Hyper-spectral response and estimation model of soil degradation in Kenli County, the Yellow River Delta

Autoři: Chunyan Chang aff001;  Fen Lin aff001;  Xue Zhou aff001;  Gengxing Zhao aff001
Působiště autorů: National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, Tai’an, China aff001;  Qingdao Hengyuande Real Estate Appraisal Limited Company, Qingdao, China aff002;  Department of Agricultural and Biological Engineering, University of Florida, Gainesville, Florida, United States of America aff003
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0227594


The ecological environment of the Yellow River Delta is fragile, and the soil degradation in the region is serious. Therefore it is important to discern the status of the soil degradation in a timely manner for soil conservation and utilization. The study area of this study was Kenli County in the Yellow River Delta of China. First, physical and chemical data of the soil were obtained by field investigations and soil sample analyses, and the hyper-spectra of air-dried soil samples were obtained via spectrometer. Then, the soil degradation index (SDI) was constructed by the key indicators of soil degradation, including pH, SSC, OM, AN, AP, AK, and soil texture. Next, according to a cluster analysis, soil degradation was divided into the following three grades: light degradation, moderate degradation, and heavy degradation. Moreover, the spectral characteristics of soil degradation were analyzed, and an estimation model of SDI was established by multiple stepwise regression. The results showed that the overall level of reflectance spectra increased with increased degree of soil degradation, that both derivative transformation and waveband reorganization could enhance the spectral information of soil degradation, and that the correlation between SDI and the spectral parameter of (Rλ2+Rλ1)/(Rλ2-Rλ1) was the highest among all the spectral parameters studied. On this basis, the optimum estimation model of SDI was established with the correlation coefficient of 0.811. This study fully embodies the potential of hyper-spectral technology in the study of soil degradation and provides a technical reference for the rapid extraction of information from soil degradation. Additionally, the study area is typical and representative, and thus can indirectly reflect the soil degradation situation of the whole Yellow River Delta.

Klíčová slova:

Agricultural soil science – Delta ecosystems – Erosion – Image processing – Light – Soil ecology – Soil chemistry – Wheat


1. Zhang XF, Xu Y, Li LJ, Dai EF, Xu WH. Regional differentiation and classification for carrying constraints on the resources and environment of China. Journal of Resources and Ecology. 2018; 9(2): 135–145.

2. Sun ZX, Luan WX, Ma Y, Pian F. Study on the spatial expansion characteristics of development zones in different coastal areas of China. Journal of Natural Resources. 2018; 33(2): 262–274.

3. Qi SZ, Liu HL. Natural and anthropogenic hazards in the Yellow River Delta, China. Natural Hazards. 2017; 8(3): 1907–1911.

4. Wu CS, Liu GH, Huang C, Liu QS, Guan XD. Ecological vulnerability assessment based on fuzzy analytical method and analytic hierarchy process in Yellow River Delta. International Journal of Environmental Research and Public Health. 2018; 15(5): 855.

5. Li YL, Zhao GX, Chang CY, Wang ZR, Wang L, Zheng JR. Soil salinity retrieval model based on OLI and HSI image fusion. Transactions of the Chinese Society of Agricultural Engineering. 2017; 33(21): 173–180.

6. Cavalli A. C., Neto F. L., Garcia G. J., Junior J. Z.. Use of AVHRR/NOAA-14 multi-temporal data to evaluate soil degradation. Acta Scientiarum. 2000; 22(4):1037–1043.

7. Wang XH, Chen YF, Chen EX, Luo YL. Quantified evaluation of soil erosion in Zhongyang county, the Loess Plateau. Journal of Moutain Science.2011; 29(4):442–448.

8. THIAM A. K.. The causes and spatial pattern of land degradation risk in southern Mauritania using multitemporal AVHRR-NDVI imagery and field data. Land Degrad. 2003; 14(1): 133–142.

9. Wessels K. J., Prince S. D., Frost P. E., van Zyl D.. Assessing the effects of human-induced land degradation in the former homelands of northern South Africa with a 1 km AVHRR NDVI time-series. Remote Sens. Environ. 2004; 91(1): 47–67.

10. Symeonakis E., Drake N.. Monitoring desertification and land degradation over sub-Saharan Africa. Int. J. Remote Sens. 2004; 25(3): 573–592.

11. Lu D., Batistella M., Mausel P., Moran E.. Mapping and monitoring land degradation risks in the Western Brazilian Amazon using multitemporal Landsat TM/ETM+ images. Land Degrad. 2007; 18(1): 41–54.

