Volume XLI-B8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 837-843, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-837-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 837-843, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-837-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  23 Jun 2016

23 Jun 2016

COMPARISON OF UNCALIBRATED RGBVI WITH SPECTROMETER-BASED NDVI DERIVED FROM UAV SENSING SYSTEMS ON FIELD SCALE

G. Bareth1,3, A. Bolten1,3, M. L. Gnyp2, S. Reusch2, and J. Jasper2 G. Bareth et al.
  • 1Institute of Geography, GIS & RS Group, University of Cologne, Albertus-Magnus-Platz, 50923 Cologne, Germany
  • 2Research Centre Hanninghof, Yara International ASA, 48249 Dülmen, Germany
  • 3Spatial Data Services Cologne UG, Cologne, Germany

Keywords: Remote Sensing, UAV, agriculture, Yara N-Sensor, winter wheat, nitrogen, vegetation index, hyperspectral, RGB

Abstract. The development of UAV-based sensing systems for agronomic applications serves the improvement of crop management. The latter is in the focus of precision agriculture which intends to optimize yield, fertilizer input, and crop protection. Besides, in some cropping systems vehicle-based sensing devices are less suitable because fields cannot be entered from certain growing stages onwards. This is true for rice, maize, sorghum, and many more crops. Consequently, UAV-based sensing approaches fill a niche of very high resolution data acquisition on the field scale in space and time. While mounting RGB digital compact cameras to low-weight UAVs (< 5 kg) is well established, the miniaturization of sensors in the last years also enables hyperspectral data acquisition from those platforms. From both, RGB and hyperspectral data, vegetation indices (VIs) are computed to estimate crop growth parameters. In this contribution, we compare two different sensing approaches from a low-weight UAV platform (< 5 kg) for monitoring a nitrogen field experiment of winter wheat and a corresponding farmers’ field in Western Germany. (i) A standard digital compact camera was flown to acquire RGB images which are used to compute the RGBVI and (ii) NDVI is computed from a newly modified version of the Yara N-Sensor. The latter is a well-established tractor-based hyperspectral sensor for crop management and is available on the market since a decade. It was modified for this study to fit the requirements of UAV-based data acquisition. Consequently, we focus on three objectives in this contribution: (1) to evaluate the potential of the uncalibrated RGBVI for monitoring nitrogen status in winter wheat, (2) investigate the UAV-based performance of the modified Yara N-Sensor, and (3) compare the results of the two different UAV-based sensing approaches for winter wheat.