Capability of spaceborne hyperspectral EnMAP mission for mapping fractional cover for soil erosion modeling

Remote Sensing

Volumn 7, Issue 9, 2015, Pages 11776-11800

  Malec, Sarah ^a   Rogge, Derek ^b   Heiden, Uta ^b   Sanchez Azofeifa, Arturo ^c   Bachmann, Martin ^b   Wegmann, Martin ^a  

^a UNIVERSITY OF BAYREUTH   (Germany)

^b GERMAN AEROSPACE CENTER DLR   (Germany)

^c UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Costa Rica; EnMAP; Imaging spectroscopy; Soil erosion modeling; Spectral mixture analysis

Indexed keywords

EROSION; MIXTURES; VEGETATION;

COSTA RICA; ENMAP; IMAGING SPECTROSCOPY; SOIL EROSION MODELING; SPECTRAL MIXTURE ANALYSIS;

SOILS;

EID: 84942540716     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs70911776     Document Type: Article

References (63)

1. Kaufmann, H.; Segl, K.; Chabrillat, S.; Hofer, S.; Stuffler, T.; Müller, A.; Richter, R.; Schreier, G; Haydn, R; Bach, H. EnMaP a hyperspectral sensor for environmental mapping and analysis (invited paper). In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Denver, CO, USA, 31 July-4 August 2006.
2. Eswaran, H.; Lal, R.; Reich, P.F. Land degradation: An overview. In Responses to Land Degradation, Proceedings of the International Conference on Land Degradation and Desertification, New Dehli, India, 2001; Hannam, I.D., Oldeman, L.R., Pening de Vries, F.W.T., Scherr, S.J., Sompatpanit, S., Eds.; Oxford Press: Oxford, UK, 2001.
3. Lal, R. Soil erosion and the global carbon budget. Environ. Int. 2003, 29, 437-450.
4. Lee, S. Soil erosion assessment and its verification using the universal soil loss equation and geographic information system: A case study at Boun, Korea. Environ. Geol. 2004, 45, 457-465.
5. Vrieling, A. Satellite remote sensing for water erosion assessment. Catena 2006, 65, 2-18.
6. Ferreira, V.; Panagopoulos, T. Seasonality of soil erosion under Mediterranean conditions at the Alqueva dam watershed. Environ. Manag. 2014, 54, 67-83.
7. Labriere, N.; Locatelli, B.; Laumonier, Y.; Freycon, V.; Bernoux, M. Soil erosion in the humid tropics: A systematic quantitative review. Agric. Ecosyst. Environ. 2015, 203, 127-139.
8. Wischmeier, W.H.; Smith, D.D. A universal soil-loss equation to guide conservation farm planning. Trans. Int. Congr. Soil Sci. 1968, 1, 418-425.
9. Morgan, R.P.C.; Quinton, J.N.; Smith, R.E.; Govers, G.; Poesen, W.A.; Auerswald, K.; Chisci, G.; Torri, D.; Styczen, M.E. The European Soil Erosion Model (EUROSEM): A dynamic approach for predicting sediment transport from fields and small catchments. Earth Surf. Process. Landf. 1998, 23, 527-544.
10. Gyssels, G.; Poesen, J.; Bochet, E.; Li, Y. Impact of plant root characteristics on resistance of soil to erosion by water: A review. Prog. Phys. Geogr. 2005, 29, 189-217.
11. De Asis, A.M.; Omasa, K. Estimation of vegetation parameter for modeling soil erosion using spectral mixture analysis of Landsat ETM data. ISPRS J. Photogramm. Remote Sens. 2007, 62, 309-324.
12. Gray, D.H.; Sotir, R.B. Biotechnical and Soil Bioengineering Slope Stabilization: A Practical Guide for Erosion Control; John Wiley and Sons: New York, NY, USA, 1996.
13. Renard, K.G.; Yoder, D.C.; Lightle, D.T.; Dabney, S.M. Universal Soil Loss Equation and Revised Universal Soil Loss Equation, 1st ed.; Blackwell Publishing Ltd.: Oxford, UK, 2011.
14. Mirsal, I.A. Soil Pollution: Origin, Monitoring and Remediation; Springer: Berlin, Heidelberg, Germany, 2008