12. Raina P., Joshi D. C., Kolarkar A. S.. Mapping of soil degradation by using remote sensing on alluvial plain, Rajasthan, India. Arid Land Res. Manage. 19937(2): 145–161.

13. Omuto C. T., Shrestha D. P.. Remote sensing techniques for rapid detection of soil physical degradation. Int. J. Remote Sens. 2007; 28(21): 4785–4805.

14. Leone A. P., Sommer S.. Multivariate analysis of laboratory spectra for the assessment of soil development and soil degradation in the Southern Apennines (Italy). Remote Sens. Environ. 2000; 72(3): 346–359.

15. Kassahun D., Tripathi N. K., Honda K.. Development of spectral band cloning techniques for soil nutrients estimation. Int. J. Remote Sens. 2006; 27(19): 4213–4225.

16. Ben-Dor E., Chabrillat S., Demattê J. A. M., Taylor G. R., Hill J., Whiting M. L., et al. Using imaging spectroscopy to study soil properties. Remote Sens. Environ. 2009; 113(1): 38–55.

17. Wang Q, Li PH, Chen X. Modeling salinity effects on soil reflectance under various moisture conditions and its inverse application: A laboratory experiment. Geoderma. 2012; 1(170): 103–111.

18. Gholizadeh A., Borůvka L., Saberioon M., Vašát R.. Visible, near-Infrared, and mid-infrared spectroscopy applications for soil assessment with emphasis on soil organic matter content and quality: state-of-the-art and key issues. Appl. Spectrosc. 2013; 67(12): 1349–1362. doi: 10.1366/13-07288 24359647

19. DeTar W. R., Chesson J. H., Penner J. V., Ojala J. C.. Detection of soil properties with airborne hyperspectral measurements of bare fields. Trans. ASABE. 2008; 2(51): 463–470.

20. Confalonieri M., Fornasier F., Ursino A., Boccardi F., Pintus B., Odoardi M.. The potential of near infrared reflectance spectroscopy as a tool for the chemical characterisation of agricultural soils. J. Near Infrared Spectrosc. 2001; 9(1): 123–131.

21. Filep T., Zacháry D., Balog K.. Assessment of soil quality of arable soils in Hungary using DRIFT spectroscopy and chemometrics. Vib. Spectrosc. 2016; 84(1): 16–23.

22. Mathieu R., Cervelle B., Rémy D., Pouget M.. Field-based and spectral indicators for soil erosion mapping in semi-arid mediterranean environments (Coastal Cordillera of central Chile). Earth Surf. Processes Landforms. 2007; 1(32): 13–31.

23. Cannane N. O. A., Rajendran M. Selvaraju R.. FT-IR spectral studies on polluted soils from industrial area at Karaikal, Puducherry State, South India. Spectrochim. Acta, Part A. 2013; 110: 46–54.

24. Guo B, Yang F, Fan YW, Han BM, Chen ST, Yang WN. Dynamic monitoring of soil salinization in Yellow River Delta utilizing MSAVI–SI feature space models with Landsat images. Environmental Earth Sciences. 2019; 78: 308–317.

25. Mirzaee S., Dashtaki S. G., Mohammadi J., Asadi H., Asadzadeh F.. Spatial variability of soil organic matter using remote sensing data. Catena. 2016; 145: 118–127.

26. Li J, Zhao GX, Chang CY, Liu HT. Land salinization information extraction method based on HSI hyperspectral and TM imagery. Spectrosc. Spectral Anal. (Beijing, China). 2014; 34(2): 520–525.

27. An DY, Zhao GX, Chang CY, Wang ZR, Li P, Zhang TR, Jia JC. Hyperspectral field estimation and remote-sensing inversion of salt content in coastal saline soils of the Yellow River Delta. Int. J. Remote Sens. 2016; 37(2): 455–470.

28. Chen HY, Zhao GX, Sun L, Wang RY, Liu Y.. Prediction of soil salinity using near-infrared reflectance spectroscopy with nonnegative matrix factorization. Appl. Spectrosc. 2016; 70(9): 1589–1597. doi: 10.1177/0003702816662605 27566255

29. Fan X, Pedroli B., Liu G, Liu Q, Liu H, Shu L. Soil salinity development in the yellow river delta in relation to groundwater dynamics. Land Degrad. Dev. 2012; 23(2): 175–189.

30. Jiang ML. Coastal saline land water and salt monitoring and preliminary analysis-KenLi county in the Yellow River Delta as an example. Chang’an University, Xi’an, China. 2015; Pp. 9–12.

