Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data

International Journal of Applied Earth Observation and Geoinformation

Volumn 26, Issue 1, 2014, Pages 12-20

  Duan, Si Bo ^a,b,c   Li, Zhao Liang ^c,d   Wu, Hua ^a   Tang, Bo Hui ^a   Ma, Lingling ^e   Zhao, Enyu ^b,e   Li, Chuanrong ^e  

^a INSTITUTE OF GEOGRAPHIC SCIENCES AND NATURAL RESOURCES RESEARCH   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c CNRS   (France)

^d INSTITUTE OF PLANT PROTECTION   (China)

^e CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Dual angle observations; Hyperspectral; Leaf area index; Look up table; PROSAIL

Indexed keywords

ACCURACY ASSESSMENT; DATA INVERSION; ENERGY BALANCE; LEAF AREA INDEX; MAIZE; MASS BALANCE; OBSERVATIONAL METHOD; POTATO; RADIATIVE TRANSFER; SPECTRAL ANALYSIS; UNMANNED VEHICLE;

EID: 84897827083     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2013.05.007     Document Type: Article

References (53)

1. Asner, G.P., Scurlock, J.M.O., Hicke, J.A., 2003. Global synthesis of leaf area indexobservations: implications for ecological and remote sensing studies. GlobalEcology and Biogeography 12, 191-205.
2. Atzberger, C., 2004. Object-based retrieval of biophysical canopy variables using arti-ficial neural nets and radiative transfer models. Remote Sensing of Environment93, 53-67.
3. Atzberger, C., Richter, K., 2012. Spatially constrained inversion of radiative trans-fer models for improved LAI mapping from future Sentinel-2 imagery. RemoteSensing of Environment 120, 208-218.
4. Bacour, C., Baret, F., Béal, D., Weiss, M., Pavageau, K., 2006. Neural network estimationof LAI, fAPAR, fCover and LAI × Cab, from top of canopy MERIS reflectance data:principles and validation. Remote Sensing of Environment 105, 313-325.
5. Bacour, C., Jacquemoud, S., Leroy, M., Hautecoeur, O., Weiss, M., Prévot, L., Bruguier,N., Chauki, H., 2002. Reliability of the estimation of vegetation characteris-tics by inversion of three canopy reflectance models on airborne POLDER data.Agronomie 22, 555-565.
6. Baret, F., Buis, S., 2008. Estimating canopy characteristics from remote sensing obser-vations: review of methods and associated problems. In: Liang, S. (Ed.), Advancesin Land Remote Sensing: System, Modeling, Inversion and Application. Springer,pp. 173-201.
7. Baret, F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, M., Berthelot, B., Niño,F., Weiss, M., Samain, O., Roujean, J.L., Leroy, M., 2007. LAI, fAPAR and fCoverCYCLOPES global products derived from VEGETATION. Part 1: principles of thealgorithm. Remote Sensing of Environment 110, 275-286.
8. Bréda, N.J.J., 2003. Ground-based measurements of leaf area index: a review of meth-ods, instruments and current controversies. Journal of Experimental Botany 54, 2403-2417.
9. Bruegge, C.J., Conel, J.E., Green, R.O., Margolis, J.S., Holm, R.G., Toon, G., 1992. Watervapor column abundance retrievals during FIFE. Journal of Geophysical Research97, 18759-18768.
10. Campbell, G.S., 1990. Derivation of an angle density function for canopies with ellip-soidal leaf angle distributions. Agricultural and Forest Meteorology 49, 173-176.
11. Combal, B., Baret, F., Weiss, M., Trubuil, A., Mace, D., Pragnere, A., Myneni, R.,Knyazikhin, Y., Wang, L., 2002a. Retrieval of canopy biophysical variables frombidirectional reflectance-using prior information to solve the ill-posed inverseproblem. Remote Sensing of Environment 84, 1-15.
12. Combal, B., Baret, F., Weiss, M., 2002b. Improving canopy variables estimation fromremote sensing data by exploiting ancillary information. Case study on sugarbeet canopies. Agronomie 22, 205-215.
13. Darvishzadeh, R., Skidmore, A., Atzberger, C., van Wieren, S., 2008a. Estimation ofvegetation LAI from hyperspectral reflectance data: effects of soil type and plantarchitecture. International Journal of Applied Earth Observation and Geoinfor-mation 10, 358-373.
14. Darvishzadeh, R., Atzberger, C., Skidmore, A., Schlerf, M., 2011. Mapping grasslandleaf area index with airborne hyperspectral imagery: a comparison study ofstatistical approaches and inversion of radiative transfer models. ISPRS Journalof Photogrammetry and Remote Sensing 66, 894-906.
