Skip to main content
SpringerLink
Log in

Mapping radiation interception in row-structured orchards using 3D simulation and high-resolution airborne imagery acquired from a UAV

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

This study was conducted to model the fraction of intercepted photosynthetically active radiation (fIPAR) in heterogeneous row-structured orchards, and to develop methodologies for accurate mapping of the instantaneous fIPAR at field scale using remote sensing imagery. The generation of high-resolution maps delineating the spatial variation of the radiation interception is critical for precision agriculture purposes such as adjusting management actions and harvesting in homogeneous within-field areas. Scaling-up and model inversion methods were investigated to estimate fIPAR using the 3D radiative transfer model, Forest Light Interaction Model (FLIGHT). The model was tested against airborne and field measurements of canopy reflectance and fIPAR acquired on two commercial peach and citrus orchards, where study plots showing a gradient in the canopy structure were selected. High-resolution airborne multi-spectral imagery was acquired at 10 nm bandwidth and 150 mm spatial resolution using a miniaturized multi-spectral camera on board an unmanned aerial vehicle (UAV). In addition, simulations of the land surface bidirectional reflectance were conducted to understand the relationships between canopy architecture and fIPAR. Input parameters used for the canopy model, such as the leaf and soil optical properties, canopy architecture, and sun geometry were studied in order to assess the effect of these inputs on canopy reflectance, vegetation indices and fIPAR. The 3D canopy model approach used to simulate the discontinuous row-tree canopies yielded spectral RMSE values below 0.03 (visible region) and below 0.05 (near-infrared) when compared against airborne canopy reflectance imagery acquired over the sites under study. The FLIGHT model assessment conducted for fIPAR estimation against field measurements yielded RMSE values below 0.08. The simulations conducted suggested the usefulness of these modeling methods in heterogeneous row-structured orchards, and the high sensitivity of the normalized difference vegetation index and fIPAR to background, row orientation, percentage cover and sun geometry. Mapping fIPAR from high-resolution airborne imagery through scaling-up and model inversion methods conducted with the 3D model yielded RMSE error values below 0.09 for the scaling-up approach, and below 0.10 for the model inversion conducted with a look-up table. The generation of intercepted radiation maps in row-structured tree orchards is demonstrated to be feasible using a miniaturized multi-spectral camera on board UAV platforms for precision agriculture purposes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  • Alton, P. B., North, P. R., & Los, S. O. (2007). The impact of diffuse sunlight on canopy light-use efficiency, gross photosynthetic product and net ecosystem exchange in three forest biomes. Global Change Biology, 13, 1–12.

    Article  Google Scholar 

  • Asrar, G., Myneni, R. B., & Choudbhury, B. J. (1992). Spatial heterogeneity in vegetation canopies and remote sensing of absorbed photosynthetically active radiation: A modelling study. Remote Sensing of Environment, 41, 85–103.

    Article  Google Scholar 

  • 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 Sensing of Environment, 78, 264–273.

    Article  Google Scholar 

  • 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 Transactions on Geoscience and Remote Sensing, 47(3), 722–738.

    Article  Google Scholar 

  • Bouguet, J. (2001). Camera calibration toolbox for Matlab. http://www.vision.caltech.edu/bouguetj/calib_doc. Accessed January 2012.

  • Choudhury, B. J. (1987). Relationships between vegetation indices, radiation absorption, and net photosynthesis evaluated by a sensitivity analysis. Remote Sensing of Environment, 22, 209–233.

    Article  Google Scholar 

  • Daughtry, C. S. T., Gallo, K. P., & Bauer, M. E. (1983). Spectral estimates of solar radiation by corn canopies. Agronomy Journal, 75, 527–531.

    Article  Google Scholar 

  • Daughtry, C. S. T., Gallo, K. P., Goward, S. N., Price, S. D., & Kustas, W. P. (1992). Spectral estimates of absorbed radiation and phytomas production in corn and soybean canopies. Remote Sensing of Environment, 39, 141–152.

    Article  Google Scholar 

  • De Wit, C. T. (1959). Potential photosynthesis of crop surfaces, Netherlands. The Journal of Agricultural Science, 7, 141–149.

    Google Scholar 

  • Disney, M. I., Lewis, P., & North, P. R. J. (2000). Monte Carlo ray tracing in optical canopy reflectance modelling. Remote Sensing Reviews, 18(2–4), 163–196.

    Article  Google Scholar 

  • Gallagher, J. N., & Biscoe, P. V. (1978). Radiation absorption, growth and yield of cereals. The Journal of Agricultural Science, 91, 47–60.

    Article  Google Scholar 

  • Goward, S. N., & Huemmrich, K. F. (1992). Vegetation canopy PAR Absorptance and the normalized difference vegetation index: An assessment using the SAIL model. Remote Sensing of Environment, 39, 119–140.

