A Bayesian Network-Based Method to Alleviate the Ill-Posed Inverse Problem: A Case Study on Leaf Area Index and Canopy Water Content Retrieval

IEEE Transactions on Geoscience and Remote Sensing

Volumn 53, Issue 12, 2015, Pages 6507-6517

  Quan, Xingwen ^a   He, Binbin ^a   Li, Xing ^a  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Bayesian network; canopy water content (CWC); ill posed inverse problem; leaf area index (LAI); prior joint probability distribution (PJPD); PROSAIL 5B radiative transfer model

Indexed keywords

BAYESIAN NETWORKS; MATRIX ALGEBRA; MEAN SQUARE ERROR; PROBABILITY; PROBABILITY DISTRIBUTIONS; RADIATIVE TRANSFER; VEGETATION;

CANOPY WATER CONTENTS; ILL-POSED INVERSE PROBLEM; JOINT PROBABILITY DISTRIBUTIONS; LEAF AREA INDEX; RADIATIVE TRANSFER MODEL;

INVERSE PROBLEMS;

BAYESIAN ANALYSIS; CANOPY EXCHANGE; INVERSE ANALYSIS; LANDSAT; LEAF AREA INDEX; MULTIVARIATE ANALYSIS; PRECISION; RADIATIVE TRANSFER; REMOTE SENSING; WATER CONTENT;

EID: 85027917680     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2015.2442999     Document Type: Article

References (51)

