Skip to main content

Spatio-temporal monitoring of cotton cultivation using ground-based and airborne multispectral sensors in GIS environment

Environmental Monitoring and Assessment volume 189, Article number: 323 (2017) Cite this article

Abstract

Multispectral sensor capability of capturing reflectance data at several spectral channels, together with the inherent reflectance responses of various soils and especially plant surfaces, has gained major interest in crop production. In present study, two multispectral sensing systems, a ground-based and an aerial-based, were applied for the multispatial and temporal monitoring of two cotton fields in central Greece. The ground-based system was Crop Circle ACS-430, while the aerial consisted of a consumer-level quadcopter (Phantom 2) and a modified Hero3+ Black digital camera. The purpose of the research was to monitor crop growth with the two systems and investigate possible interrelations between the derived well-known normalized difference vegetation index (NDVI). Five data collection campaigns were conducted during the cultivation period and concerned scanning soil and plants with the ground-based sensor and taking aerial photographs of the fields with the unmanned aerial system. According to the results, both systems successfully monitored cotton growth stages in terms of space and time. The mean values of NDVI changes through time as retrieved by the ground-based system were satisfactorily modelled by a second-order polynomial equation (R 2 0.96 in Field 1 and 0.99 in Field 2). Further, they were highly correlated (r 0.90 in Field 1 and 0.74 in Field 2) with the according values calculated via the aerial-based system. The unmanned aerial system (UAS) can potentially substitute crop scouting as it concerns a time-effective, non-destructive and reliable way of soil and plant monitoring.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  • Aldana-Jague, E., Heckrath, G., Macdonald, A., van Wesemael, B., & Van Oost, K. (2016). UAS-based soil carbon mapping using VIS-NIR (480–1000 nm) multi-spectral imaging: potential and limitations. Geoderma, 275, 55–66.

    CAS  Article  Google Scholar 

  • Ali, A. M., Thind, H. S., Sharma, S., & Yadvinder-Singh. (2015). Site-specific nitrogen management in dry direct-seeded rice using chlorophyll meter and leaf colour chart. Pedosphere, 25(1), 72–81.

    Article  Google Scholar 

  • Bellvert, J., Zarco-Tejada, P. J., Girona, J., & Fereres, E. (2014). Mapping crop water stress index in a ‘pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle. Precision Agriculture, 15, 361–376.

    Article  Google Scholar 

  • Bendig, J., Bolten, A., Bareth, G. (2013). UAV-based imaging for multi-temporal, very high resolution crop surface models to monitor crop growth variability. PFG, 6, 0551-0562, Stuttgart.

  • Bourgeon, M.-A., Paoli, J.-N., Jones, G., Villette, S., & Gée, C. (2016). Field radiometric calibration of a multispectral on-the-go sensor dedicated to the characterization of vineyard foliage. Computers and Electronics in Agriculture, 123, 184–194.

    Article  Google Scholar 

  • Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., & Gattelli, M. (2015). Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sensing, 7, 4026–4047.

    Article  Google Scholar 

  • Cao, Q., Miao, Y., Wang, H., Huang, S., Cheng, S., Khosla, R., & Jiang, R. (2013). Non-destructive estimation of rice plant nitrogen status with Crop Circle multispectral active canopy sensor. Field Crops Research, 154, 133–144.

    Article  Google Scholar 

  • Farooque, A. A., Chang, Y. K., Zaman, Q. U., Groulx, D., Schumann, A. W., & Esau, T. J. (2013). Performance evaluation of multiple ground based sensors mounted on a commercial wild blueberry harvester to sense plant height, fruit yield and topographic features in real-time. Computers and Electronics in Agriculture, 91, 135–144.

    Article  Google Scholar 

  • Florax, R. J. G. M., Voortman, R. L., & Brouwer, J. (2002). Spatial dimensions of precision agriculture: a spatial econometric analysis of millet yield on Sahelian coversands. Agricultural Economics, 27, 425–443.

    Article  Google Scholar 

  • Freeman, P. K., & Freeland, R. S. (2015). Agricultural UAVs in the U.S.: potential, policy, and hype. Remote Sensing Applications: Society and Environment, 2, 35–43.

    Article  Google Scholar 

  • Goovaerts, P. (1998). Geostatistical tools for characterizing the spatial variability of microbiological and physico-chemical soil properties. Biology and Fertility of Soils, 27, 315–334.

    CAS  Article  Google Scholar 

  • He, J., Wang, J., He, D., Dong, J., & Wang, Y. (2011). The design and implementation of an integrated optimal fertilization decision support system. Mathematical and Computer Modelling, 54(3–4), 1167–1174.

    Article  Google Scholar 

  • Huang, Y. B., Thomson, S. J., Hoffmann, W. C., Lan, Y. B., & Fritz, B. K. (2013). Development and prospect of unmanned aerial vehicle technologies for agricultural production management. International Journal Agricultural and Biological Engineering, 6(3), 1–10.

    Google Scholar 

  • Husson, E., Lindgren, F., & Ecke, F. (2014). Assessing biomass and metal contents in riparian vegetation along a pollution gradient using an unmanned aircraft system. Water Air Soil Pollution, 225, 1957.

    Article  Google Scholar 

  • Knipling, B. E. (1970). Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment, 1(3), 155–159.

    Article  Google Scholar 

  • Lee, W. S., & Ehsani, R. (2015). Sensing systems for precision agriculture in Florida. Computers and Electronics in Agriculture, 112, 2–9.

    Article  Google Scholar 

  • Lelong, C. C. D., Burger, P., Jubelin, G., Roux, B., Sylvain Labbé, S., & Baret, F. (2008). Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors, 8, 3557–3585.

