Cookies Policy

This site uses cookies. By continuing to browse the site you are agreeing to our use of cookies.

I accept this policy

Find out more here

Estimating radiation interception in an olive orchard using physical models and multispectral airborne imagery

No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.

Brill’s MyBook program is exclusively available on BrillOnline Books and Journals. Students and scholars affiliated with an institution that has purchased a Brill E-Book on the BrillOnline platform automatically have access to the MyBook option for the title(s) acquired by the Library. Brill MyBook is a print-on-demand paperback copy which is sold at a favorably uniform low price.

Access this article

+ Tax (if applicable)
Add to Favorites
You must be logged in to use this functionality

image of Israel Journal of Plant Sciences

This study was conducted to estimate the fraction of Intercepted Photosinthetically Active Radiation (fIPAR) in an olive orchard. The method proposed to estimate fIPAR in olive canopies consisted of a coupled radiative transfer model that linked the 3D Forest Light Interaction Model (FLIGHT) and the Orchard Radiation Interception Model (ORIM). This method was used to assess the estimation of instantaneous fIPAR as a function of planting grids, percentage cover, and soil effects. The linked model was tested against field measurements of fIPAR acquired for a commercial olive orchard, where study plots showing a gradient in the canopy structure and percentage cover were selected. High-resolution airborne multispectral imagery was acquired at 10 nm bandwidth and 15-cm spatial resolution, and the reflectance used to calculate vegetation indices from each study site. In addition, simulations of the land surface bidirectional reflectance were conducted to understand the relationships between canopy architecture and fIPAR on typical olive orchard planting patterns. Input parameters used for the canopy model, such as the leaf and soil optical properties, the architecture of the canopy, and sun geometry, were studied in order to assess the effect of these inputs on the Normalized Difference Vegetation Index (NDVI) and fIPAR relationships. FLIGHT and ORIM models were independently assessed for fIPAR estimation using structural and ceptometer field data collected from each study site, yielding RMSE values of 0.1 for the FLIGHT model, while the specific olive simulation model by ORIM yielded lower errors (RMSE = 0.05). The reflectance simulations conducted as a function of the orchard architecture confirmed the usefulness of the modeling methods for this heterogeneous olive crop, and the high sensitivity of the NDVI and fIPAR to background, percentage cover, and sun geometry on these heterogeneous orchard canopies. The fIPAR estimations obtained from the airborne imagery through predictive relationships yielded RMSE error values of 0.11 when using FLIGHT to simulate both the canopy reflectance and the fIPAR of the study sites. The coupled FLIGHT+ORIM model yielded better results, obtaining RMSE = 0.05 when using airborne remote sensing imagery to estimate fIPAR.

Affiliations: 1: Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Alameda del Obispo ; 2: Departamento de Agronomía, Universidad de Córdoba (UCO)


Full text loading...


Data & Media loading...