15. Shoshanya, M.; Goldshlegerb, N.; Chudnovskyc, A.: Monitoring of agricultural soil degradation by remote-sensing methods: A review. Int. J. Remote Sens. 2013, 37, 6152-6181.
16. Veihe, A.; Rey, J.; Quinton, J.N.; Strauss, P.; Sancho, F.M.; Somarriba, M. Modelling of event-based soil erosion in Costa Rica, Nicaragua and Mexico: Evaluation of the Eurosem model. Catena 2001, 44, 187-203.
17. Asner, G.P.; Knapp, D.E.; Cooper, A.N.; Bustamante, M.M.C.; Olander, L.P. Ecosystem structure throughout the brazilian amazon from landsat observations and automated spectral unmixing. Earth Interact. 2005, 9, 1-31.
18. Guerschman, J.P.; Scarth, P.F.; McVicar, T.R.; Renzullo, L.J.; Malthus, T.J.; Stewart, J.B.; Rickards, J.E.; Trevithick, R. Assessing the effects of site heterogeneity and soil properties when unmixing photosynthetic vegetation, non-photosynthetic vegetation and BS fractions from Landsat and MODIS data. Remote Sens. Environ. 2015, 161, 12-26.
19. Okin, G.S.; Clarke, K.D.; Lewis, M.M. Comparison of methods for estimation of absolute vegetation and soil fractional cover using MODIS normalized BRDF-adjusted reflectance. Remote Sens. Environ. 2013, 130, 266-270.
20. Okin, G.S. Relative spectral mixture analysis-A multitemporal index of total vegetation cover. Remote Sens. Environ. 2007, 106, 467-479.
21. Asner, G.P.; Heidebrecht, K.B. Spectral unmixing of vegetation, soil and dry carbon in arid regions: Comparing multi-spectral and hyperspectral observations. Int. J. Remote Sens. 2002, 23, 400-410.
22. Asner, G.P.; Lobell, D.B. A biogeophysical approach for automated SWIR unmixing of soils and vegetation. Remote Sens. Environ. 2000, 74, 99-112.
23. Elvidge, C.D. Visible and near infrared reflectance characteristics of dry plant materials. Int. J. Remote Sens. 1990, 11, 1775-1795.
24. Ustin, S.L.; Roberts, D.A.; Gamon, J.A.; Asner, G.P.; Green, R.O. Using imaging spectroscopy to study ecosystem processes and properties. Bio. Sci. 2004, 54, 523-534.
25. Ben-Dor, E.; Chabrillat, S.; Dematte. J.A.M.; Taylor, G.R.; Hill, J.; Whiting, M.L.; Sommer, S. Using imaging spectroscopy for soil properties. Remote Sens. Environ. 2009, 113, S38-S55.
26. Nocita, M.; Stevens, A.; van Wesemael, B.; Aitkenhead, M.; Bachmann, M.; Barthes, B.; Ben-Dor, E.; Brown, D.J.; Clairotte, M., Csorba, A.; et al. Spectroscopy: An alternative to wet chemistry for soil monitoring. Adv. Agron. 2015, 132, 1-21.
27. Daughtry, C.S.T.; Doraiswamy, P.C.; Hunt, E.R., Jr.; Stern, A.J.; McMurtrey, J.E., III; Prueger, J.H. Remote sensing of crop residue cover and soil tillage intensity. Soil Tillage Res. 2006, 91, 101-108.
28. Delegido, J.; Verrelst, J.; Rivera, J.P.; Ruiz-Verdu, A.; Moreno, J. Brown and green LAI mapping through spectral indices. Int. J. Appl. Earth Obs. Geoinform. 2015, 35, 350-358.
29. Mulder, V.L.; de Bruin, S.; Schaepman, M.E.; Mayr, T.R. The use of remote sensing in soil and terrain mapping-A review. Geoderma 2011, 162, 1-19.
30. Förster, S.; Wilczok, C.; Brosinsky, A.; Segl, K. Assessment of sediment connectivity from vegetation cover and topography using remotely sensed data in a dryland catchment in the Spanish Pyrenees. J. Soil. Sediment. 2014, 14, 1982-2000.
31. Bayer, A.; Bachmann, M.; Müller, A.; Kaufmann, H. A comparison of feature-based MLR and PLS regression techniques for the prediction of three soil constituents in a degraded South African ecosystem. Appl. Environ. Soil Sci. 2012, 2012, 1-20.
