SpringerLink
Log in

Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part II: application to maize and onion crops of a semi-arid region in Spain

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

Leaf area index (LAI) is involved in biological, environmental and physiological processes, which are related to photosynthesis, transpiration, interception of radiation and energy balance. Thus, most crop models use LAI as a key feature to characterize the growth and development of crops. However, direct measures of LAI are destructive and tedious so that samplings can seldom be repeated in time and in space. Green canopy cover (GCC) is directly involved in crop growth and development. GCC estimation can benefit from aerial observation, as it can be measured by using image analysis or estimated by obtaining different vegetation indices. The main purpose of the second part of this paper was to study the relationships between GCC and LAI by using aerial images from UAVs in order to characterize crop growth. Also, the relationships between GCC and a vegetation index based on the visible spectrum was calibrated and validated. Relationships between LAI and GCC, growing degree days (GDD) and GCC and GDD and LAI were calibrated and validated for maize and onion crops with proper fitting. Visible atmospherically resistant index also appears to be a sensitive indicator to different growing stages and could generally be applied to any field crop. To apply this methodology, GCC and LAI relationships must be calibrated for many other crops in different irrigable areas. In addition, the cost of the UAV is expected to decrease while autonomy increases through improved battery life and reductions in the weight of on-board sensors.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  • Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration. Guidelines for computing crop water requirements., FAO Irrigation and drainage paper No 56 Rome: Food and Agriculture Organization (FAO).

    Google Scholar 

  • Asrar, G., Myneni, R. B., & Kanemasu, E. T. (1989). Theory and applications of optical remote sensing. In G. Asrar (Ed.), Estimation of plant canopy attributes from spectral reflectance measurements (pp. 252–296). New York: Wiley.

    Google Scholar 

  • Baret, F., & Guyot, G. (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing Environment, 35, 161–173.

    Article  Google Scholar 

  • Barker, D. J., Ferraro, F. P., La Guardia Nave, R., Sulc, R. M., Lopes, F., & Albrecht, K. A. (2010). Analysis of herbage mass and herbage accumulation rate using Gompertz equations. Agronomy Journal, 102, 849–857.

    Article  Google Scholar 

  • Bell, E. T. (1934). Exponential polynomials. The Annals of Mathematics, 35(2), 258–277.

    Article  Google Scholar 

  • Botella, O., De Juan, J. A., de Santa, Martín, & Olalla, F. (1997). Growth, development, and yield of five sunflower hybrids. European Journal of Agronomy, 6, 47–59.

    Article  Google Scholar 

  • Calera, A., Martínez, C., & Meliá, J. (2001). A procedure for obtaining green plant canopy cover. Its relation with NDVI in a case study for barley. International Journal of Remote Sensing, 22(17), 3357–3362.

    Article  Google Scholar 

  • Chen, X., Vierling, L., Rowell, E., & DeFelice, T. (2004). Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM + vegetation cover estimates in a ponderosa pine forest. Remote Sensing of Environment, 91, 14–26.

    Article  Google Scholar 

  • Córcoles, J. I., Ortega, J. F., Hernández, D., & Moreno, M. A. (2013). Estimation of leaf area index in onion (Allium cepa L.) using an unmanned aerial vehicle. Biosystems Engineering, 115, 31–42.

    Article  Google Scholar 

  • de Martín Santa Olalla, F., Juan, J. A., & Fabeiro, C. (1994). Growth and yield analysis of soybean (Glycine Max (L.) Merr.) under different irrigation schedules in Castilla-La Mancha, Spain. European Journal of Agronomy, 3(3), 187–196.

    Article  Google Scholar 

  • de Medeiros, G. A., Arruda, F. B., Sakai, E., & Fujiwara, M. (2001). The influence of crop canopy on evapotranspiration and crop coefficient of beans (Phaseolus vulgaris L.). Agricultural Water Management, 49, 211–224.

