fAPAR estimates over the Iberian Peninsula by the inversion of the 4SAIL 2 radiative transfer model

Authors

  • B. Martínez Universitat de València
  • E. Albargues Earth Observation Laboratory (EOLAB)
  • F. Camacho Earth Observation Laboratory (EOLAB)
  • A. Moreno Universitat de València
  • M.A. Gilabert Universitat de València

DOI:

https://doi.org/10.4995/raet.2014.3177

Keywords:

fAPAR, 4SAIL2, inversion, ANNs

Abstract

This work aims to the estimation of fAPAR over the Iberian Peninsula using MODIS data. First, the 4SAIL2 and PROSPECT radiative transfer models have been used to simulate a data set of reflectance and fAPAR. Second, an artificial neuronal network (ANN) has been trained using the simulated data and finally, it has been inverted to derive fAPAR estimates over the Iberian Peninsula from MODIS reflectances images. Moreover, the impact that the observation and illumination configuration have on the fAPAR estimates has been assessed. The fAPAR estimates from MODIS have been compared with other validated fAPAR products. The results confirm an overall error around the user requirements (0.1) when the fAPAR estimated from the (PROSPECT+4SAIL2+Nadir) combination is compared with the selected products. This combination is proposed as an alternative to estimate fAPAR over the Iberian Peninsula due to the ability to characterize different land cover types as well as the high intra-annual variability of particular canopies. 

Downloads

Download data is not yet available.

Author Biographies

B. Martínez, Universitat de València

Dpt. Física de la Terra i Termodinàmica, Universitat de València

A. Moreno, Universitat de València

Dpt. Física de la Terra i Termodinàmica, Universitat de València

M.A. Gilabert, Universitat de València

Dpt. Física de la Terra i Termodinàmica, Universitat de València

References

Bacour, C., Baret, F., Beal, D., Weiss, M., Pavageau, K. 2006. Neural network estimation of LAI, fAPAR, fCover and LAI×Cab, from top of canopy MERIS reflectance data: principles and validation. Remote Sensing of Environment, 105(4): 313-325. http://dx.doi.org/10.1016/j.rse.2006.07.014

Baret, F., Guyot, G. 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35(2-3): 161-173. http://dx.doi.org/10.1016/0034-4257(91)90009-U

Baret, F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, M. et al. 2007. LAI, fAPAR and fCover CYCLOPES global products derived from

VEGETATION. Part 1: Principles of the algorithm. Remote Sensing of Environment, 110(3): 275-286. http://dx.doi.org/10.1016/j.rse.2007.02.018

Baret, F., Weiss, M., Lacaze, R., Camacho, F., Makhmara, H., Pacholcyzk, P., Smets, B. 2013. GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production. Remote Sensing of Environment, 137: 299-309. http://dx.doi.org/10.1016/j.rse.2012.12.027

Bishop, C.M. 1996. Neural networks: a pattern recognition perspective. Clarendon press, 482 pp.

Bolle, H.-J., Eckardt, M., Koslowsky, D., Maselli, F., Meliá-Miralles, J., Menenti, M. 2006. Mediterranean land-surface processes assessed from space. Berlin:Springer, 760 pp. http://dx.doi.org/10.1007/978-3-540-45310-9

Camacho, F., Cernicharo, J., Lacaze, R., Baret, F., Weiss, M. 2013. GEOV1: LAI, FAPAR Essential Climate Variables and FCOVER global time series capitalizing over existing products. Part 2: validation and intercomparison with reference products. Remote Sensing of Environment, 137: 310-329. http://dx.doi.org/10.1016/j.rse.2013.02.030

Cernicharo, J. 2010. Estimación del contenido de agua de la vegetación mediante inversión de modelos de transferencia radiativa a partir de redes neuronales. Proyecto final de carrera, Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Valencia. 65 pp.

