Influencia del ángulo de observación en la estimación del índice de área foliar (LAI) mediante imágenes PROBA/CHRIS

  1. Delegido, J.
  2. Meza, C. M.
  3. Pasqualotto, N.
  4. Moreno, J.
Journal:
Revista de teledetección: Revista de la Asociación Española de Teledetección

ISSN: 1133-0953

Year of publication: 2016

Issue: 46

Pages: 45-55

Type: Article

DOI: 10.4995/RAET.2016.4612 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Revista de teledetección: Revista de la Asociación Española de Teledetección

Sustainable development goals

Abstract

The estimation of biophysical variables, such as the Leaf Area Index (LAI), using remote sensing techniques, is still the subject of numerous studies, since these variables allow obtaining valuable information on the vegetation status. In this work, we estimate LAI from multiangular PROBA/CHRIS images, by analyzing the reflectance measured in its 5 observation angles, for the bands centered in 665 and 705 nm. These wavelengths correspond to the chlorophyll absorption band and the Red-Edge region, respectively. The Normalized Difference Index (NDI) calculated from this wavelengths, shows good correlation with LAI and allows its remote sensing estimation and its applicability to the recently launched ESA Sentinel 2, thanks to its new bands in the Red-Edge. This research analyzed the influence on the acquisition geometry in the NDI, calibrating the relationship between this index and the LAI for each of the five observation angles in the PROBA / CHRIS images. As a result, we have obtained a relationship capable of providing LAI from the viewing angle and the NDI index.

