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Using an unmanned aerial vehicle to evaluate nitrogen variability and height effect with an active crop canopy sensor

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Abstract

Ground-based active sensors have been used in the past with success in detecting nitrogen (N) variability within maize production systems. The use of unmanned aerial vehicles (UAVs) presents an opportunity to evaluate N variability with unique advantages compared to ground-based systems. The objectives of this study were to: determine if a UAV was a suitable platform for use with an active crop canopy sensor to monitor in-season N status of maize, if UAV’s were a suitable platform, is the UAV and active sensor platform a suitable substitute for current handheld methods, and is there a height effect that may be confounding measurements of N status over crop canopies? In a 2013 study comparing aerial and ground-based sensor platforms, there was no difference in the ability of aerial and ground-based active sensors to detect N rate effects on a maize crop canopy. In a 2014 study, an active sensor mounted on a UAV was able to detect differences in crop canopy N status similarly to a handheld active sensor. The UAV/active sensor system (AerialActive) platform used in this study detected N rate differences in crop canopy N status within a range of 0.5–1.5 m above a relatively uniform turfgrass canopy. The height effect for an active sensor above a crop canopy is sensor- and crop-specific, which needs to be taken into account when implementing such a system. Unmanned aerial vehicles equipped with active crop canopy sensors provide potential for automated data collection to quantify crop stress in addition to passive sensors currently in use.

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References

  • Abendroth, L. J., Elmore, R. W., Boyer, M. J., & Marlay, S. K. (2011). Corn growth and development. PMR 1009. Ames, USA: Iowa State University Extension.

    Google Scholar 

  • Barker, D. W., & Sawyer, J. E. (2010). Using active canopy sensors to quantify corn nitrogen stress and nitrogen application rate. Agronomy Journal, 102(3), 964–971.

    Article  CAS  Google Scholar 

  • Berni, J. A. J., Zarco-Tejada, P. J., Suárez, L., & Fereres, E. (2009). Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience Remote Sensing, 47(3), 722–738.

    Article  Google Scholar 

  • Birth, G. S., & McVey, G. R. (1968). Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal, 60(6), 640.

    Article  Google Scholar 

  • Buschmann, C., & Nagel, E. (1993). In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. International Journal of Remote Sensing, 14, 711–722.

    Article  Google Scholar 

  • Ciganda, V. S., Gitelson, A. A., & Schepers, J. S. (2012). How deep does a remote sensor sense? Expression of chlorophyll content in a maize canopy. Remote Sensing of the Environment, 126, 240–247.

    Article  Google Scholar 

  • Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79–97.

    Article  Google Scholar 

  • Dash, J., & Curran, P. J. (2004). The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing., 25(23), 5403–5413.

    Article  Google Scholar 

  • Datt, B. (1999). Visible/near infrared reflectance and chlorophyll content in eucalyptus leaves. International Journal of Remote Sensing, 20(14), 2741–2759.

    Article  Google Scholar 

  • Dellinger, A. E., Schmidt, J. P., & Beegle, D. B. (2008). Developing nitrogen fertilizer recommendations for corn using an active sensor. Agronomy Journal, 100(6), 1546–1552.

    Article  CAS  Google Scholar 

  • Gitelson, A. A. (2003). Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters, 30(5), 4–7.

    Article  Google Scholar 

  • Gitelson, A. A. (2004). Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology, 161(2), 165–173.

    Article  CAS  PubMed  Google Scholar 

  • Holland, K. H., Lamb, D. W., & Schepers, J. S. (2012). Radiometry of proximal active optical sensors (AOS) for agricultural sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing., 5(6), 1793–1802.

    Article  Google Scholar 

  • Holland, K. H., & Schepers, J. S. (2010). Derivation of a variable rate nitrogen application model for in-season fertilization of corn. Agronomy Journal, 102(5), 1415–1424.

    Article  Google Scholar 

  • Kitchen, N. R., Sudduth, K. A., Drummond, S. T., Scharf, P. C., Palm, H. L., Roberts, D. F., et al. (2010). Ground-based canopy reflectance sensing for variable-rate nitrogen corn fertilization. Agronomy Journal, 102(1), 71–84.