1. Annandale, J.G., Jovanovic, N.Z., Campbell, G.S., Du Sautoy, N., Lobit, P. 2004. Two-dimensional solar radiation interception model for hedgerow fruit trees. Agr. Forest. Meteorol. 121: 207-225.
2. Asrar, G., Fuchs, M., Kanemasu, E.T., Hatfield, J.H. 1984. Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agron. J. 76: 300-306.
3. Barton, C.V.M., North, P.R.J. 2001. Remote sensing of canopy light use efficiency using the photochemical reflectance index. Model and sensitivity analysis. Remote Sens. Environ., 78: 264-273.
4. Beede, R.H., Goldhamer, D.A. 1994. Olive irrigation management In: Ferguson, L., Sibbett, G.S., Martin, G.C. eds. Olive production manual. Univ. of California Pub. 3353, pp. 61-68.
5. Ben-Gal, A., Yermiyahu, U., Zipori, I., Presnov, E., Hanoch, E., Dag, A. 2011. The influence of bearing cycles on olive oil production response to irrigation. Irrig. Sci. 29: 253-263.
6. Berni, J.A.J., Zarco-Tejada, P.J., Suarez, L., Fereres, E. 2009. Thermal and narrow-band multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans. Geosci. Electron. 47: 722-738.
7. Bouguet, J. 2001. Camera calibration toolbox for Matlab. (
8. Connor, D.J., Fereres, E. 2005. The physiology of adaptation and yield expression in olive. Horicultural Reviews 31: 155-229.
9. Daughtry, C.S.T., Gallo, K.P., Bauer, M.E. 1983. Spectral estimates of solar radiation by corn canopies. Agron. J. 75: 527-531.
10. De Castro, F., Fetcher, N. 1998. Three dimensional model of the interception of light by a canopy. Agr. Forest. Meteorol. 90: 215-233.
11. Disney, M.I., Lewis, P., North, P. R. J. 2000. Monte Carlo ray tracing in optical canopy reflectance modelling. Remote Sens. Rev. 18: 163-196.
12. Fernades-Silva, A.A., Ferreira, T.C., Correia, C.M., Malheiro, A.C., Villalobos, F.J. 2010. Influence of different irrigation regimes on crop yield and water use efficiency of olive. Plant Soil 333: 35-47.
13. Friday, J.B., Fowness, J.H. 2001. A simulation model for hedgerow light interception and growth. Agr. Forest. Meteorol. 108: 29-43.
14. Gómez, J.A., Zarco-Tejada, P.J., García-Morillo, J., Gama, J., Soriano, M.A. 2011. Determining biophysical parameters for olive trees using CASI airborne and Quickbird satellite imagery. Agron. J. 103: 644-654.
15. Gueymard, C.A. 1995. SMARTS, A Simple Model of the Atmospheric Radiative Transfer of Sunshine: algorithms and performance assessment. Technical Report No. FSEC-PF-270-95. Florida Solar Energy Center, Cocoa, FL.
16. Gueymard, C.A. 2005. Interdisciplinary applications of a versatile spectral solar irradiance model: a review. Energy 30: 1551-1576.
17. Guillen-Climent, M.L., Zarco-Tejada, P.J., Berni, J.A.J., North, P.R.J., Villalobos, F.J. 2012. rco-Tejada, P.J., Berni, J.A.J., North, P.R.J. and Villalobos, F.J. (2012), Mapping radiation interception in row-structured orchards using 3D simulation and high resolution airborne imagery acquired from a UAV. Precision Agriculture 13: 473-500.
18. Hall, F.G., Huemmrich, K.F., Goetz, S.J., Sellers, P.J., Nickerson, J.E. 1992. Satellite remote sensing of surface energy balance: success, failures, and unresolved issues in FIFE. J. Geophys. Res. 97: 19,061-19,089.
19. Huemmrich, K.F. 2001. The GeoSail model: a simple addition to the SAIL model to describe discontinuous canopy reflectance. Remote Sens. Environ. 75, 423-431.
20. Huemmrich, K.F. Goward, S.N., 1997. Vegetation canopy PAR absorptance and NDVI: an assessment for ten species with SAIL model. Remote Sens. Environ. 61: 254-269.
21. Iniesta F, Testi L, Orgaz F, Villalobos FJ. 2009. The effects of regulated and continuous eficit irrigation on the water use, growth and yield of olive trees. Eur. J. Agron. 30: 258-265.
22. Kempeneers, P., Zarco-Tejada, P. J., North, P. R. J., De Backer, S., Delalieux, S., Sepulcre-Cantó, G., Morales, F., Van Aardt, J. A. N., Sagardoy, R., Coppin, P., Scheunders, P. 2008. Model inversion for chlorophyll estimation in open canopies from hyperspectral imagery. Int. J. Remote Sens. 29: 5093-5111.
23. Li-Cor Inc. 1983. 1800-12 Integrating Sphere instruction manual, Publication no. 8305-0034, Lincoln, NE USA.
24. Mariscal, M.J., Orgaz, F., Villalobos, F.J. 2000. Modelling and measurement of radiation interception by olive canopies. Agr. Forest. Meteorol. 100: 183-197.
25. Monteith J.L. 1977. Climate and the efficiency of crop production in Britain. Philos. Trans. R. Soc. London Ser. B 281: 277-294.
26. Moorthy, I., Miller, J.R., Berni, J.A.J., Zarco-Tejada, P.J., Hu, B., Chen, J. 2011. Field characterization of olive (Olea europaea L.) tree crown architecture using terrestrial laser scanning data. Agr. Forest. Meteorol. 151: 204-214.
27. Moriondo, M., Maselli, F., Bindi, M. 2007. A simple model of regional wheat yield based on NDVI data. Eur. J. Agron. 26: 266-274.