32. Okin, G.; Roberts, D.; Murray, B.; Okin, W. Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. Remote Sens. Environ. 2001, 77, 212-225.
33. Bannari, A.; Staenz, K.; Champagne, C.; Khurshid, K.S. Spatial variability mapping of crop residue using Hyperion (EO-1) hyperspectral data. Remote Sens. 2015, 7, 8107-8127.
34. Ghosh, G.; Kumar, S.; Saha, S.K. Hyperspectral satellite data in mapping salt-affected soils using linear spectral unmixing analysis. J. Indian Soc. Remote Sens. 2012, 40, 129-136.
35. Wu, J.; Liu, Y.; Wang, J.; He, T. Application of Hyperion data to land degradation mapping in the Hengshan region of China. Int. J. Remote Sens. 2010, 31, 5145-5161.
36. Kruse, F.A.; Boarsmann, J.W.; Huntingtin, J.F. Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1388-1400.
37. Roberts, D.A.; Dennison, P.E.; Gardner, M.E.; Hetzel, Y.; Ustin, S.L.; Lee, C.T. Evaluation of the potential of Hyperion for fire danger assessment by comparison to the airborne visible/infrared imaging spectrometer. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1297-1320.
38. Middleton, E.M.; Ungar, S.G.; Mandl, D.J.; Ong, L.; Frye, S.W.; Campbell, P.E.; Landis, D.R.; Young, J.P.; Pollack, N.H. The earth observing one (EO-1) satellite mission: Over a decade in space. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2013, 6, 243-256.
39. Guanter, L.; Kaufmann, H.; Segl, K.; Foerster, S.; Rogass, C.; Chabrillat, S.; Kuester, T.; Hollstein, A.; Rossner, G.; Chlebek, C.; et al. The Environmental Mapping and Analysis Program (EnMAP) spaceborne imaging spectroscopy mission for Earth observation. Remote Sens. 2015, 7, 8830-8857.
40. Roberts, D.A.; Quattrochi, D.A.; Hulley, G.C.; Hook, S.J.; Green, R.O. Synergies between VSWIR and TIR data for the urban environment: An evaluation of the potential for the hyperspectral infrared imager (HyspIRI) decadal survey mission. Remote Sens. Environ. 2012, 117, 83-101.
41. Matsunaga, T.; Iwasaki, A.; Tsuchida, S.; Tanii, J.; Kashimura, O.; Nakamura, R.; Yamamoto, H.; Tachikawa, T.; Rokugawa, S. Current status of Hyperspectral Imager Suite (HISUI). In Peoceedings of the IEEE Geoscience and Remote Sensing Symposium, Melbourne, Australia, 21-26 July 2013; pp. 3510-3513.
42. Segl, K.; Guanter, L.; Rogaß, C.; Küster, T.; Roessner, S.; Kaufmann, H.; Sang, B.; Mogulsky, V.; Hofer, S. Eetes- the EnMAP end-to-end simulation tool. IEEE Trans. Geosci. Remote Sens. 2012, 5, 522-530.
43. Rogge, D.; Rivard, B.; Segl, K.; Grant, B.; Feng, J. Mapping of NiCu-PGE ore hosting ultramafic rocks using airborne and simulated EnMAP hyperspectral imagery, Nunavik, QC, Canada. Remote Sens. Environ. 2014, 152, 302-317.
44. Soil Conservation Service. Soil Taxonomy: A Basic System of Soil Classification for Making and Interpreting Soil Surveys, 2nd ed.; U.S. Department of Agriculture Handbook 436; Natural Resources Conservation Service: Washington, DC, USA, 10 August 1999.
45. Bachmann, M.; Habermeyer, M.; Holzwarth, S.; Richter, R.; Müller, A. Including quality measures in an automated processing chain for airborne hyperspectral data. In Proceedings of the 5th EARSel Workshop on Imaging Spectroscopy, Bruges, Belgium, 23-25 April 2007.
46. Richter, R.; Schlapfer, D. Atmospheric/Topographical Correction for Airborne Imagery: ATCOR-4 User Guide; DLR IB: Wessling, Germany, 2010.