15. Darvishzadeh, R., Matkan, A.A., Ahangar, A.D., 2012. Inversion of a radiative transfermodel for estimation of rice canopy chlorophyll content using a lookup-tableapproach. IEEE Journal of Selected Topics in Applied Earth Observations andRemote Sensing 5, 1222-1230.
16. Darvishzadeh, R., Skidmore, A., Schlerf, M., Atzberger, C., 2008b. Inversion of aradiative transfer model for estimating vegetation LAI and chlorophyll in a het-erogeneous grassland. Remote Sensing of Environment 112, 2592-2604.
17. Dorigo, W.A., 2012. Improving the robustness of cotton status characterisation byradiative transfer model inversion of multi-angular CHRIS/PROBA data. IEEEJournal of Selected Topics in Applied Earth Observations and Remote Sensing5, 18-29.
18. Dorigo, W., Richter, R., Baret, F., Bamler, R., Wagner, W., 2009. Enhanced automatedcanopy characterization from hyperspectral data by a novel two step radiativetransfer model inversion approach. Remote Sensing 1, 1139-1170.
19. Dorigo, W.A., Zurita-Milla, R., de Wit, A.J.W., Brazile, J., Singh, R., Schaepman, M.E.,2007. A review on reflective remote sensing and data assimilation techniquesfor enhanced agroecosystem modeling. International Journal of Applied EarthObservation and Geoinformation 9, 165-193.
20. Duan, S.-B., Li, Z.-L., Tang, B.-H., Wu, H., Ma, L., Zhao, E., Li, C., 2013.Land surface reflectance retrieval from hyperspectral data collectedby an unmanned aerial vehicle over the Baotou test site. PLoS ONE,http://dx.doi.org/10.1371/journal.pone.0066972.
21. Estellés, V., Utrillas, M.P., Martínez-Lozano, J.A., Alcántara, A., Alados-Arboledas,L., Olmo, F.J., Lorente, J., de Cabo, X., Cachorro, V., Horvath, H., Labajo, A., Sor-ribas, M., Díaz, J.P., Díaz, A.M., Silva, A.M., Elías, T., Pujadas, M., Rodrigues, J.A.,Cañada, J., García, Y., 2006. Intercomparison of spectroradiometers and Sunphotometers for the determination of the aerosol optical depth during theVELETA-2002 field campaign. Journal of Geophysical Research 111, D17207,http://dx.doi.org/10.1029/2005JD006047.
22. 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., 2008. PROSPECT-4 and 5: advances in the leafoptical properties model separating photosynthetic pigments. Remote Sensingof Environment 112, 3030-3043.
23. Gao, B.-C., 1993. An operational method for estimating signal to noise ratios fromdata acquired with imaging spectrometers. Remote Sensing of Environment 43,23-33.
24. APAR, and net primary production of terrestrial ecosystems. RemoteSensing of Environment 70, 29-51.
25. Halthore, R.N., Eck, T.F., Holben, B.N., Markham, B.L., 1997. Sun photometric mea-surements of atmospheric water vapor column abundance in the 940-nm band.Journal of Geophysical Research 102, 4343-4352.
26. Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., Strachan, I.B., 2004. Hyper-spectral vegetation indices and novel algorithms for predicting green LAI ofcrop canopies: modeling and validation in the context of precision agriculture.Remote Sensing of Environment 90, 337-352.
27. Jacquemoud, S., Baret, F., 1990. PROSPECT: a model of leaf optical properties spectra.Remote Sensing of Environment 34, 75-91.
28. Jacquemoud, S., Baret, F., Andrieu, B., Danson, F.M., Jaggard, K., 1995. Extraction ofvegetation biophysical parameters by inversion of the PROSPECT + SAIL modelon sugar beet canopy reflectance data-application to TM data. Remote Sensingof Environment 52, 163-172.
29. Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P.J., Asner,G.P., François, C., Ustin, S.L., 2009. PROSPECT + SAIL models: a review ofuse for vegetation characterization. Remote Sensing of Environment 113,S56-S66.
30. Kimes, D.S., Knyazikhin, Y., Privette, J.L., Abuelgasim, A.A., Gao, F., 2000. Inversionmethods for physically-based models. Remote Sensing Reviews 18, 381-439.
31. Koetz, B., Baret, F., Poilvé, H., Hill, J., 2005. Use of coupled canopy structure dynamicand radiative transfer models to estimate biophysical canopy characteristics.Remote Sensing of Environment 95, 115-124.
32. Kuusk, A., 1995. A fast invertible canopy reflectance model. Remote Sensing of Envi-ronment 51, 342-350.
33. Li, Z.-L., Tang, R., Wan, Z., Bi, Y., Zhou, C., Tang, B., Yan, G., Zhang, X., 2009. Areview of current methodologies for regional evapotranspiration estimationfrom remotely sensed data. Sensors 9, 3801-3853.
