Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection

Remote Sensing of Environment

Volumn 132, Issue , 2013, Pages 102-119

  Zhao, Kaiguang ^a   Valle, Denis ^a   Popescu, Sorin ^b   Zhang, Xuesong ^c   Mallick, Bani ^b  

^a DUKE UNIVERSITY   (United States)

^b TEXAS A AND M UNIVERSITY   (United States)

^c UNIVERSITY OF MARYLAND   (United States)

Author keywords

Band selection; Bayesian model averaging; Carbon; Carotenoid; Chlorophyll; Hyperspectral; Leaf pigment; MCMC; Model misspecification; Model selection; Model uncertainty; Nitrogen; Plant biochemistry

Indexed keywords

BAND SELECTION; BAYESIAN MODEL AVERAGING; CAROTENOID; HYPERSPECTRAL; MCMC; MISSPECIFICATION; MODEL SELECTION; MODEL UNCERTAINTIES;

BIOCHEMISTRY; CARBON; CHLOROPHYLL; ESTIMATION; HYDROGEN; INFERENCE ENGINES; NITROGEN; PIGMENTS; REMOTE SENSING; UNCERTAINTY ANALYSIS;

BAYESIAN NETWORKS;

BAYESIAN ANALYSIS; BIOCHEMICAL COMPOSITION; CARBON; DATA SET; NITROGEN; PHYTOCHEMISTRY; PIGMENT; REMOTE SENSING; SPECTROSCOPY; UNCERTAINTY ANALYSIS;

EID: 84873503436     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2012.12.026     Document Type: Article

References (55)