47. Müller, R.; Lehner, M.; Müller, R.; Reinartz, P.; Schroeder, M.; Vollmer, B. A program for direct georeferencing of airborne and spaceborne line scanner images. In Proceedings of the International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, Denver, CO, USA, 10-15 November 2002; pp. 148-153.
48. Schläpfer, D.; Richter, R.; Feingersh, T. Operational BRDF effects correction for wide-field-of-view optical scanners (BREFCOR). IEEE Trans. Geosci. Remote Sens. 2015, 53, 1855-1864.
49. Guanter, L.; Segl, K.; Kaufmann, H. Simulation of the optical remote-sensing scenes with application to the EnMAP hyperspectral mission. IEEE Trans. Geosci. Remote Sens. 2009, 47, 2340-2351.
50. Segl, K.; Guanter, L.; Kaufmann, H.; Schubert, J.; Kaiser, S.; Sang, B.; Hofer, S. Simulation of spatial sensor characteristics in the context of the EnMAP hyper-spectral mission. IEEE Trans. Geosci. Remote Sens. 2010, 48, 3046-3054.
51. Esch, T.; Marconcini, M.; Felbier, A.; Roth, A.; Heldens, W.; Huber, M; Schwinger, M.; Taubenböck, H.; Müller, A.; Dech, S. Urban footprint processor-Fully automated processing chain generating settlement masks from global data of the TanDEM-X mission. IEEE Geosci. Remote Sens. Lett. 2013, 10, 1617-1621.
52. Adams, J.B.; Smith, M.O.; Gillespie, A.R. Imaging spectroscopy: Interpretation based on spectral mixture analysis. In Remote Geochemical Analysis: Elemental and Mineralogical Composition; Pieters, C.M., Englert, P.A., Eds.; Cambridge University Press: Cambridge, UK, 1993; pp. 145-166.
53. Rogge, D.; Bachmann, M.; Rivard, B.; Feng, J. Spatial sub-sampling using local endmembers for adapting OSP and SSEE for large-scale hyperspectral surveys. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 2012, 5, 183-195.
54. Price, J.C. How unique are spectral signatures? Remote Sens. Environ. 1994, 49, 181-186.
55. Roberts, D.; Gardner, M.; Church, R.; Ustin, S.; Scheer, G.; Green, R. Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models. Remote Sens. Environ. 1998, 65, 267-279.
56. Bachmann, M. Automated Estimation of Ground Cover Fractions Using MESMA Unmixing. Ph. D. Thesis, University of Wuerzburg, Wuerzburg, Germany, December 2007.
57. Bachmann, M.; Müller, A.; Dech, S. Increasing and evaluating the reliability of multiple endmember spectral mixture analysis (MESMA). In Proceedings of the 6th EARSeL-SIG-IS, Tel Aviv, Israel, 16-19 March 2009.
58. Zabcic, N.; Rivard, B.; Ong, C.; Müller, A. Using airborne hyperspectral data to characterize the surface pH and mineralogy of pyrite mine tailings. Int. J. Appl. Earth Obs. Geoinform. 2014, 32, 152-162.
59. Mitra, B.; Scotta, H.D.; Dixon, J.C.; McKimmey, J.M. Applications of fuzzy logic to the prediction of soil erosion in a large watershed. Geoderma 1998, 86, 183-209.
60. Bakimchandra, O. Integrated Fuzzy-GIS Approach for Assessing Regional Soil Erosion Risks. Ph. D. Thesis, University of Stuttgart, Stuttgart, Germany, August 2011.
61. Wischmeier, W.H.; Smith, D.D. Predicting Rainfall Erosion Losses-A Guide to Conservation Planning; Agriculture Handbook No. 537; U.S. Department of Agriculture: Washington, DC, USA, December 1978.
62. Thorp, K.R.; French, A.N.; Rango, A. Effect of image spatial and spectral characteristics on mapping semi-arid rangeland vegetation using multiple endmember spectral mixture analysis (MESMA). Remote Sens. Environ. 2013, 132, 120-130.
63. Okujeni, A.; van der Linden, S.; Hostert, P. Extending the vegetation-impervious-soil model using simulated EnMAP data and machine learning. Remote Sens. Environ. 2015, 158, 69-80.