1. R. Houborg, M. Anderson, and C. Daughtry, "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., vol. 113, no. 1, pp. 259-274, Jan. 2009.
2. R. Houborg and E. Boegh, "Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance data," Remote Sens. Environ., vol. 112, no. 1, pp. 186-202, Jan. 2008.
3. R. Houborg, H. Soegaard, and E. Boegh, "Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data," Remote Sens. Environ., vol. 106, no. 1, pp. 39-58, Jan. 2007.
4. H. L. Fang, S. L. Liang, and A. Kuusk, "Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model," Remote Sens. Environ., vol. 85, no. 3, pp. 257-270, May 2003.
5. M. T. Yilmaz, E. R. Hunt, and T. J. Jackson, "Remote sensing of vegetation water content from equivalent water thickness using satellite imagery," Remote Sens. Environ., vol. 112, no. 5, pp. 2514-2522, May 2008.
6. R. Darvishzadeh, A. Skidmore, M. Schlerf, and C. Atzberger, "Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland," Remote Sens. Environ., vol. 112, no. 5, pp. 2592-2604, May 2008.
7. B. B. He, X. W. Quan, and M. F. Xing, "Retrieval of leaf area index in alpine wetlands using a two-layer canopy reflectance model," Int. J. Appl. Earth Observ. Geoinf., vol. 21, pp. 78-91, Apr. 2013.
8. P. J. Zarco-Tejada, C. A. Rueda, and S. L. Ustin, "Water content estimation in vegetation with MODIS reflectance data and model inversion methods," Remote Sens. Environ., vol. 85, no. 1, pp. 109-124, Apr. 2003.
9. M. Trombetti, D. Riano, M. A. Rubio, Y. B. Cheng, and S. L. Ustin, "Multi-temporal vegetation canopy water content retrieval and interpretation using artificial neural networks for the continental USA," Remote Sens. Environ., vol. 112, no. 1, pp. 203-215, Jan. 2008.
10. M. Yebra et al., "A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products," Remote Sens. Environ., vol. 136, pp. 455-468, Sep. 2013.
11. B. Combal et al., "Retrieval of canopy biophysical variables from bidirectional reflectance-Using prior information to solve the ill-posed inverse problem," Remote Sens. Environ., vol. 84, no. 1, pp. 1-15, Jan. 2003.
12. A. A. Gitelson, A. Vina, V. Ciganda, D. C. Rundquist, and T. J. Arkebauer, "Remote estimation of canopy chlorophyll content in crops," Geophys. Res. Lett., vol. 32, no. 8, pp. 1-4, Apr. 22, 2005.
13. C. J. Tucker, "Remote sensing of leaf water content in the near infrared," Remote Sens. Environ., vol. 10, no. 1, pp. 23-32, Aug. 1980.
14. R. Darvishzadeh et al., "LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements," ISPRS J. Photogramm., vol. 63, no. 4, pp. 409-426, Jul. 2008.
15. M. Meroni, R. Colombo, and C. Panigada, "Inversion of a radiative transfer model with hyperspectral observations for LAImapping in poplar plantations," Remote Sens. Environ., vol. 92, no. 2, pp. 195-206, Aug. 2004.
16. C. Atzberger, "Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models," Remote Sens. Environ., vol. 93, no. 1/2, pp. 53-67, Oct. 2004.
17. D. S. Kimes and R. F. Nelson, "Attributes of neural networks for extracting continuous vegetation variables from optical and radar measurements," Int. J. Remote Sens., vol. 19, no. 14, pp. 2639-2663, Sep. 1998.
18. S. Jacquemoud, C. Bacour, H. Poilve, and J. P. Frangi, "Comparison of four radiative transfer models to simulate plant canopies reflectance: Direct and inverse mode," Remote Sens. Environ., vol. 74, no. 3, pp. 471-481, Dec. 2000.
19. S. Jacquemoud et al., "Estimating leaf biochemistry using the PROSPECT leaf optical properties model," Remote Sens. Environ., vol. 56, no. 3, pp. 194-202, Jun. 1996.
20. J. Qin, S. L. Liang, X. W. Li, and J. D. Wang, "Development of the adjoint model of a canopy radiative transfer model for sensitivity study and inversion of leaf area index," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 7, pp. 2028-2037, Jul. 2008.
21. M. Yebra and E. Chuvieco, "Generation of a species-specific look-up table for fuel moisture content assessment," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 2, no. 1, pp. 21-26, Mar. 2009.
22. M. Weiss, F. Baret, R. B. Myneni, A. Pragnere, and Y. Knyazikhin, "Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data," Agronomie, vol. 20, no. 1, pp. 3-22, Jan./Feb. 2000.
23. M. Schlerf and C. Atzberger, "Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data," Remote Sens. Environ., vol. 100, no. 3, pp. 281-294, Feb. 2006.
24. C. Walthall et al., "A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery," Remote Sens. Environ., vol. 92, no. 4, pp. 465-474, Sep. 2004.
25. B. G. Marcot, R. S. Holthausen, M. G. Raphael, M. M. Rowland, and M. J. Wisdom, "Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement," Forest Ecol. Manage., vol. 153, no. 1-3, pp. 29-42, Nov. 2001.
26. M. Kalacska, A. Sanchez-Azofeifa, T. Caelli, B. Rivard, and B. Boerlage, "Estimating leaf area index from satellite imagery using Bayesian networks," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 8, pp. 1866-1873, Aug. 2005.