    Article  Google Scholar 

  • Gueymard, C.A. (2005). SMARTS Code, Version 2.9.5 user’s manual solar consulting services. Online PDF document from http://www.nrel.gov/rredc/smarts/. Accessed January 2012.

  • 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. Journal of Geophysical Results, 97, 19061–19089.

    Article  Google Scholar 

  • Huemmrich, K. F. (2001). The GeoSail model: A simple addition to the SAIL model to describe discontinuous canopy reflectance. Remote Sensing of Environment, 75, 423–431.

    Article  Google Scholar 

  • Huemmrich, K. F., & Goward, S. N. (1997). Vegetation canopy PAR Absorptance and NDVI: An assessment for ten species with SAIL model. Remote Sensing of Environment, 61, 254–269.

    Article  Google Scholar 

  • Huete, A. R. (1989). Soil influences in remotely sensed vegetation canopy-spectra. In G. Asrar (Ed.), Theory-applications of optical remote sensing (pp. 107–141). New York: Willey.

    Google Scholar 

  • Huete, A. R., Jackson, R. D., & Post, D. F. (1985). Spectral response of a plant canopy with different soil backgrounds. Remote Sensing of Environment, 17, 37–53.

    Article  Google Scholar 

  • Hunt, R. E. (1994). Relationship between woody biomass & PAR conversion efficiency for estimating net primary production from NDVI. International Journal Remote Sensing, 15, 1725–1730.

    Article  Google Scholar 

  • Jackson, J. E. (1980). Light interception and utilization by orchard systems. Horticultural Reviews, 2, 208–267.

    Google Scholar 

  • Kempeneers, P., Zarco-Tejada, P. J., North, P. R. J., De Backer, S., Delalieux, S., Sepulcre-Cantó, G., et al. (2008). Model inversion for chlorophyll estimation in open canopies from hyperspectral imagery. International Journal of Remote Sensing, 29(17–18), 5093–5111.

    Article  Google Scholar 

  • Koetz, B., Baret, F., Poilvé, H., & Hill, J. (2005). Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics. Remote Sensing of Environment, 95(1), 115–124.

    Article  Google Scholar 

  • Lang, A. R. G. (1987). Simplified estimate of leaf area index from transmittance of the sun’s beam. Agricultural and Forest Meteorology, 41, 179–186.

    Article  Google Scholar 

  • Lang, A. R. G., Xiang, Y., & Norman, J. M. (1985). Crop structure and the penetration of direct sunlight. Agricultural and Forest Meteorology, 35, 8–101.

    Article  Google Scholar 

  • Lemeur, R. (1973). A method for simulating the direct solar radiation regime in sunflower, Jerusalen artichoke, corn and soybean canopies using actual stand structure data. Agricultural and Forest Meteorology, 12, 229–247.

    Google Scholar 

  • Li-Cor Inc. (1984). Li-Cor model 1800-12 integrating sphere instruction manual (Revision 1984). Li-Cor Incorporated, Lincoln, NE, Publication No. 8305-0034.

  • Loomis, R. S., & Williams, W. A. (1963). Maximum crop productivity: An estimate. Crop Science, 3, 67–72.

    Article  Google Scholar 

  • Mariscal, M. J., Orgaz, F., & Villalobos, F. J. (2000). Modelling and measurement of radiation interception by olive canopies. Agricultural and Forest Meteorology, 100, 183–197.

    Article  Google Scholar 

  • Monteith, J. L. (1972). Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology, 9, 747–766.

    Article  Google Scholar 

  • Moran, M. S., Inoue, Y., & Barmes, E. M. (1997). Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment, 61(3), 319–346.

    Article  Google Scholar 

  • Moriondo, M., Maselli, F., & Bindi, M. (2007). A simple model of regional wheat yield based on NDVI data. European Journal of Agronomy, 26, 266–274.

    Article  Google Scholar 

  • Myneni, R. B., & Williams, D. L. (1994). On the relationships between FAPAR and NDVI. Remote Sensing of Environment, 49, 200–211.

    Article  Google Scholar 

  • North, P. R. J. (1996). Three-dimensional forest light interaction model using a Monte Carlo method. IEEE Transactions on Geoscience and Remote Sensing, 34(4), 946–956.

    Article  Google Scholar 

  • North, P. R. J. (2002). Estimation of f APAR , LAI, and vegetation fractional cover from ATSR-2 imagery. Remote Sensing of Environment, 80, 114–121.

    Article  Google Scholar 

  • Pinty, B., Widlowski, J. L., Gobron, N., & Verstraete, M. M. (2002). Uniqueness of multi-angular information—Part 1: A surface heterogeneity indicator from MISR. IEEE Transactions on Geosciences and Remote Sensing, 40, 1560–1573.