    Article  Google Scholar 

  • Li, H., Lascano, R. J., Barnes, E. M., Booker, J., Wilson, L. T., Bronson, K. F., & Segarra, E. (2001). Multispectral reflectance of cotton related to plant growth, soil water and texture, and site elevation. Agronomy Journal, 93, 1327–1337.

    Article  Google Scholar 

  • Liu, Y., Song, P., Peng, J., & Ye, C. (2012). A physical explanation of the variation in threshold for delineating terrestrial water surfaces from multi-temporal images: effects of radiometric correction. International Journal of Remote Sensing, 33(18), 5862–5875.

    Article  Google Scholar 

  • Longley, P.A., Goodchild, M.F., Maguire, D.J., Rhind, D.W. (2015). Geographic information systems and science. John Wiley & Sons, Inc. pp. 414–7.

  • Michez, A., Piégay, H., Lisein, J., Claessens, H., & Lejeune, P. (2016). Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system. Environmental Monitoring and Assessment, 188, 146. doi:10.1007/s10661-015-4996-2.

    Article  Google Scholar 

  • Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosystems Engineering, 114, 358–371.

    Article  Google Scholar 

  • National Research Council. (1997). Precision agriculture in the 21st century: geospatial and information technologies in crop management (p. 19). Washington: National Academy Press.

    Google Scholar 

  • Ouédraogo, M. M., Degré, A., Debouche, C., & Lisein, J. (2014). The evaluation of unmanned aerial system-based photogrammetry and terrestrial laser scanning to generate DEMs of agricultural watersheds. Geomorphology, 214, 339–355.

    Article  Google Scholar 

  • Papadopoulos, A., Kalivas, D., & Hatzichristos, T. (2015). GIS modelling for site-specific nitrogen fertilization towards soil sustainability. Sustainability, 7, 6684–6705.

    CAS  Article  Google Scholar 

  • Peng, S., Buresh, R. J., Huang, J., Zhong, X., Zou, Y., Yang, J., Wang, G., Liu, Y., Hu, R., Tang, Q., Cui, K., Zhang, F., & Dobermann, A. (2010). Improving nitrogen fertilization in rice by site-specific N management. A review. Agronomy for Sustainable Development, 30, 649–656.

    CAS  Article  Google Scholar 

  • Reyniers, M., Vrindts, E., & Baerdemaeker, J. D. (2006). Comparison of an aerial-based system and an on the ground continuous measuring device to predict yield of winter wheat. European Journal Agronomy, 24, 87–94.

    Article  Google Scholar 

  • Shah, S., & Aggarwal, J. K. (1996). Intrinsic parameter calibration procedure for a (high-distortion) fish-eye lens camera with distortion model and accuracy estimation. Pattern Recognition, 29(11), 1775–1788.

    Article  Google Scholar 

  • Tang, L., & Shao, G. (2015). Drone remote sensing for forestry research and practices. Journal of Forest Research, 26(4), 791–797.

    Article  Google Scholar 

  • Vega, F. A., Ramirez, F. C., Saiz, M. P., & Rosua, F. O. (2015). Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop. Biosystems Engineering, 132, 19–27.

    Article  Google Scholar 

  • Zarco-Tejada, P. J., Ustin, S. L., & Whiting, M. L. (2005). Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery. Agronomy Journal, 97, 641–653.

    Article  Google Scholar 

  • Zhang, H., Lan, Y., Suh, C. P. C., Westbrook, J., Hoffmann, W. C., Yang, C., & Huang, Y. (2013). Fusion of remotely sensed data from airborne and ground-based sensors to enhance detection of cotton plants. Computers and Electronics in Agriculture, 93, 55–59.

    Article  Google Scholar 

  • Zhang, C., Walters, D., & Kovacs, J. M. (2014). Applications of low altitude remote sensing in agriculture upon farmers' requests—a case study in northeastern Ontario, Canada. PloS One, 9(11), e112894. doi:10.1371/journal.pone.0112894.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the two cotton producers and the local agronomist Charalampos Diamantis for their help. The research project was funded under the Action “Research & Technology Development Innovation projects (AgroETAK)”, MIS 453350, in the framework of the Operational Program “Human Resources Development”. It was co-funded by the European Social Fund and by national resources through the National Strategic Reference Framework 2007–2013 (NSRF 2007–2013) coordinated by the Hellenic Agricultural Organisation “DEMETER”.

Author information

Affiliations

  1. Department of Phytopathology, Laboratory of Non-Parasitic Diseases, Benaki Phytopathological Institute, 8 St. Delta Str., 14561, Kifissia, Athens, Greece

    Antonis Papadopoulos

  2. Department of Natural Resource Management & Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos Str., 11855, Athens, Greece

    Dionissios Kalivas

  3. Institute of Soil and Water Resources, Department of Soil Science of Athens, Hellenic Agricultural Organization—DEMETER, 1 S. Venizelou Str., 14123, Lykovrissi, Athens, Greece

    Sid Theocharopoulos

Authors
  1. Antonis Papadopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dionissios Kalivas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sid Theocharopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Antonis Papadopoulos.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Papadopoulos, A., Kalivas, D. & Theocharopoulos, S. Spatio-temporal monitoring of cotton cultivation using ground-based and airborne multispectral sensors in GIS environment. Environ Monit Assess 189, 323 (2017). https://doi.org/10.1007/s10661-017-6042-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-017-6042-z

Keywords

  • Precision agriculture
  • Multispectral sensors
  • Unmanned aerial system
  • NDVI
  • Cotton