    Article  Google Scholar 

  • de Medeiros, G. A., Arruda, F. B., Sakai, E., Fujiwara, M., & Boni, N. R. (2000). Vegetative growth and bean crop coefficient as related to accumulated growing-degree-days. Pesquisa Agropecuária Brasileira, 35(9), 1733–1742.

    Article  Google Scholar 

  • DeJonge, K. C., Andales, A. A., Ascough, J. C., & Hansen, N. C. (2011). Modeling of full and limited irrigation scenarios for corn in a semiarid environment. Transactions of ASABE, 54(2), 481–492.

    Article  Google Scholar 

  • Dutta, A., Dutta, S. K., Jena, S., Nath, R., Bandyopadhyay, P., & Chakraborty, P. K. (2011). Effect of growing degree days on biological growth indices of wheat and mustard. Journal of Crop and Weed, 7(1), 70–76.

    Google Scholar 

  • Gilabert, M. A., González-Piqueras, J., & Martínez, B. (2010). Remote sensing optical observations of vegetation properties. In F. Maselli, M. Meneti, & P. A. Brivio (Eds.), Theory and applications of vegetation indices (pp. 1–43). Scarborough: Research Signpost.

    Google Scholar 

  • Gitelson, A. A., Kaufman, Y. J., Stark, R., & Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80(1), 76–87.

    Article  Google Scholar 

  • Gitelson, A. A., Viña, A., Arkebauer, T. J., Rundquist, D. C., Keydan, G., & Leavitt, B. (2003). Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters, 30(5), 1248–1251.

    Article  Google Scholar 

  • Gupta, R. K., Prasad, T. S., & Vijayan, D. (2000). Relationship between LAI and NDVI for IRS LISS and LANDSAT TM bands. Advances in Space Research, 26(7), 1047–1050.

    Article  Google Scholar 

  • Herbst, M., Roberts, J. M., Rosier, P. T. W., & Gowing, D. J. (2006). Measuring and modelling the rainfall interception loss by hedgerows in southern England. Agricultural and Forest Meteorology, 141, 244–256.

    Article  Google Scholar 

  • Herwitz, S. R., Johnson, L. F., Dunagan, S. E., Higgins, R. G., Sullivan, D. V., Zheng, J., et al. (2004). Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support. Computers and Electronics in Agriculture, 44, 49–61.

    Article  Google Scholar 

  • Hsiao, T. C., Heng, L., Steduto, P., Rojas-Lara, B., Raes, D., & Fereres, E. (2009). AquaCrop—the FAO crop model to simulate yield response to water: III. Parameterization and testing for maize. Agronomy Journal, 101(3), 448–459.

    Article  Google Scholar 

  • Jiménez, M. (2008). La distribución de agua bajo riego por aspersión estacionario y su influencia sobre el rendimiento del cultivo de cebolla (Allium cepa L.) (Water distribution under sprinkler irrigation and its influence on onion yield (Allium cepa L.)) (PhD Dissertation. Technical High School of Agricultural Engineering. University of Castilla-La Mancha, Albacete, 2008). (In Spanish).

  • Jovanovic, N. Z., & Annandale, J. G. (1999). An FAO typo crop modification to SWB for inclusion of crops with limited data: Examples for vegetables crops. Water SA, 25(2), 181–190.

    Google Scholar 

  • Juskiw, P. E., Jame, Y. W., & Kryzanowski, L. (2001). Phenological development of spring barley in a short-season growing area. Agronomy Journal, 93(2), 370–379.

    Article  Google Scholar 

  • Kato, T., & Kamichika, M. (2006). Determination of a crop coefficient for evapotranspiration in a sparse sorghum field. Irrigation and Drainage, 55(2), 165–175.

    Article  Google Scholar 

  • Kiniry, J. R. (1991). Modeling plant and soil systems. In J. Hanks & J. T. Ritchie (Eds.), Maize phasic development (pp. 55–70). Madison: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America.

