Multiple cost functions and regularization options for improved retrieval of leaf chlorophyll content and LAI through inversion of the PROSAIL model

Remote Sensing

Volumn 5, Issue 7, 2013, Pages 3280-3304

  Rivera, Juan Pablo ^a   Verrelst, Jochem ^a   Leonenko, Ganna ^b   Moreno, José ^a  

^a UNIVERSITY OF VALENCIA   (Spain)

^b CARDIFF UNIVERSITY   (United Kingdom)

Author keywords

Biophysical parameters; Cost functions; LUT based inversion; PROSAIL; Radiative transfer models; Sentinel 2

Indexed keywords

CHLOROPHYLL; COSTS; ERRORS; RADIATIVE TRANSFER; TABLE LOOKUP;

BIOPHYSICAL PARAMETERS; LUT-BASED INVERSION; PROSAIL; RADIATIVE TRANSFER MODEL; SENTINEL-2;

COST FUNCTIONS;

EID: 84881513198     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs5073280     Document Type: Article

References (67)

1. Whittaker, R.H.; Marks, P.L. Methods of Assessing Terrestrial Productivity. In Primary Productivity of the Biosphere; Springer Verlag: Berlin/Heidelberg, Germany, 1975; pp. 55-118.
2. Lichtenthaler, H.K. Chlorophylls and carotenoids: Pigments of photosynthetic biomembranes. Methods Enzymol. 1987, 148, 350-382.
3. Malenovský, Z.; Rott, H.; Cihlar, J.; Schaepman, M.; García-Santos, G.; Fernandes, R.; Berger, M. Sentinels for science: Potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land. Remote Sens. Environ. 2012, 120, 91-101.
4. Lichtenthaler, H.K.; Lang, M.; Sowinska, M.; Heisel, F.; Miehe, J.A. Detection of vegetation stress via a new high resolution fluorescence imaging system. J. Plant Physiol. 1996, 148, 599-612.
5. Sampson, P.H.; Zarco-Tejada, P.J.; Mohammed, G.H.; Miller, J.R.; Noland, T.L. Hyperspectral remote sensing of forest condition: Estimating chlorophyll content in tolerant hardwoods. For. Sci. 2003, 49, 381-391.
6. Dorigo, W.A.; Zurita-Milla, R.; de Wit, A.J.W.; Brazile, J.; Singh, R.; Schaepman, M.E. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. Int. J. Appl. Earth Obs. Geoinf. 2007, 9, 165-193.
7. Delegido, J.; Verrelst, J.; Meza, C.; Rivera, J.; Alonso, L.; Moreno, J. A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems. Eur. J. Agron. 2013, 46, 42-52.
8. Gianquinto, G.; Orsini, F.; Fecondini, M.; Mezzetti, M.; Sambo, P.; Bona, S. A methodological approach for defining spectral indices for assessing tomato nitrogen status and yield. Eur. J. Agron. 2011, 35, 135-143.
9. Baret, F.; Hagolle, O.; Geiger, B.; Bicheron, P.; Miras, B.; Huc, M.; Berthelot, B.; Niço, F.; Weiss, M.; Samain, O.; et al. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION. Part 1: Principles of the algorithm. Remote Sens. Environ. 2007, 110, 275-286.
10. Myneni, R.; Hoffman, S.; Knyazikhin, Y.; Privette, J.; Glassy, J.; Tian, Y.; Wang, Y.; Song, X.; Zhang, Y.; Smith, G.; et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ. 2002, 83, 214-231.
11. Ganguly, S.; Nemani, R.; Zhang, G.; Hashimoto, H.; Milesi, C.; Michaelis, A.; Wang, W.; Votava, P.; Samanta, A.; Melton, F.; et al. Generating global leaf area index from landsat: Algorithm formulation and demonstration. Remote Sens. Environ. 2012, 122, 185-202.
12. Dash, J.; Curran, P. The MERIS terrestrial chlorophyll index. Int. J. Remote Sens. 2004, 25, 5403-5413.
13. Bacour, C.; Baret, F.; Béal, D.;Weiss, M.; Pavageau, K. Neural network estimation of LAI, fAPAR, fCover and LAI × Cab, from top of canopy MERIS reflectance data: Principles and validation. Remote Sens. Environ. 2006, 105, 313-325.
14. Houborg, R.; Boegh, E. Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance data. Remote Sens. Environ. 2008, 112, 186-202.