    Article  CAS  Google Scholar 

  • Lamb, D. W., Trotter, M. G., & Schneider, D. A. (2009). Ultra low-level airborne (ULLA) sensing of crop canopy reflectance: A case study using a CropCircle™ sensor. Computers and Electronics in Agriculture., 69(1), 86–91.

    Article  Google Scholar 

  • Li, Y., Chen, D., Walker, C. N., & Angus, J. F. (2010). Estimating the nitrogen status of crops using a digital camera. Field Crop Research, 118(3), 221–227.

    Article  Google Scholar 

  • Raun, W. R., Solie, J. B., Taylor, R. K., Arnall, D. B., Mack, C. J., & Edmonds, D. E. (2008). Ramp calibration strip technology for determining midseason nitrogen rates in corn and wheat. Agronomy Journal, 100(4), 1088–1093.

    Article  CAS  Google Scholar 

  • Roberts, D. F., Kitchen, N. R., Scharf, P. C., & Sudduth, K. A. (2010). Will variable-rate nitrogen fertilization using corn canopy reflectance sensing deliver environmental benefits? Agronomy Journal, 102(1), 85–95.

    Article  CAS  Google Scholar 

  • Rouse Jr., J.W., Haas, R.H., Schell, J.A., & Deering, D.W. (1973). Monitoring vegetation systems in the great plains with ERTS. In Third earth resources technology satellite-1 symposium (Vol. 1, pp. 309–330).

  • Samborski, S. M., Tremblay, N., & Fallon, E. (2009). Strategies to make use of plant sensors-based information for nitrogen recommendations. Agronomy Journal, 101(4), 800–816.

    Article  CAS  Google Scholar 

  • Scharf, P. C., Kitchen, N. R., Sudduth, K. A., Davis, J. G., Hubbard, V. C., & Lory, J. A. (2005). Field-scale variability in optimal nitrogen fertilizer rate for corn. Agronomy Journal, 97, 452–461.

    Article  Google Scholar 

  • Scharf, P. C., Shannon, D. K., Palm, H. L., Sudduth, K. A., Drummond, S. T., Kitchen, N. R., et al. (2011). Sensor-based nitrogen applications out-performed producer-chosen rates for corn in on-farm demonstrations. Agronomy Journal, 103(6), 1683–1691.

    Article  Google Scholar 

  • Schmidt, J., Beegle, D., Zhu, Q., & Sripada, R. (2011). Improving in-season nitrogen recommendations for maize using an active sensor. Field Crops Research, 120(1), 94–101.

    Article  Google Scholar 

  • Shanahan, J., Kitchen, N. R., Raun, W., & Schepers, J. (2008). Responsive in-season nitrogen management for cereals. Computers and Electronics in Agriculture, 61(1), 51–62.

    Article  Google Scholar 

  • Shanahan, J. F., Schepers, J. S., Francis, D. D., Varvel, G. E., Wilhelm, W. W., Tringe, J. M., et al. (2001). Use of remote-sensing imagery to estimate corn grain yield. Agronomy Journal, 93, 583–589.

    Article  Google Scholar 

  • Solari, F. (2006). Developing a crop based strategy for on-the-go nitrogen management in irrigated cornfields. AAT 3216347. PhD dissertation, University of Nebraska, Lincoln, USA.

  • Thompson, L. J., Ferguson, R. B., Kitchen, N. R., Frazen, D. W., Mamo, M., Yang, H., et al. (2015). Model and sensor-based recommendation approaches for in-season nitrogen management in corn. Agronomy Journal, 107, 2020–2030.

    Article  CAS  Google Scholar 

  • Viña, A., Gitelson, A. A., Nguy-Robertson, A. L., & Peng, Y. (2011). Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sensing of the Environment., 115(12), 3468–3478.

    Article  Google Scholar 

  • Zillmann, E., Graeff, S., Link, J., Batchelor, W. D., & Claupein, W. (2006). Assessment of cereal nitrogen requirements derived by optical on-the-go sensors on heterogeneous soils. Agronomy Journal, 98(3), 682–690.

    Article  Google Scholar 

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Correspondence to Brian Krienke.

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Krienke, B., Ferguson, R.B., Schlemmer, M. et al. Using an unmanned aerial vehicle to evaluate nitrogen variability and height effect with an active crop canopy sensor. Precision Agric 18, 900–915 (2017). https://doi.org/10.1007/s11119-017-9534-5

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