27. Y. H. Qu, J. D. Wang, H. W. Wan, X. W. Li, and G. Q. Zhou, "A Bayesian network algorithm for retrieving the characterization of land surface vegetation," Remote Sens. Environ., vol. 112, no. 3, pp. 613-622, Mar. 2008.
28. B. Combal, F. Baret, and M. Weiss, "Improving canopy variables estimation from remote sensing data by exploiting ancillary information. Case study on sugar beet canopies," Agronomie, vol. 22, no. 2, pp. 205-215, Mar. 2002.
29. T. Lavergne et al., "Application to MISR land products of an RPV model inversion package using adjoint and Hessian codes," Remote Sens. Environ., vol. 107, no. 1-2, pp. 362-375, Mar. 2007.
30. X.W. Quan, B. B. He, Y. Wang, Z. Tang, and X. Li, "An extended Fourier approach to improve the retrieved Leaf Area Index (LAI) in a time series from an alpine wetland," Remote Sens., vol. 6, no. 2, pp. 1171-1190, Feb. 2014.
31. A. Huete et al., "Overview of the radiometric and biophysical performance of the MODIS vegetation indices," Remote Sens. Environ., vol. 83, no. 1/2, pp. 195-213, Nov. 2002.
32. S. Jurdao, M. Yebra, J. P. Guerschman, and E. Chuvieco, "Regional estimation of woodland moisture content by inverting radiative transfer models," Remote Sens. Environ., vol. 132, pp. 59-70, May 2013.
33. L. L. Wang, E. R. Hunt, J. J. Qu, X. J. Hao, and C. S. T. Daughtry, "Remote sensing of fuel moisture content from ratios of narrow-band vegetation water and dry-matter indices," Remote Sens. Environ., vol. 129, pp. 103-110, Feb. 2013.
34. S. Jacquemoud et al., "PROSPECT plus SAIL models: A review of use for vegetation characterization," Remote Sens. Environ., vol. 113, pp. S56-S66, Sep. 2009.
35. G. le Maire et al., "Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass," Remote Sens. Environ., vol. 112, no. 10, pp. 3846-3864, Oct. 2008.
36. S. Jacquemoud, and F. Baret, "PROSPECT: A model of leaf optical properties spectra," Remote Sens. Environ., vol. 34, no. 2, pp. 75-91, Nov. 1990.
37. T. Fourty, F. Baret, S. Jacquemoud, G. Schmuck, and J. Verdebout, "Leaf optical properties with explicit description of its biochemical composition: Direct and inverse problems," Remote Sens. Environ., vol. 56, no. 2, pp. 104-117, May 1996.
38. J. B. Feret et al., "PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments," Remote Sens. Environ., vol. 112, no. 6, pp. 3030-3043, Jun. 2008.
39. L. Bousquet, S. Lacherade, S. Jacquemoud, and I. Moya, "Leaf BRDF measurements and model for specular and diffuse components differentiation," Remote Sens. Environ., vol. 98, no. 2/3, pp. 201-211, Oct. 2005.
40. W. Verhoef, L. Jia, Q. Xiao, and Z. Su, "Unified optical-thermal fourstream radiative transfer theory for homogeneous vegetation canopies," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 6, pp. 1808-1822, Jun. 2007.
41. W. Verhoef and H. Bach, "Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models," Remote Sens. Environ., vol. 87, no. 1, pp. 23-41, Sep. 2003.
42. W. Verhoef, "Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model," Remote Sens. Environ., vol. 16, no. 2, pp. 125-141, Oct. 1984.
43. W. Verhoef, "Earth observation modeling based on layer scattering matrices," Remote Sens. Environ., vol. 17, no. 2, pp. 165-178, Apr. 1985.
44. C. Bacour, S. Jacquemoud, Y. Tourbier, M. Dechambre, and J. P. Frangi, "Design and analysis of numerical experiments to compare four canopy reflectance models," Remote Sens. Environ., vol. 79, no. 1, pp. 72-83, Jan. 2002.
45. A. Kuusk, "The hot spot effect in plant canopy reflectance," in Photon-Vegetation Interactions. New York, NY, USA: Springer-Verlag, 1991, pp. 139-159.
46. R. B. Myneni et al., "Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data," Remote Sens. Environ., vol. 83, no. 1/2, pp. 214-231, Nov. 2002.
47. J. B. Feret et al., "Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling," Remote Sens. Environ., vol. 115, no. 10, pp. 2742-2750, Oct. 2011.
48. Y. T. Mustafa, P. E. Van Laake, and A. Stein, "Bayesian network modeling for improving forest growth estimates," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 2, pp. 639-649, Feb. 2011.
49. Y. T. Mustafa, V. A. Tolpekin, and A. Stein, "Application of the expectation maximization algorithm to estimate missing values in Gaussian Bayesian network modeling for forest growth," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 5, pp. 1821-1831, May 2012.
50. M. Yebra, E. Chuvieco, and D. Riano, "Estimation of live fuel moisture content fromMODIS images for fire risk assessment," Agricultural Forest Meteorol., vol. 148, no. 4, pp. 523-536, Apr. 2008.
51. M. Yebra and E. Chuvieco, "Linking ecological information and radiative transfer models to estimate fuel moisture content in the Mediterranean region of Spain: Solving the ill-posed inverse problem," Remote Sens. Environ., vol. 113, no. 11, pp. 2403-2411, Nov. 2009.