Bibliographic References

  • Alonso, L., Moreno, J. 2004. Quasi-Automatic Geometric Correction and Related Geometric Issues in the Exploitation of CHRIS/Proba Data. En: Proceedings of the second CHRIS/Proba Workshop, 28-30 April 2004, ESA/ESRIN, Frascati, Italy (ESA SP-578, July 2004).
  • Bacour, C., Baret, F., Béal, 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., Buis, S. 2007. Estimating canopy characteristics from remote sensing observations. Review of methods and associated problems. In S. Liang (ed.), Advances in Land Remote Sensing: System, Modeling, Inversion and Application. Springer Netherlands. pp. 173-201. http://dx.doi. org/10.1007/978-1-4020-6450-0_7
  • Baret, F., Guyot, G. 1991. Potentials and limits of vegetation indices for LAI and PAR assessment. Remote Sensing of Environment. 35(2-3), 161-173. http://dx.doi.org/10.1016/0034-4257(91)90009-U
  • Bonan, G. 1993. Importance of leaf area index and forest type when estimating photosynthesis in boreal forests. Remote Sensing of Environment, 43(3), 303-314. http://dx.doi.org/10.1016/00344257(93)90072-6
  • Camacho, F., García-Haro, J., Gilabert, M. A., Meliá, J. 2002. La anisotropía de la BRDF: Una nueva signatura de las cubiertas vegetales. Revista de Teledetección. 18, 29-46.
  • Chen, J. M., Pavlic, G., Brown, L., Cihlar, J., Leblanc, S. G., White, H. P., Hall, R. J., Peddle, D.R., King, D.J., Trofymow, J.A., Swift, E., Van der Sanden, J., Pellikka, P. K. E. 2002. Derivation and validation of Canada-wide coarseresolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote Sensing of Environment, 80(1), 165-184. http://dx.doi.org/10.1016/S00344257(01)00300-5
  • Delegido, J., Fernández, G., Gandía, S., Moreno, J. 2008. Retrieval of chlorophyll content and LAI of crops using hyperspectral techniques: Application to PROBA/CHRIS data. International Journal of Remote Sensing 29(24), 7107-7127. http://dx.doi. org/10.1080/01431160802238401
  • Delegido, J., Verrelst, J., Alonso L., Moreno, J. 2011. Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content. Sensors, 11(7), 7063-7081. http://dx.doi. org/10.3390/s110707063
  • Delegido, J., Verrelst, J., Meza, C. M., Rivera, J.P. Alonso, L., Moreno, J. 2013. A red-edge spectral index for remote sensing estimation of green LAI over Agroecosystems. European Journal Agronomy, 46, 42-52. http://dx.doi.org/10.1016/j. eja.2012.12.001
  • Delegido, J., Verrelst, J., Rivera, J.P., Ruiz-Verdú, A., Moreno, J. 2015. Brown and green LAI mapping through spectral indices. International Journal of Applied Earth Observation and Geoinformation, 35(B), 350-358. http://dx.doi.org/10.1016/j. jag.2014.10.001
  • Dong, T., Meng, J., Shang, J., Liu, J., Wu, B. 2015. Evaluation of Chlorophyll-Related Vegetation Indices Using Simulated Sentinel-2 Data for Estimation of Crop Fraction of Absorbed Photosynthetically Active Radiation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(8), 40494059. http://dx.doi. org/10.1109/JSTARS.2015.2400134
  • Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., Bargellini, P. 2012. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120, 25-36. http://dx.doi. org/10.1016/j.rse.2011.11.026
  • ESA. 2009. SEN3EXP Ground Measurement Acquisition Report Vegetation Parameters. Barrax site, 20-24 June, 2009.
  • ESA. 2016. GMES observing the earth, Sentinel-2. Último acceso: 10 de Enero, 2016, de https:// directory.eoportal.org/web/eoportal/satellitemissions/c-missions/copernicus-sentinel-2
  • Galvão, L. S., Breunig, F.M., Santos, J. R., Moura, Y.M. 2013. View-illumination effects on hyperspectral vegetation indices in the Amazonian tropical forest. International Journal of Applied Earth Observation and Geoinformation, 21, 291-300. http://dx.doi. org/10.1016/j.jag.2012.07.005
  • Gilabert, M. A., González-Piqueras, J., García-Haro, J. 1997. Acerca de los índices de vegetación. Revista de Teledetección, 8, 1-10.
  • Guanter, L., Alonso, L., Moreno, J. 2005. A method for the surface reflectance retrieval from Proba/ CHRIS data over land: Application to ESA SPARC campaigns. Geoscience and Remote Sensing, IEEE Transactions on, 43(12), 2908-2917. http://dx.doi. org/10.1109/TGRS.2005.857915
  • Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada., J. P., Strachan, I. B. 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the content of precision agriculture. Remote Sensing of Environment, 90(3), 337-352. http://dx.doi. org/10.1016/j.rse.2003.12.013
  • He, Y., Guo, X., Wilmshurst, J. 2006. Studying mixed grassland ecosystems I: suitable hyperspectral vegetation indices. Journal Remote Sensing, 32(2), 98-107. http://dx.doi.org/10.5589/m06-009
  • He, L., Song, X., Feng, W., Guo, B. B., Zhang, Y. S., Wang, Y. H., Wang, C. Y., Guo, T. C. 2016. Improved remote sensing of leaf nitrogen concentration in winter wheat using multi-angular hyperspectral data. Remote Sensing of Environment, 174, 122-133. http://dx.doi.org/10.1016/j.rse.2015.12.007
  • Huete, A. R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309. http://dx.doi.org/10.1016/00344257(88)90106-X
  • Jiménez-Muñoz, J. C., Sobrino, J. A., Guanter, L., Moreno, J., Plaza, A., Martínez, P. 2005. Fractional vegetation cover estimation from Proba/CHRIS data: methods, analysis of angular effects and application to the land surface emissivity retrieval. En: Proceedings of the 3rd CHRIS/ProbaWorkshop. 21-23 de marzo, ESA-ESRIN, Frascati, Italia.
  • Pearson, R. L., Miller L. D. 1972. Remote mapping of standing crop biomass for estimation of the productivity of the short-grass prairie, Pawnee National Grasslands, Colorado. En: Proceedings of the Eighth International Symposium on Remote Sensing of Environment, ERIM International. 13571381.
  • Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., Sorooshian, S. 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48, 119-126. http://dx.doi.org/10.1016/00344257(94)90134-1
  • Richardson, A. J., Wiegand, C. L., 1977. Distinguishing vegetation from soil background information. ISPRS J. Photogramm. 4, 1541-1552.
  • Rondeaux, G., Steven, M., Baret, F. 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95-107. http://dx.doi. org/10.1016/0034-4257(95)00186-7
  • Rouse, J.W. Haas, R.H. Schell, J. A. 1974. Monitoring the vernal advancement of retrogradation of natural vegetation, NASA/GSFC, Type III. Final Report. Greenbelt, MD, USA. 1-371.
  • Sibanda, M., Mutanga, O., Rouget, M. 2015. Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments. ISPRS Journal of Photogrammetry and Remote Sensing, 110, 55-65. http://dx.doi.org/10.1016/j.isprsjprs.2015.10.005
  • Verger, A., Camacho, F., Meliá, J. 2004. Influencia de la geometría de adquisición en el NDVI. Revista de Teledetección, 21, 95-99.
  • Verger, A. 2008. Anàlisi comparativa d’algorismes operacionals d’estimació de paràmetres biofísics de la coberta vegetal amb teledetecció. PhD tesis, Universidad de València. 307 pp.
  • Watson, D. J. 1947. Comparative physiological studies on growth of field crops: I. Variation in net assimilation rate and leaf area between species and varieties, and within and between yeas. Annals of Botany, 1, 41-76.
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