34. Li, Z.-L., Tang, B.-H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I.F., Sobrino, J.A., 2013a.Satellite-derived land surface temperature: current status and perspectives.Remote Sensing of Environment 131, 14-37.
35. Li, Z.-L., Wu, H., Wang, N., Qiu, S., Sobrino, J.A., Wan, Z., Tang, B.-H., Yan, G., 2013b.Land surface emissivity retrieval from satellite data. International Journal ofRemote Sensing 34, 3084-3127.
36. Meroni, M., Colombo, R., Panigada, C., 2004. Inversion of a radiative transfer modelwith hyperspectral observations for LAI mapping in poplar plantations. RemoteSensing of Environment 92, 195-206.
37. Myneni, R.B., Hoffman, S., Knyazikhin, Y., Privette, J.L., Glassy, J., Tian, Y., Wang, Y.,Song, X., Zhang, Y., Smith, G.R., Lotsch, A., Friedl, M., Morisette, J.T., Votava,P., Nemani, R.R., Running, S.W., 2002. Global products of vegetation leaf areaand fraction absorbed PAR from year one of MODIS data. Remote Sensing ofEnvironment 83, 214-231.
38. Richter, K., Atzberger, C., Vuolo, F., D'Urso, G., 2009. Experimental assessment of theSentinel-2 band setting for RTM-based LAI retrieval of sugar beet and maize.Canadian Journal of Remote Sensing 35, 465-481.
39. Richter, K., Atzberger, C., Vuolo, F., D'Urso, G., 2011. Evaluation of Sentinel-2 spectralsampling for radiative transfer model based LAI estimation of wheat sugar beet,and maize. IEEE Journal of Selected Topics in Applied Earth Observations andRemote Sensing 4, 458-464.
40. Running, S.R., Baldocchi, D.D., Turner, D.P., Gower, S.T., Bakwin, P.S., Hibbard, K.A.,1999. A global terrestrial monitoring network integrating tower fluxes flasksampling, ecosystem modeling and EOS satellite data. Remote Sensing of Envi-ronment 70, 108-128.
41. Sandmeier, S., Müller, C., Hosgood, B., Andreoli, G., 1998. Physical mechanisms inhyperspectral BRDF data of grass and watercress. Remote Sensing of Environ-ment 66, 222-233.
42. Schlerf, M., Atzberger, C., 2012. Vegetation structure retrieval in beech and spruceforests using spectrodirectional satellite data. IEEE Journal of Selected Topics inApplied Earth Observations and Remote Sensing 5, 8-17.
43. Si, Y., Schlerf, M., Zurita-Milla, R., Skidmore, A.K., Wang, T., 2012. Mapping the spatio-temporal variation of grassland quantity and quality using MERIS data and thePROSAIL model. Remote Sensing of Environment 121, 415-425.
44. Verhoef, W., 1984. Light scattering by leaf layers with application to canopyreflectance modeling: The SAIL model. Remote Sensing of Environment 16,125-141.
45. Verhoef, W., 1985. Earth observation modeling based on layer scattering matrices.Remote Sensing of Environment 17, 165-178.
46. Verrelst, J., Romijn, E., Kooistra, L., 2012. Mapping vegetation density in a hetero-geneous river floodplain ecosystem using pointable CHRIS/PROBA data. RemoteSensing 4, 2866-2889.
47. Vohland, M., Mader, S., Dorigo, W., 2010. Applying different inversion techniques toretrieve stand variables of summer barley with PROSPECT + SAIL. InternationalJournal of Applied Earth Observation and Geoinformation 12, 71-80.
48. Vuolo, F., Dini, L., D'Urso, G., 2008. Retrieval of leaf area index from CHRIS/PROBAdata: an analysis of the directional and spectral information content. Interna-tional Journal of Remote Sensing 29, 5063-5072.
49. Weiss, M., Baret, F., Myneni, R.B., Pragnère, A., Knyazikhin, Y., 2000. Investigationof a model inversion technique to estimate canopy biophysical variables fromspectral and directional reflectance data. Agronomie 20, 3-22.
50. Yang, F., Sun, J., Fang, H., Yao, Z., Zhang, J., Zhu, Y., Song, K., Wang, Z., Hu, M., 2012.Comparison of different methods for corn LAI estimation over northeasternChina. International Journal of Applied Earth Observation and Geoinformation18, 462-471.
51. Yang, G., Zhao, C., Liu, Q., Huang, W., Wang, J., 2011. Inversion of a radiative trans-fer model for estimating forest LAI from multisource and multiangular opticalremote sensing data. IEEE Transactions on Geoscience and Remote Sensing 49, 988-1000.
52. Yao, Y., Liu, Q., Liu, Q., Li, X., 2008. LAI retrieval and uncertainty evaluations for typicalrow-planted crops at different growth stages. Remote Sensing of Environment112, 94-106.
53. Zhao, F., Gu, X., Verhoef, W., Wang, Q., Yu, T., Liu, Q., Huang, H., Qin, W., Chen, L.,Zhao, H., 2010. A spectral directional reflectance model of row crops. RemoteSensing of Environment 114, 265-285.