    Google Scholar 

  • Kucharik, C. J., Norman, J. M., & Gower, S. T. (1998). Measurements of branch area and adjusting leaf area index to indirect measurements. Agricultural and Forest Meteorology, 91, 69–88.

    Article  Google Scholar 

  • Lancaster, J. E., Triggs, C. M., De Ruiter, J. M., & Gandar, P. W. (1996). Bulbing in onions: photoperiod and temperature requirements and prediction of bulb size and maturity. Annals of Botany, 78(4), 423–430.

    Article  Google Scholar 

  • Lisazo, J. I., Batchelor, W. D., & Westgate, M. E. (2003). A leaf area model to simulate cultivar-specific expansion and senescence of maize leaves. Field Crop Research, 80, 1–17.

    Article  Google Scholar 

  • Lofton, J., Tubana, B. S., Kanke, Y., Teboh, J., Viator, H., & Dalen, M. (2012). Estimating sugarcane yield potential using an in-season determination of normalized difference vegetative index. Sensors, 12, 7529–7547.

    Article  PubMed  PubMed Central  Google Scholar 

  • López, H. (2004). Modelización de la respuesta agronómica del cultivo del maíz (Zea mays L.) a la dosis de nitrógeno (Agronomic modelling of maize (Zea mays L.) response to nitrogen fertilization).(PhD Dissertation. Technical High School of Agricultural Engineering. University of Castilla-La Mancha, Albacete, 2004). (In Spanish).

  • López-Urrea, R., de Santa, Martín, Olalla, F., Montoro, A., & López-Fuster, P. (2009). Single and dual crop coefficients and water requirements for onion (Allium cepa L.) under semiarid conditions. Agricultural Water Management, 96, 1031–1036.

    Article  Google Scholar 

  • López-Urrea, R., Montoro, A., Mañas, F., López-Fuster, P., & Fereres, E. (2012). Evapotranspiration and crop coefficients from lysimeter easurements of mature ‘Tempranillo’ wine grapes. Agricultural Water Management, 112, 13–20.

    Article  Google Scholar 

  • Maturano, M. (2002). Estudio del uso del agua y del nitrógeno dentro del marco de una agricultura sostenible en las regiones maiceras castellano-manchega y Argentina (Study of water use and nitrogen within the framework of sustainable agriculture in maize-growing areas of Castilla-La Mancha and Argentina) (Ph.D. Thesis, Technical High School of Agricultural Engineering, University of Castilla-La Mancha, Albacete, 2002). (In Spanish).

  • Monteith, J. L. (1996). The quest for balance in crop modelling. Agronomy Journal, 88, 695–697.

    Article  Google Scholar 

  • Nash, J., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I-A discussion of principles. Journal of Hydrology, 10(3), 282–290.

    Article  Google Scholar 

  • Nielsen, D. C., Miceli-García, J. J., & Lyon, D. J. (2012). Canopy cover and leaf area index relationships for wheat, triticale, and corn. Agronomy Journal, 104(6), 1569–1573.

    Article  Google Scholar 

  • Nuarsa, I. W., Nishio, F., & Hongo, C. (2011). Relationship between rice spectral and rice yield using Modis data. Journal of Agricultural Science, 3(2), 80–88.

    Article  Google Scholar 

  • Pereira, L. S., & Allen, R. G. (1999). CIGR Handbook of Agricultural Engineering, Vol. I: Land and Water Engineering. In H. N. van Lier (Ed.), Irrigation and Drainage (pp. 213–262). St. Joseph: ASAE.

    Google Scholar 

  • Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J.-M., Tucker, C. J., & Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution, 20, 503–510.

    Article  Google Scholar 

  • Ritchie J., Singh U., Godwin D., & Hunt L. (1989). CERES-Maize V.2.10: user’s guide for maize crop growth simulation model (Vol. 2). East Lansing: Michigan State University.