15. Houborg, R.; Anderson, M.; Daughtry, C. Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale. Remote Sens. Environ. 2009, 113, 259-274.
16. Stuffler, T.; Kaufmann, C.; Hofer, S.; Förster, K.; Schreier, G.; Mueller, A.; Eckardt, A.; Bach, H.; Penné, B.; Benz, U.; et al. The EnMAP hyperspectral imager-An advanced optical payload for future applications in Earth observation programmes. Acta Astronaut. 2007, 61, 115-120.
17. Labate, D.; Ceccherini, M.; Cisbani, A.; De Cosmo, V.; Galeazzi, C.; Giunti, L.; Melozzi, M.; Pieraccini, S.; Stagi, M. The PRISMA payload optomechanical design, a high performance instrument for a new hyperspectral mission. Acta Astronaut. 2009, 65, 1429-1436.
18. Roberts, D.; Quattrochi, D.; Hulley, G.; Hook, S.; Green, R. 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.
19. Darvishzadeh, R.; Skidmore, A.; Schlerf, M.; Atzberger, C. Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland. Remote Sens. Environ. 2008, 112, 2592-2604.
20. Knyazikhin, Y.; Kranigk, J.; Myneni, R.; Panfyorov, O.; Gravenhorst, G. Influence of small-scale structure on radiative transfer and photosynthesis in vegetation canopies. J. Geophys. Res. D 1998, 103, 6133-6144.
21. Durbha, S.; King, R.; Younan, N. Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer. Remote Sens. Environ. 2007, 107, 348-361.
22. Combal, B.; Baret, F.; Weiss, M.; Trubuil, A.; Mace, D.; Pragnère, A.; Myneni, R.; Knyazikhin, Y.; Wang, L. Retrieval of canopy biophysical variables from bidirectional reflectance using prior information to solve the ill-posed inverse problem. Remote Sens. Environ. 2003, 84, 1-15.
23. Richter, K.; Atzberger, C.; Vuolo, F.; Weihs, P.; D'Urso, G. Experimental assessment of the Sentinel-2 band setting for RTM-based LAI retrieval of sugar beet and maize. Can. J. Remote Sens. 2009, 35, 230-247.
24. Weiss, M.; Baret, F.; Myneni, R.; Pragnère, A.; Knyazikhin, Y. Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data. Agronomie 2000, 20, 3-22.
25. Fang, H.; Liang, S. A hybrid inversion method for mapping leaf area index from MODIS data: Experiments and application to broadleaf and needleleaf canopies. Remote Sens. Environ. 2005, 94, 405-424.
26. Qu, Y.; Wang, J.; Wan, H.; Li, X.; Zhou, G. A Bayesian network algorithm for retrieving the characterization of land surface vegetation. Remote Sens. Environ. 2008, 112, 613-622.
27. Walthall, C.; Dulaney, W.; Anderson, M.; Norman, J.; Fang, H.; Liang, S. A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery. Remote Sens. Environ. 2004, 92, 465-474.
28. Weiss, M.; Baret, F. Evaluation of canopy biophysical variable retrieval performances from the accumulation of large swath satellite data. Remote Sens. Environ. 1999, 70, 293-306.
29. Jacquemoud, S.; Baret, F.; Andrieu, B.; Danson, F.; Jaggard, K. Extraction of vegetation biophysical parameters by inversion of the PROSPECT+SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors. Remote Sens. Environ. 1995, 52, 163-172.
30. Baret, F.; Buis, S. Estimating Canopy Characteristics from Remote Sensing Observations. Review of Methods and Associated Problems. In Advances in Land Remote Sensing: System, Modeling, Inversion and Application; Liang, S., Ed.; Springer: Berlin, Germany, 2008; pp. 173-201.
31. Combal, B.; Baret, F.; Weiss, M. Improving canopy variables estimation from remote sensing data by exploiting ancillary information. Case study on sugar beet canopies. Agronomie 2002, 22, 205-215.