    Article  Google Scholar 

  • Prieto-Blanco, A., North, P. R. J., Barnsley, M. J., & Fox, N. (2009). Satellite-driven modelling of net primary productivity (NPP): theoretical analysis. Remote Sensing of Environment, 113, 137–147.

    Article  Google Scholar 

  • Robinson, T., & Lakso, A. (1991). Bases of yield and production efficiency in apple orchard system. Journal of the American Society for Horticultural Science, 116(2), 188–194.

    Google Scholar 

  • Ross, J. (1981). The radiation regime and architecture of plant stands. The Hague: W. Junk.

    Google Scholar 

  • 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 Sensing of Environment, 113, 730–744.

    Article  Google Scholar 

  • Suárez, L., Zarco-Tejada, P. J., González-Dugo, V., Berni, J. A. J., Sagardoy, R., Morales, F., et al. (2010). Detecting water stress effects on fruit quality in orchards with time-series PRI airborne imagery. Remote Sensing of Environment, 114, 286–298.

    Article  Google Scholar 

  • 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. Agricultural and Forest Meteorology, 73, 29–42.

    Article  Google Scholar 

  • Weiss, M., Baret, F., Myneni, R. B., Pragnère, A., & Knyazikhin, Y. (2000). Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data. Agronomie, 20(1), 3–22.

    Article  Google Scholar 

  • Widlowski, J.-L. (2010). On the bias of instantaneous FAPAR estimates in open-canopy forests. Agricultural and Forest Meteorology, 150, 1501–1522.

    Article  Google Scholar 

  • Widlowski, J.-L., Pinty, B., Disney, M., Gastellu-Etchegorry, J.-P., Lavergne, T., Lewis, P. E., et al. (2008). The RAMI on-line model checker (ROMC): A web-based benchmarking facility for canopy reflectance models. Remote Sensing of Environment, 112(3), 1144–1150.

    Article  Google Scholar 

  • Widlowski, J.-L., Taberner, M., Pinty, B., Bruniquel-Pinel, V., Disney, M., Fernandez, R., et al. (2007). Third radiation transfer model intercomparison (RAMI) exercise: Documenting progress in canopy reflectance models. Journal of Geophysical Research, 112, D09111.

    Article  Google Scholar 

  • Zarco-Tejada, P. J., Berjón, A., López-Lozano, R., Miller, J. R., Martín, P., Cachorro, V., et al. (2005). Assessing vineyard condition with hyperspectral indices: Leaf & canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sensing of Environment, 99, 271–287.

    Article  Google Scholar 

  • Zarco-Tejada, P. J., Berni, J. A. J., Suárez, L., & Fereres, E. (2008). A new era in remote sensing of crops with unmanned robots. SPIE Newsroom,. doi:10.1117/2.1200812.1438.

    Google Scholar 

  • Zarco-Tejada, P. J., González Dugo, V., & Berni, J. A. J. (2012). Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sensing of Environment, 117, 322–337.

    Article  Google Scholar 

  • 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 Sensing of Environment, 113, 880–888.

    Article  Google Scholar 

Download references

Acknowledgments

Financial support from the Spanish Ministry of Science and Innovation (MCI) for the projects AGL2009-13105, CONSOLIDER CSD2006-67, and AGL2003-01468 is gratefully acknowledged, as well as the Junta de Andalucía-Excelencia AGR-595 and FEDER. M.L. Guillén-Climent was supported by a grant JAE of CSIC, co-funded by the European Social Fund. Technical support from UAV Navigation, Tetracam Inc. and UAV Services and Systems for the airborne requirements are acknowledged. A. Vera, D. Notario, R. Romero, and A. Hornero are acknowledged for technical support in field and airborne campaigns.

Author information

Authors and Affiliations

  1. Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Alameda del Obispo, s/n, 14004, Córdoba, Spain

    M. L. Guillen-Climent, Pablo J. Zarco-Tejada, J. A. J. Berni & F. J. Villalobos

  2. Environmental Modelling and Earth Observation Group, Department of Geography, University of Wales, Swansea, SA2 8PP, UK

    P. R. J. North

  3. Departamento de Agronomía, Universidad de Córdoba, Campus Universitario de Rabanales, 14014, Córdoba, Spain

    F. J. Villalobos

Authors
  1. M. L. Guillen-Climent
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pablo J. Zarco-Tejada
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. A. J. Berni
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. P. R. J. North
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. F. J. Villalobos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pablo J. Zarco-Tejada.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Guillen-Climent, M.L., Zarco-Tejada, P.J., Berni, J.A.J. et al. Mapping radiation interception in row-structured orchards using 3D simulation and high-resolution airborne imagery acquired from a UAV. Precision Agric 13, 473–500 (2012). https://doi.org/10.1007/s11119-012-9263-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-012-9263-8

Keywords

Search

Navigation