Retrieval of seasonal leaf area index from simulated EnMAP data through optimized LUT-based inversion of the PROSAIL model

Remote Sensing

Volumn 7, Issue 8, 2015, Pages 10321-10346

  Locherer, Matthias ^a   Hank, Tobias ^a   Danner, Martin ^a   Mauser, Wolfram ^a  

^a UNIVERSITY OF MUNICH   (Germany)

Author keywords

Agriculture; EnMAP; Hyperspectral; LAI; LUT; Model inversion; PROSAIL; Radiative transfer model; Simulated data; Vegetation

Indexed keywords

AGRICULTURE; COST FUNCTIONS; RADIATIVE TRANSFER; TABLE LOOKUP; VEGETATION;

ENMAP; HYPERSPECTRAL; LAI; LUT; MODEL INVERSION; PROSAIL; RADIATIVE TRANSFER MODEL; SIMULATED DATA;

INVERSE PROBLEMS;

EID: 84939429618     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs70810321     Document Type: Article

References (41)

1. Van Ittersum, M.K.; Cassman, K.G.; Grassini, P.; Wolf, J.; Tittonell, P.; Hochman, Z. Yield gap analysis with local to global relevance-A review. Field Crops Res. 2013, 143, 4-17.
2. Kaufmann, H.; Förster, S.; Wulf, H.; Segl, K.; Guanter, L.; Bochow, M.; Heiden, U.; Mueller, A.; Heldens, W.; Schneiderhan, T.; et al. Science Plan of the Environmental Mapping and Analysis Program (EnMAP); Deutsches GeoForschungsZentrum GFZ: Potsdam, Germany, 2012; p. 63.
3. Govender, M.; Chetty, K.; Bulcock, H. A review of hyperspectral remote sensing and its application in vegetation and water resource studies. Water SA 2007, 33, doi:10.4314/wsa.v33i2.49049.
4. Guanter, L.; Kaufmann, H.; Segl, K.; Foerster, S.; Rogass, C.; Chabrillat, S.; Kuester, T.; Hollstein, A.; Rossner, G.; Chlebek, C.; et al. The EnMAP spaceborne imaging spectroscopy mission for earth observation. Remote Sens. 2015, 7, 8830-8857.
5. Duveiller, G.; Weiss, M.; Baret, F.; Defourny, P. Retrieving wheat green area index during the growing season from optical time series measurements based on neural network radiative transfer inversion. Remote Sens. Environ. 2011, 115, 887-896.
6. 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.
7. Le Maire, G.; Marsden, C.; Verhoef, W.; Ponzoni, F.J.; Seen, D.L.; Bégué, A.; Stape, J.L.; Nouvellon, Y. Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of eucalyptus plantations. Remote Sens. Environ. 2011, 115, 586-599.
8. Richter, K.; Vuolo, F.; D'Urso, G.; Dini, L. Evaluation of different methods for the retrieval of LAI using high resolution airborne data. Proc. SPIE 2007, doi:10.1117/12.738167.
9. Stevens, A.; Udelhoven, T.; Denis, A.; Tychon, B.; Lioy, R.; Hoffmann, L.; van Wesemael, B. Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy. Geoderma 2010, 158, 32-45.
10. Oppelt, N.; Mauser, W. Hyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data. Int. J. Remote. Sens. 2004, 25, 145-159.
11. Weiss, M.; Troufleau, D.; Baret, F.; Chauki, H.; Prévot, L.; Olioso, A.; Bruguier, N.; Brisson, N. Coupling canopy functioning and radiative transfer models for remote sensing data assimilation. Agric. For. Meteorol. 2001, 108, 113-128.
12. Hank, T.; Bach, H.; Mauser, W. Using a remote sening-supported hydro-agroecological model for field-scale simulation of heterogeneous crop growth and yield: Application for wheat in central europe. Remote Sens. 2015, 7, 3934-3965.
13. Jacquemoud, S.; Verhoef, W.; Baret, F.; Bacour, C.; Zarco-Tejada, P.J.; Asner, G.P.; Francois, C.; Ustin, S.L. PROSPECT plus SAIL models: A review of use for vegetation characterization. Remote Sens. Environ. 2009, 113, S56-S66.
14. Jacquemoud, S.; Baret, F.; Andrieu, B.; Danson, F.M.; Jaggard, K. Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRS sensors. Remote Sens. Environ. 1995, 52, 163-172.
15. 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.
16. Baret, F.; Buis, S. Estimating canopy characteristics from remote sensing observations: Review of methods and associated problems. In Advances in Land Remote Sensing; Springer: Dordrecht, The Netherlands, 2008, pp. 173-201.