32. Dorigo, W.; Richter, R.; Baret, F.; Bamler, R.; Wagner, W. Enhanced automated canopy characterization from hyperspectral data by a novel two step radiative transfer model inversion approach. Remote Sens. 2009, 1, 1139-1170.
33. Koetz, B.; Baret, F.; Poilvé, H.; Hill, J. Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics. Remote Sens. Environ. 2005, 95, 115-124.
34. Richter, K.; Atzberger, C.; Vuolo, F.; D'Urso, G. Evaluation of Sentinel-2 spectral sampling for radiative transfer model based LAI estimation of wheat, sugar beet, and maize. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2011, 4, 458-464.
35. Darvishzadeh, R.; Matkan, A.A.; Dashti Ahangar, A. Inversion of a radiative transfer model for estimation of rice canopy chlorophyll content using a lookup-table approach. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2012, PP, 1-9.
36. Leonenko, G.; North, P.; Los, S. Statistical distances and their applications to biophysical parameter estimation: Information measures, M-estimates, and minimum contrast methods. Remote Sens. 2013, 5, 1355-1388.
37. Verrelst, J.; Rivera, J.; Leonenko, G.; Moreno, J. Optimizing LUT-based RTM inversion for semiautomatic mapping of crop biophysical parameters from Sentinel-2 and -3 data: Role of cost functions. IEEE Trans. Geosci. Remote Sens. 2013, in press.
38. Atzberger, C.; Richter, K. Spatially constrained inversion of radiative transfer models for improved LAI mapping from future Sentinel-2 imagery. Remote Sens. Environ. 2012, 120, 208-218.
39. Chen, J.; Pavlic, G.; Brown, L.; Cihlar, J.; Leblanc, S.; White, H.; Hall, R.; Peddle, D.; King, D.; Trofymow, J.; et al. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote Sens. Environ. 2002, 80, 165-184.
40. Kullback, S.; Leibler, R. On information and sufficiency. Ann. Math. Stat. 1951, 22, 79-86.
41. Pardo, L. Statistical Inference Based on Divergence Measures, Statistics: A Series Textbooks and Monographs; Chapman and Hall/CRC: Boca Raton, FL, USA, 2006.
42. Staudte, R.; Sheather, S. Robust Estimation and Testing; Wiley: New York, NY, USA, 1990.
43. Taniguchi, M. On estimation of parameters of Gaussian stationary processes. J. Appl. Probab. 1979, 16, 575-591.
44. Gandía, S.; Fernández, G.; Garcia, J.C.; Moreno, J. Retrieval of Vegetation Biophysical Variables from CHRIS/PROBA Data in the SPARC Campaign. In Proceedings of the 2nd CHRIS/PROBAWorkshop, Frascati, Italy, 28-30 April 2004.
45. Fernández, G.; Moreno, J.; Gandía, S.; Martínez, B.; Vuolo, F.; Morales, F. Statistical Variability of Field Measurements of Biophysical Parameters in SPARC-2003 and SPARC-2004 Campaigns. In Proceedings of the SPARC Workshop WPP-250, Enschede, The Netherlands, 4-5 July 2005.
46. Drusch, M.; Del Bello, U.; Carlier, S.; Colin, O.; Fernandez, V.; Gascon, F.; Hoersch, B.; Isola, C.; Laberinti, P.; Martimort, P.; et al. Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sens. Environ. 2012, 120, 25-36.
47. Barnsley, M.J.; Settle, J.J.; Cutter, M.A.; Lobb, D.R.; Teston, F. The PROBA/CHRIS mission: A low-cost smallsat for hyperspectral multiangle observations of the earth surface and atmosphere. IEEE Trans. Geosci. Remote Sens. 2004, 42, 1512-1520.
48. Alonso, L.; Moreno, J. Advances and Limitations in A Parametric Geometric Correction of CHRIS/PROBA Data. In Proceedings of the 3rd CHRIS/Proba Workshop, Frascati, Italy, 21-23 March 2005.
49. Guanter, L.; Alonso, L.; Moreno, J. A method for the surface reflectance retrieval from PROBA/CHRIS data over land: Application to ESA SPARC campaigns. IEEE Trans. Geosci. Remote Sens. 2005, 43, 2908-2917.
