beijing, China. (In Chinese).Google Scholar
  34. Zhao QG, Shi XZ (2007) The Overview of Soil Resources. Science Press, Beijing, China. (In Chinese).Google Scholar
  35. Zhou W, Ji JF, Williams B, et al. (2007) Determination of goethite and hematite in red clay by diffuse reflectance spectroscopy. Geological Journal of China Universities 13(4): 730–736. DOI: 1006-7493(2007)13:4〈730:LYMFSG〉2.0.TX;2-OGoogle Scholar
  36. CAMO Software AS (2006) The Unscrambler Program Operation. Available online: (Accessed on 20 May 2011)Google Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Chen Lin
    • 1
  • Sheng-lu Zhou
    • 2
    Email author
  • Shao-hua Wu
    • 2
  • Qing Zhu
    • 1
  • Qi Dang
    • 3
  1. 1.State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and LimnologyChinese Academy of SciencesNanjingChina
  2. 2.Department of Land Resources and Tourism Sciences School of Geographic and Oceanographic SciencesNanjing UniversityNanjingChina
  3. 3.State Key Laboratory of Soil and Sustainable Agriculture, Nanjing Insti-tute of Soil ScienceChinese Academy of SciencesNanjingChina

Personalised recommendations