    Google Scholar 

  • Rundquist, D., Gitelson, A. A., Derry, D., Ramirez, J., Stark, R., & Keydan, G. P. (2001). Remote estimation of vegetation fraction in corn canopies. In G. Grenier & S. Blackmore (Eds.), Proceedings of the 3rd European conference on precision agriculture (pp. 301–306). Montpelier: Agro Montpelier.

    Google Scholar 

  • Srivastava, A. K., Chakravarty, N. V. K., Sharma, P. K., Goutom Bhagavati, Prasad, R. N., Gupta, V. K., et al. (2005). Relation of growing degree-days with plant growth and yield in mustard varieties grown under semi-arid environment. Journal of Agricultural Physics, 5(1), 23–28.

    Google Scholar 

  • Stark, R., Gitelson, A. A., Grits, U., Rundquist, D., & Kaufman, Y. (2000). New technique for remote estimation of vegetation fraction: principles, algorithms and validation. Aspects of Applied Biology, 60, 241–246.

    Google Scholar 

  • Steduto, P., Hsiao, T. C., Raes, D., & Fereres, E. (2009). AquaCrop-The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agronomy Journal, 101, 426–437.

    Article  Google Scholar 

  • Stöckle, C. O., Donatelli, M., & Nelson, R. (2003). Cropsyst, a crop** systems simulation model. European Agronomy Journal, 18, 289–307.

    Article  Google Scholar 

  • Tei, F., Scaife, A., & Aikman, D. P. (1996). Growth of lettuce, onion, and red beet. 1. Growth analysis, light interception, and radiation use efficiency. Annals of Botany, 78, 633–643.

    Article  Google Scholar 

  • Turner, D. P., Cohen, W. B., Kennedy, R. E., Fassnacht, K. S., & Briggs, J. M. (1999). Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sensing of Environment, 70, 82–98.

    Article  Google Scholar 

  • Viña, A., Gitelson, A. A., Rundquist, D. C., Keydan, G., Leavitt, B., & Schepers, J. (2004). Monitoring maize (L) phenology with remote sensing. Agronomy Journal, 96(4), 1139–1147.

    Article  Google Scholar 

  • Wang, Q., Adiku, S., Tenhunen, J., & André, G. (2004). On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sensing of Environment, 94, 244–255.

    Article  Google Scholar 

  • Watson, D. J. (1947). Comparative physiological studies in the growth of field crops. Variation in net assimilation rate and leaf area between species and varieties, and within and between years. Annals of Botany, 11, 41–76.

    CAS  Google Scholar 

  • Wright, J. L. (1982). New evapotranspiration crop coefficients. Journal of the Irrigation and Drainage Division-ASCE, 108(1), 57–74.

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Education Ministry of Spain for its financing with a University Teaching Scholarship (Formación de Profesorado Universitario, FPU) from Researching Human Resources Education National Program, included in Scientific Researching, Development and Technological Innovation National Plan 2008–2011 (EDU/3083/2009). We also wish to thank the Water User Association SORETA located in Tarazona de La Mancha, Albacete, Spain and the Irrigation Users’ Association of “Eastern Mancha” for their support of this work.

Author information

Authors and Affiliations

  1. Regional Centre of Water Research (CREA), UCLM, Ctra. de las Peñas, km 3,2, 02071, Albacete, Spain

    R. Ballesteros, J. F. Ortega & M. A. Moreno

  2. Institute for Regional Development (IDR), Campus Universitario s/n, 02071, Albacete, Spain

    D. Hernández

Authors
  1. R. Ballesteros
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. F. Ortega
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. D. Hernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. A. Moreno
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Ballesteros.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ballesteros, R., Ortega, J.F., Hernández, D. et al. Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part II: application to maize and onion crops of a semi-arid region in Spain. Precision Agric 15, 593–614 (2014). https://doi.org/10.1007/s11119-014-9357-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-014-9357-6

Keywords

Search

Navigation