1. ACCP Accelerated canopy chemistry program final report to NASA-EOS-IWG 1994, National Aeronautics and Space Administration, Washington DC.
2. Asner G.P., Martin R.E. Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels. Remote Sensing of Environment 2008, 112:3958-3970.
3. Asner G.P., Martin R.E. Airborne spectranomics: Mapping canopy chemical and taxonomic diversity in tropical forests. Frontiers in Ecology and the Environment 2009, 7:269-276.
4. Asner G.P., Martin R.E., Tupayachi R., Emerson R., Martinez P., Sinca F., et al. Taxonomy and remote sensing of leaf mass per area (LMA) in humid tropical forests. Ecological Applications 2011, 21:85-98.
5. Atzberger C., Guerif M., Baret F., Werner W. Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat. Computers and Electronics in Agriculture 2010, 73:165-173.
6. Bolster K.L., Martin M.E., Aber J.D. Determination of carbon fraction and nitrogen concentration in tree foliage by near infrared reflectance: A comparison of statistical methods. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere 1996, 26:590-600.
7. Brown P.J., Vannucci M., Fearn T. Multivariate Bayesian variable selection and prediction. Journal of the Royal Statistical Society Series B-Statistical Methodology 1998, 60:627-641.
8. Chen T., Martin E. Bayesian linear regression and variable selection for spectroscopic calibration. Analytica Chimica Acta 2009, 631:13-21.
9. Chen T., Morris J., Martin E. Gaussian process regression for multivariate spectroscopic calibration. Chemometrics and Intelligent Laboratory Systems 2007, 87:59-67.
10. Chen T., Wang B. Bayesian variable selection for Gaussian process regression: Application to chemometric calibration of spectrometers. Neurocomputing 2010, 73:2718-2726.
11. Curran P.J. Remote-sensing of foliar chemistry. Remote Sensing of Environment 1989, 30:271-278.
12. 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 Sensing of Environment 2008, 112:2592-2604.
13. Dawson T.P., Curran P.J., Plummer S.E. LIBERTY - Modeling the effects of leaf biochemical concentration on reflectance spectra. Remote Sensing of Environment 1998, 65:50-60.
14. Denison D.G.T. Bayesian methods for nonlinear classification and regression 2002, Wiley, Chichester.
15. Fan Y., Sisson S.A. Reversible jump Markov chain Monte Carlo 2010, (Arxiv preprint arXiv:1001.2055).
16. Feret J.B., Francois C., Asner G.P., Gitelson A.A., Martin R.E., Bidel L.P.R., et al. PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments. Remote Sensing of Environment 2008, 112:3030-3043.
17. Feret J.B., Francois C., Gitelson A., Asner G.P., Barry K.M., Panigada C., et al. Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling. Remote Sensing of Environment 2011, 115:2742-2750.
18. Gamon J.A., Serrano L., Surfus J.S. The photochemical reflectance index: An optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia 1997, 112:492-501.
19. Garbulsky M.F., Penuelas J., Gamon J., Inoue Y., Filella I. The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies. A review and meta-analysis. Remote Sensing of Environment 2011, 115:281-297.
20. Gelman A. Bayesian data analysis 2004, Chapman & Hall/CRC, Boca Raton, Fla; London. 2nd ed.
21. Gitelson A.A., Buschmann C., Lichtenthaler H.K. The chlorophyll fluorescence ratio F-735/F-700 as an accurate measure of the chlorophyll content in plants. Remote Sensing of Environment 1999, 69:296-302.
22. Gitelson A.A., Gritz Y., Merzlyak M.N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology 2003, 160:271-282.
23. Gitelson A.A., Keydan G.P., Merzlyak M.N. Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophysical Research Letters 2006, 33.
24. Gitelson A.A., Vina A., Ciganda V., Rundquist D.C., Arkebauer T.J. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters 2005, 32.
25. Grossman Y.L., Ustin S.L., Jacquemoud S., Sanderson E.W., Schmuck G., Verdebout J. Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data. Remote Sensing of Environment 1996, 56:182-193.
26. Hastie T., Tibshirani R., Friedman J.H., MyiLibrary The elements of statistical learning data mining, inference, and prediction: with 200 full-color illustrations. Springer series in statistics 2001, Springer, New York, (pp. xvi, 533 pp.).
27. Hoeting J.A., Madigan D., Raftery A.E., Volinsky C.T. Bayesian model averaging: A tutorial. Statistical Science 1999, 14:382-401.
28. Hosgood B., Jacquemoud S., Andreoli G., Verdebout J., Pedrini A., Schmuck G. Leaf Optical Properties EXperiment 93 (LOPEX93) 1994, European Commission - Joint Research Centre EUR 16095, Ispra (Italy).
29. Jackson R.B., Randerson J.T., Canadell J.G., Anderson R.G., Avissar R., Baldocchi D.D., et al. Protecting climate with forests. Environmental Research Letters 2008, 3.
30. Jacquemoud S., Baret F. Prospect - A model of leaf optical-properties spectra. Remote Sensing of Environment 1990, 34:75-91.
31. Jacquemoud S., Verdebout J., Schmuck G., Andreoli G., Hosgood B. Investigation of leaf biochemistry by statistics. Remote Sensing of Environment 1995, 54:180-188.
32. Knyazikhin Y., Schull M.A., Stenberg P., Mõttus M., Rautiainen M., Yang Y., et al. Hyperspectral remote sensing of foliar nitrogen content. Proceedings of the National Academy of Sciences 2012.
33. Kokaly R.F., Asner G.P., Ollinger S.V., Martin M.E., Wessman C.A. Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sensing of Environment 2009, 113:S78-S91.
34. le Maire G., Francois C., Dufrene E. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment 2004, 89:1-28.
35. Li L., Ustin S.L., Riano D. Retrieval of fresh leaf fuel moisture content using genetic algorithm partial least squares (GA-PLS) modeling. IEEE Geoscience and Remote Sensing Letters 2007, 4:216-220.
36. Liang S.L. Recent developments in estimating land surface biogeophysical variables from optical remote sensing. Progress in Physical Geography 2007, 31:501-516.
37. Martin M.E., Plourde L.C., Ollinger S.V., Smith M.L., McNeil B.E. A generalizable method for remote sensing of canopy nitrogen across a wide range of forest ecosystems. Remote Sensing of Environment 2008, 112:3511-3519.
38. Milton E.J., Schaepman M.E., Anderson K., Kneubuhler M., Fox N. Progress in field spectroscopy. Remote Sensing of Environment 2009, 113:S92-S109.
39. Nguyen H.T., Lee B.W. Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. European Journal of Agronomy 2006, 24:349-356.
40. Pasolli L., Melgani F., Blanzieri E. Gaussian process regression for estimating chlorophyll concentration in subsurface waters from remote sensing data. IEEE Geoscience and Remote Sensing Letters 2010, 7:464-468.
41. Richardson A.D., Duigan S.P., Berlyn G.P. An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytologist 2002, 153:185-194.
42. Schlerf M., Atzberger C., Hill J., Buddenbaum H., Werner W., Schuler G. Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy. International Journal of Applied Earth Observation and Geoinformation 2010, 12:17-26.
43. Serrano L., Penuelas J., Ustin S.L. Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals. Remote Sensing of Environment 2002, 81:355-364.
44. Sims D.A., Gamon J.A. Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: A comparison of indices based on liquid water and chlorophyll absorption features. Remote Sensing of Environment 2003, 84:526-537.
45. Sloughter J.M., Raftery A.E., Gneiting T., Fraley C. Probabilistic quantitative precipitation forecasting using Bayesian model averaging. Monthly Weather Review 2007, 135:3209-3220.
46. Townsend P.A., Foster J.R., Chastain R.A., Currie W.S. Application of imaging spectroscopy to mapping canopy nitrogen in the forests of the central Appalachian Mountains using Hyperion and AVIRIS. IEEE Transactions on Geoscience and Remote Sensing 2003, 41:1347-1354.
47. Ustin S.L., Roberts D.A., Gamon J.A., Asner G.P., Green R.O. Using imaging spectroscopy to study ecosystem processes and properties. Bioscience 2004, 54:523-534.
48. Wintle B.A., McCarthy M.A., Volinsky C.T., Kavanagh R.P. The use of Bayesian model averaging to better represent uncertainty in ecological models. Conservation Biology 2003, 17:1579-1590.
49. Wold S., Sjostrom M., Eriksson L. PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 2001, 58:109-130.
50. Zarco-Tejada P.J., Miller J.R., Noland T.L., Mohammed G.H., Sampson P.H. Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 2001, 39:1491-1507.
51. Zhang X., Zhao K. Bayesian neural networks for uncertainty analysis of hydrologic modeling: A comparison of two schemes. Water Resources Management 2012, 1-18.
52. Zhang Y.Q., Chen J.M., Miller J.R., Noland T.L. Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery. Remote Sensing of Environment 2008, 112:3234-3247.
53. Zhao K.G., Popescu S. Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA. Remote Sensing of Environment 2009, 113:1628-1645.
54. Zhao K.G., Popescu S., Meng X.L., Pang Y., Agca M. Characterizing forest canopy structure with lidar composite metrics and machine learning. Remote Sensing of Environment 2011, 115:1978-1996.
55. Zhao K.G., Popescu S., Zhang X.S. Bayesian learning with Gaussian processes for supervised classification of hyperspectral data. Photogrammetric Engineering and Remote Sensing 2008, 74:1223-1234.