28. Myneni, R.B., Williams, D.L. 1994. On the relationships between FAPAR and NDVI. Remote Sens. Environ. 49: 200-211.
29. North. P.R.J. 1996. Three-dimensional forest light interaction model using a Monte Carlo method. IEEE Trans. Geosci. Electron. 34: 946-956.
30. North, P.R.J. 2002. Estimation of fAPAR, LAI, and vegetation fractional cover from ATSR-2 imagery. Remote Sens. Environ. 80: 114-121.
31. Olofsson, P., Eklundh, L. 2007. Estimation of abserbed PAR across Scandinavia from satellite measurments. Part II: Modeling and evaluating the fractional absorption. Remote Sens. Environ. 110: 240-251.
32. Orgaz, F., Villalobos, F.J., Testi, L., Fereres, E. 2007. A model of daily mean canopy conductance for calculating transpiration of olive canopies. Func. Plant Biol. 34: 178-188.
33. Oyarzun, R.A., Stöckle, C.O., Whiting, M.D. 2007. A simple approach to modeling radiation interception by fruit-tree orchards. Agr. Forest. Meteorol., 142: 12-24.
34. Pastor, M., García-Vila, M., Soriano, M.A., Vega, V., Fereres, E. 2007. Productivity of olive orchards in response to tree density. J. Hortic. Sci. Biotechnol. 82: 555-562.
35. Prieto-Blanco, A., North, P.R.J., Barnsley, M.J., Fox, N. 2009. Satellite-driven modelling of net primary productivity (NPP): theoretical analysis. Remote Sens. Environ. 113: 137-147.
36. Roujean, J.L., Breon, F.M. 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurement. Remote Sens. Environ. 51: 375-384.
37. Stuckens, J., Somers, B., Delalieux, S., Verstraeten, W.W., Coppin, P. 2009. The impact of common assumptions on canopy radiativa transfer simulations: a case study in Citrus orchards. J. Quant. Spectrosc. Radiat. Transfer 110: 1-21.
38. Suárez, L., Zarco-Tejada, P.J., Berni, J.A.J., González-Dugo, V., Fereres, E. 2009. Modelling PRI for water stress detection using radiative transfer models. Remote Sens. Environ. 113: 730-744.
39. Suárez, L., Zarco-Tejada, P.J., González-Dugo, V., Berni, J.A.J., Sagardoy, R., Morales, F., Fereres, E. 2010. Detecting water stress effects on fruit quality in orchards with time-serie PRI airborne imagery. Remote Sens. Environ. 114: 286-298.
40. Testi, L., Villalobos, F.J., Orgaz, F., Fereres, E. 2006. Water requirements of olive orchards. I. Simulation of daily -evapo-transpiration for scenario analysis. Irrig. Sci. 24: 69-76.
41. Villalobos, F.J., Orgaz, F., Mateos, L. 1995. Non-destructive measurement of leaf area in olive (Olea europaea L.) trees using a gap inversion method. Agr. Forest. Meteorol. 73: 29-42.
42. Villalobos, F.J., Testi, L., Hidalgo, J., Pastor, M. and Orgaz, F. 2006. Modelling potential growth and yield of olive (Olea europaea L.) canopies. Eur. J. Agron. 24: 296-303.
43. Vossen, P. 2007. Olive oil: history, production and characteristics of the world's classic oils. HortScience 42: 1093-1110
44. Wiegand, C.L., Richardson, A.J., Escobar, D.E., Gerbermann, A.H. 1991. Vegetation indices in crop assessments. Remote Sens. Environ. 35: 105-119.
45. Widlowski, J-L., Taberner, M., Pinty, B., Bruniquel-Pinel, V., Disney, M., Fernandez, R., Gastellu-Etchegorry, J-P., Gobron, N., Kuusk, A., Lavergne, T., Leblanc, S., Lewis, P.E., Martin, E., Mottus, M., North, P.R.J., Quin, W., Robustelli, M., Rochdi, N., Ruiloba, R., Soler, C., Thompson, R., Verhoef, W., Verstraete, M.M. and Xie, D. 2007. Third Radiation transfer Model Intercomparison (RAMI) exercise: documenting progress in canopy reflectance models. J. Geophys. Res. 112: D09111.
46. Widlowski, J-L., Pinty, B., Disney, M., Gastellu-Etchegorry, J-P., Lavergne, T., Lewis, P.E., North, P. R. J., Robustelli, M., Thompson, R. and Verstraete, M.M. 2008. The RAMI on-line model checker (ROMC): a web-based benchmarking facility for canopy reflectance models. Remote Sens. Environ. 112: 1144-1150.
47. Zhang, Q., Middleton, E.M., Margolis, H.A., Drolet, G.G., Barr, A.A., Black, T.A. 2009. Can a satellite-derived estimate of the fraction of PAR absorbed by chlorophyll (FAPARchl) improve predictions of light-use efficiency and ecosystem photosynthesis for a boreal aspen forest? Remote Sens. Environ. 113: 880-888.
48. Zarco-Tejada, P.J., Berjón, A., López-Lozano, R., Miller, J.R., Martín, P., Cachorro, V., González, M.R., Frutos, A. 2005. Assessing vineyard condition with hyperspectral indices: leaf & canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sens. Environ. 99: 271-287.

Article metrics loading...



Can't access your account?
  • Tools

  • Add to Favorites
  • Printable version
  • Email this page
  • Subscribe to ToC alert
  • Get permissions
  • Recommend to your library

    You must fill out fields marked with: *

    Librarian details
    Your details
    Why are you recommending this title?
    Select reason:
    Israel Journal of Plant Sciences — Recommend this title to your library
  • Export citations
  • Key

  • Full access
  • Open Access
  • Partial/No accessInformation