50. Verrelst, J.; Rivera, J.; Alonso, L.; Moreno, J. ARTMO: An Automated Radiative Transfer Models Operator Toolbox for Automated Retrieval of Biophysical Parameters through Model Inversion. In Proceedings of EARSeL 7th SIG-Imaging SpectroscopyWorkshop, Edinburgh, UK, 11-13 April 2011.
51. Verrelst, J.; Romijn, E.; Kooistra, L. Mapping vegetation density in a heterogeneous river floodplain ecosystem using pointable CHRIS/PROBA data. Remote Sens. 2012, 4, 2866-2889.
52. Jacquemoud, S.; Verhoef, W.; Baret, F.; Bacour, C.; Zarco-Tejada, P.; Asner, G.; François, C.; Ustin, S. PROSPECT + SAIL models: A review of use for vegetation characterization. Remote Sens. Environ. 2009, 113, S56-S66.
53. Feret, J.B.; François, C.; Asner, G.P.; Gitelson, A.A.; Martin, R.E.; Bidel, L.P.R.; Ustin, S.L.; le Maire, G.; Jacquemoud, S. PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments. Remote Sens. Environ. 2008, 112, 3030-3043.
54. Garrigues, S.; Lacaze, R.; Baret, F.; Morisette, J.; Weiss, M.; Nickeson, J.; Fernandes, R.; Plummer, S.; Shabanov, N.; Myneni, R.; et al. Validation and intercomparison of global leaf area index products derived from remote sensing data. J. Geophys. Res.-Biogeosci. 2008, 113, doi:10.1029/2007JG000635.
55. Pinty, B.; Lavergne, T.; Vobbeck, M.; Kaminski, T.; Aussedat, O.; Giering, R.; Gobron, N.; Taberner, M.; Verstraete, M.; Widlowski, J.L. Retrieving surface parameters for climate models from Moderate Resolution Imaging Spectroradiometer (MODIS)-Multiangle Imaging Spectroradiometer (MISR) albedo products. J. Geophy. Res.-Atmos. 2007, 112; doi:10.1029/2006JD008105.
56. Weiss, M.; Baret, F.; Garrigues, S.; Lacaze, R. LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: validation and comparison with MODIS collection 4 products. Remote Sens. Environ. 2007, 110, 317-331.
57. Clevers, J.; Gitelson, A. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3. ITC J. 2013, in press.
58. Delegido, J.; Verrelst, J.; Alonso, L.; Moreno, J. Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors 2011, 11, 7063-7081.
59. Verrelst, J.; Muñoz, J.; Alonso, L.; Delegido, J.; Rivera, J.; Camps-Valls, G.; Moreno, J. Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3. Remote Sens. Environ. 2012, 118, 127-139.
60. Richter, K.; Hank, T.; Vuolo, F.; Mauser, W.; D'Urso, G. Optimal exploitation of the Sentinel-2 spectral capabilities for crop leaf area index mapping. Remote Sens. 2012, 4, 561-582.
61. Gitelson, A.; Merzlyak, M.; Lichtenthaler, H. Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm. J. Plant Physiol. 1996, 148, 501-508.
62. Si, Y.; Schlerf, M.; Zurita-Milla, R.; Skidmore, A.; Wang, T. Mapping spatio-temporal variation of grassland quantity and quality using MERIS data and the PROSAIL model. Remote Sens. Environ. 2012, 121, 415-425.
63. Baret, F. Biophysical vegetation variables retrieval from remote sensing observations. Proc. SPIE 2010, 7824, 17-19.
64. Drusch, M.; Gascon, F.; Berger, M. Sentinel-2 Mission Requirements Document, 2010. Available online: http://esamultimedia.esa.int/docs/GMES/Sentinel-2MRD.pdf (accessed on 27 April 2013).
65. Atzberger, C. Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models. Remote Sens. Environ. 2004, 93, 53-67.
66. Meroni, M.; Colombo, R.; Panigada, C. Inversion of a radiative transfer model with hyperspectral observations for LAI mapping in poplar plantations. Remote Sens. Environ. 2004, 92, 195-206.
67. Brown, L.; Chen, J.; Leblanc, S.; Cihlar, J. A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests: An image and model analysis. Remote Sens. Environ. 2000, 71, 16-25.