17. Jacquemoud, S.; Bacour, C.; Poilvé, H.; Frangi, J.P. Comparison of four radiative transfer models to simulate plant canopies reflectance: Direct and inverse mode. Remote Sens. Environ. 2000, 74, 471-481.
18. Verhoef, W.; Bach, H. Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models. Remote Sens. Environ. 2003, 87, 23-41.
19. Oppelt, N.; Mauser, W. Airborne visible/infrared imaging spectrometer AVIS: Design, characterization and calibration. Sensors 2007, 7, 1934-1953.
20. Baumgartner, A.; Gege, P.; Köhler, C.; Lenhard, K.; Schwarzmaier, T. Characterisation methods for the hyperspectral sensor HySpex at DLR's calibration home base. Proc. SPIE Remote Sens. Agric. Ecosyst. Hydrol. XVI 2012, doi:10.1117/12.974664.
21. Richter, R.; Schläpfer, D. Atmospheric/Topographic Correction for Airborne Imagery, ATCOR-4 User Guide, Version 4.2; DLR: Wessling, Germany, 2007; p.125.
22. Segl, K.; Guanter, L.; Rogass, C.; Kuester, T.; Roessner, S.; Kaufmann, H.; Sang, B.; Mogulsky, V.; Hofer, S. Eetes-The EnMAP end-to-end simulation tool. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2012, 5, 522-530.
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. Kimes, D.S.; Knyazikhin, Y.; Privette, J.L.; Abuelgasim, A.A.; Gao, F. Inversion methods for physically-based models. Remote Sens. Rev. 2000, 18, 381-439.
25. Darvishzadeh, R.; Atzberger, C.; Skidmore, A.; Schlerf, M. Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models. ISPRS J. Photogramm. 2011, 66, 894-906.
26. Combal, B.; Baret, F.; Weiss, M.; Trubuil, A.; Macé, 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.
27. Vuolo, F.; Atzberger, C.; Richter, K.; D'Urso, G.; Dash, J. Retrieval of biophysical vegetation products from rapideye imagery. Proc. ISPRS Tech. Comm. VII Symp. -100 Years ISPRS -Adv. Remote Sens. Sci. 2010, XXXVIII, 281-286.
28. Durbha, S.S.; King, R.L.; Younan, N.H. Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer. Remote Sens. Environ. 2007, 107, 348-361.
29. Darvishzadeh, R.; Matkan, A.A.; Ahangar, A.D. 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, 5, 1222-1230.
30. Weiss, M.; Baret, F.; Myneni, R.B.; 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.
31. 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.
32. Baret, F.; Hagolle, O.; Geiger, B.; Bicheron, P.; Miras, B.; Huc, M.; Berthelot, B.; Nino, 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.
33. Verger, A.; Baret, F.; Camacho, F. Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with Chris/Proba observations. Remote Sens. Environ. 2011, 115, 415-426.
34. 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.
35. Lavergne, T.; Kaminski, T.; Pinty, B.; Taberner, M.; Gobron, N.; Verstraete, M.M.; Vossbeck, M.; Widlowski, J.L.; Giering, R. Application to MISR land products of an RPV model inversion package using adjoint and hessian codes. Remote Sens. Environ. 2007, 107, 362-375.
36. Verrelst, J.; Rivera, J.P.; Leonenko, G.; Alonso, L.; Moreno, J. Optimizing LUT-based RTM inversion for semiautomatic mapping of crop biophysical parameters from Sentinel-2 and -3 data: Role of cost functions. Geosci. Remote Sens. IEEE Trans. 2014, 52, 257-269.
37. Rivera, J.; Verrelst, J.; Leonenko, G.; Moreno, J. Multiple cost functions and regularization options for improved retrieval of leaf chlorophyll content and LAI through inversion of the PROSAIL model. Remote Sens. 2013, 5, 3280-3304.
38. Leonenko, G.; Los, S.; North, P. Statistical distances and their applications to biophysical parameter estimation: Information measures, M-estimates, and minimum contrast methods. Remote Sens. 2013, 5, 1355-1388.
39. Wainwright, J.; Mulligan, M. Environmental Modelling: Finding Simplicity in Complexity; John Wiley & Sons: Hoboken, NJ, USA, 2005.
40. Staudte, R.G.; Sheather, S.J. Robust Estimation and Testing; John Wiley & Sons: Hoboken, NJ, USA, 2011.
41. Richter, K.; Atzberger, C.; Hank, T.B.; Mauser, W. Derivation of biophysical variables from earth observation data: Validation and statistical measures. J. Appl. Remote Sens. 2012, 6, doi:10.1117/1.JRS.6.063557.