Chen, J.M., Liu, J., Leblanc, S.G., Lacaze, R., Roujean, J.L. 2003. Multi-angular optical remote sensing for assessing vegetation structure and carbon absorption. Remote Sensing of Environment, 84(4): 516-525. http://dx.doi.org/10.1016/S0034-4257(02)00150-5

Dawson, T.P., Curran, P.J. Plummer, S.E. 1998. LIBERTY - Modeling the Effects of Leaf Biochemical Concentration on Reflectance Spectra. Remote Sensing of Environment, 65(1): 50-60. http://dx.doi.org/10.1016/S0034-4257(98)00007-8

Eberle, A. 2007. Design of an optimized spectral index to estimate vegetation water content for the Iberian Peninsula using MODIS data. Proyecto fin de carrera. Università degli Studi di Trento, 108 pp.

Féret, 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 leaf optical properties model separating photosynthetic pigments, Remote Sensing of Environment, 112(6): 3030-3043. http://dx.doi.org/10.1016/j.rse.2008.02.012

Ganapol, B.D., Johnson, L.F., Hlavka, C.A., Peterson, D.L. Bond, B. 1998. LCM2: A coupled leaf/ canopy radiative transfer model. Remote Sensing of Environment, 70(2): 153-166. http://dx.doi.org/10.1016/S0034-4257(99)00030-9

García-Haro, F.J., Camacho, F., Meliá, J. 2008. Vegetation Parameters Validation Report (VEGA VR), SAF/LAND/UV/VR VEGA/2.1, January 2008, 91 pp. Available on-line at http://landsaf. meteo.pt (accessed on 11 Novembre 2014). GCOS. 2006. Systematic observation requirements for satellite-based products for climate. (GCOS-107.WMO/TD No. 1338). September 2006. 103 pp. Geneve (Switzerland) Disponible en http://www.wmo.int/pages/prog/gcos/Publications/gcos-107.pdf (accessed on 11 Novembre 2014).

GCOS. 2010. Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (2010 Update). GCOS-138 (GOOS-184, GTOS-76, WMO-TD/No. 1523), 180 pp. Geneve (Switzerland) Disponible en http://www.wmo.int/pages/prog/gcos/Publications/gcos-138.pdf (accessed on 11 Novembre 2014).

Gilabert, M.A., Meliá, J. 1990. Usefulness of the temporal analysis and the normalized difference in the study of rice by means of Landsat-5 TM

images: identification and inventory of rice fields. Geocarto International, 5(4): 17-26. http://dx.doi.org/10.1080/10106049009354278

Gobron, N., Pinty, B., Verstraete, M., Govaerts, Y.,1999. The MERIS Global Vegetation Index (MGVI): description and preliminary application. International Journal of Remote Sensing, 20(9): 1917-1927. http://dx.doi.org/10.1080/014311699212542

Gobron, N., Pinty, B., Aussedat, O., Chen, J.M., Cohen, W.B., Fensholt, R., et al. 2006. Evaluation of fraction of absorbed photosynthetically active radiation products for different canopy radiation transfer regimes: methodology and results using Joint Research Center products derived from Sea-WiFS against ground-based estimations. Journal of Geophysical Research, 111 (D13):110, http://dx.doi.org/10.1029/2005JD006511

Haykin. S.S., 2009. Neural networks and learning machines, Vol. 10. 2009: Prentice Hall Upper Saddle River, NJ.

Jacquemoud, S., Baret, F., 1990. PROSPECT: a model of leaf optical properties spectra. Remote Sensing of Environment, 34(2): 75-91. http://dx.doi.org/10.1016/0034-4257(90)90100-Z

Jacquemoud, S., Verhoef W., Baret, F., Bacour, C., et al., 2009. PROSPECT + SAIL Models: a review of use for vegetation characterization, Remote Sensing of Environment, 113: S56-S66. http://dx.doi.org/10.1016/j.rse.2008.01.026

Knyazikhin, Y., Martonchik, J.V., Myneni, R.B., Dine, D.J., Running, S.W. 1998. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. Journal of Geophysical Research, 103(D24): 32257-32275. http://dx.doi.org/10.1029/98JD02462

Kuusk, A. 1995. A Markov chain model of canopy reflectance. Agricultural and Forest Meteorology, 76(3-4): 221-236. http://dx.doi.org/10.1016/0168-1923(94)02216-7

Leprieur, D., Verstraete, M.M., Pinty, B. 1994. Evaluation of the performance of various vegetation indices to retrieve cover from AVHRR data. Remote Sensing Reviews, 10(4): 265-284. http://dx.doi.org/10.1080/02757259409532250

Lourakis, M.I.A. 2005. A Brief Description of the Levenberg–Marquardt Algorithm Implemented by Levmar. Disponible en http://users.ics.forth.

gr/~lourakis/levmar/levmar.pdf (accessed on 11 Novembre 2014).