31. Sur K., Chauhan P.. Imaging spectroscopic approach for land degradation studies: a case study from the arid land of India. Geomatics, Natural Hazards and Risk. 2019; 10(1): 898–911.

32. Bednář M., Šarapatka B.. Relationships between physical–geographical factors and soil degradation on agricultural land. Environmental Research. 2018; 164: 660–668. doi: 10.1016/j.envres.2018.03.042 29631225

33. Zhao HG, Tang YY, Yang ST. Dynamic identification of soil erosion risk in the middle reaches of the Yellow River Basin in China from 1978 to 2010. Journal of Geograghical Sciences. 2018; 28(2): 175–192.

34. Wang XP, Zhang F, Ding JL, Kung HT, Latif A., Johnson V. C.. Estimation of soil salt content (SSC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR), Northwest China, based on a Bootstrap-BP neural network model and optimal spectral indices. Science of the Total Environment. 2017; 615: 918–930. doi: 10.1016/j.scitotenv.2017.10.025 29017133

35. Li XC, Zhao GX, Chen HY, Wu B, Tian Y, Zhang YH. The interval estimation of soil organic matter content. Journal of Geomatics Science and Technology. 2014; 31: 593–597+602.

36. Cornfield A. H.. Ammonia released on Treating Soils with N Sodium Hydroxide as a Possible Means of predicting the Nitrogen-supplying Power of Soils. Nature. 1960; 187:260–261.

37. Olsen S. R., Cole C. V., Watanabe F. S., Dean L. A.. Estimation of available phosphorus in soils by extraction with sodium bicarbonate. Circular of United States Department of Agriculture. 1954; pp: 939.

38. Schollenberger C. J., Simon R. H.. Determination of exchange capacity and exchangeable bases in soil-ammonium acetate method. Soil Sci. 1945; 59:13–24.

39. Wang ZR, Zhao GX, Gao MX, Chang CY. Spatial variability of soil salinity in coastal saline soil at different scales in the Yellow River Delta, China. Environ Monit Assess. 2017; 189: 80. doi: 10.1007/s10661-017-5777-x 28124294

40. O. O. N. S. Survey, Nutrient Classification Statistics of Agricultural Soil Samples. in: Tian Y., Zhen W., Zhao Y., Gong Y. and Sui P., editors. National Soil Survey Data of China. Beijing, China: China Agriculture Press. 1997; Pp. 132–140.

41. Analysis of Soil Water Solubility. in: Jiang R., Yang C., Xu G., Bao S. and Han X., editors. Soil Agricultural Chemistry Analysis. Beijing, China: China Agriculture Press. 2000; Pp. 178–179.

42. Qiao HQ, Cheng WS. Ecological Security Evaluation of Land based on Entropy Weight Matter-element Model. Chin. J. Soil Sci. 2016; 47(2): 302–307.

43. Ni JP, Li P, Wei CF, Xie DT. Potentialities evaluation of regional land consolidation based on AHP and entropy weight method. Transactions of the Chinese Society of Agricultural Engineering. 2009; 25(5): 202–209.

44. Cloutis E.. Review Article: Hyperspectral geological remote sensing: evaluation of analytical techniques. Int. J. Remote Sens. 1996; 17(12): 2215–2242.

45. Terhoeven-Urselmans T., Schmidt H., Joergensen R. G., Ludwig B.. Usefulness of near-infrared spectroscopy to determine biological and chemical soil properties: Importance of sample pre-treatment. Soil Biol. Biochem. 2008; 40(5): 1178–1188.

46. Chang WC, Laird A. D.. Near infrared reflectance spectroscopic analysis of soil C and N. Soil Sci. 2002; 167(2): 110–116.

47. B Su. Identification and assessment of land degradation using remote sensing techniques—a case study in Inner Mongolia Autonomous Region. Chinese academy of forestry. 2016; pp: 84.

48. J Li. Soil degradation assessment and remote sensing retrieval of typical ecological fragile zone in the Yellow River Delta. Shandong Agricultural University. 2014; pp: 34.

49. Wang ZR, Zhao GX, Gao MX, Chang CY, Jiang SQ, Jia JC, Li J. Spatial variation of soil water and salt and microscopic variation of soil salinity in summer in typical area of the Yellow River Delta in Kenli County. Acta Ecol. Sin. 2016; 36(4): 1040–1049.

Článek vyšel v časopise


2020 Číslo 1
Nejčtenější tento týden