Lutch, W., Roujean, J.L. 2000. Considerations in the parametric modeling of BRDF and albedo from multiangle satellite sensor observations. Remote Sensing Reviews, 18(2-4): 343-380. http://dx.doi.org/10.1080/02757250009532395

Madsen, K., Nielsen, H.B., Tingleff, O. 2004. Methods for Non-linear Least Squares Problems, second ed. IMM, Technical University of Denmark, 50 pp. Disponible en http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=3215 (accessed on 11 Novembre 2014).

Martínez B., Camacho, F., Verger, A., García-Haro, F.J., Gilabert, M.A. 2013. Intercomparison and quality assessment of MERIS, MODIS and SEVIRI FAPAR products over the Iberian Peninsula. International Journal of Applied Earth Observation and Geoinformation, 21: 463-476. http://dx.doi.org/10.1016/j.jag.2012.06.010

Mccallum, I., Wagner, W., Schmullius, C., Shvidenko, A., Obersteiner, M., Fritz, S., Nilsson, S. 2010. Comparison of four global FAPAR datasets over Northern Eurasia for the year 2000. Remote Sensing of Environment, 114(5): 941-949. http://dx.doi.org/10.1016/j.rse.2009.12.009

Moreno, A., 2014. Retrieval and assessment of CO2 uptake by Mediterranean ecosystems using remote sensing and meteorological data. Tesis Doctoral, Universidad de Valencia, 172 pp.

Myneni, R.B., Nemani, R.R., Running, S.W. 1997. Estimation of global leaf area index and absorbed par using radiative transfer models. IEEE Transaction on Geoscience and Remote Sensing, 35(6): 1380-1393. http://dx.doi.org/10.1109/36.649788

Pérez-Hoyos, A., García-Haro, F.J., San Miguel Ayanz, J. 2012. A methodology to generate a synergetic land-cover map by fusion of different land-cover products. International Journal of Applied Earth

Observation and Geoinformation, 19: 72-87. http://dx.doi.org/10.1016/j.jag.2012.04.011

Rosema, A., Verhoef, W., Noorbergen, H., Borgesius, J.J. 1992. A new forest light interaction model in support of forest monitoring. Remote Sensing of Environment, 42(1): 23-41. http://dx.doi.org/10.1016/0034-4257(92)90065-R

Roujean, J.L., Bréon, F.M. 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51(3): 375-384. http://dx.doi.org/10.1016/0034-4257(94)00114-3

Schaaf, C.B., Gao, F., Strahler, A.H., Lucht, W., Li, X., Tsang T., et al. 2002. First operational BRDF, albedo and Nadir reflectance products from MODIS. Remote Sensing of Environment, 83(1-2): 135-148. http://dx.doi.org/10.1016/S0034-4257(02)00091-3

Snyman, J.A. 2005. Practical Mathematical Optimization: An introduction to basic optimization theory and classical and new gradient-based algorithms. Springer Publishing, 257 pp.

Verger, A., Baret, F., Weiss, M. 2008. Performances of neural networks for deriving LAI estimates from existing CYCLOPES and MODIS products, Remote Sensing of Environment, 112(6): 2789-2803. http://dx.doi.org/10.1016/j.rse.2008.01.006

Verhoef, W. 1984. Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model. Remote Sensing of Environment, 16(2): 125-141. http://dx.doi.org/10.1016/0034-4257(84)90057-9

Verhoef, W., Bach, H. 2007. Coupled soil-leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data. Remote Sensing of Environment, 109(2): 166-182. http://dx.doi.org/10.1016/j.rse.2006.12.013

Published

2014-12-16

Issue

Section

Research articles