The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-7/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 149–155, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-149-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 149–155, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-149-2015

  28 Apr 2015

28 Apr 2015

Crop Ground Cover Fraction and Canopy Chlorophyll Content Mapping using RapidEye imagery

E. Zillmann1, M Schönert1, H. Lilienthal2, B. Siegmann3, T Jarmer3, P. Rosso1, and T. Weichelt1 E. Zillmann et al.
  • 1BlackBridge, Kurfürstendamm 22, 10719 Berlin, Germany
  • 2Julius-Kühn-Institute (JKI), Federal Research Centre for Cultivated Plants, 38116 Braunschweig, Germany
  • 3Institute for Geoinformatics and Remote Sensing, University of Osnabrueck, 49076 Osnabrueck, Germany

Keywords: Ground Cover, Canopy Chlorophyll Content, RapidEye, Spatial Variability, Precision Agriculture

Abstract. Remote sensing is a suitable tool for estimating the spatial variability of crop canopy characteristics, such as canopy chlorophyll content (CCC) and green ground cover (GGC%), which are often used for crop productivity analysis and site-specific crop management. Empirical relationships exist between different vegetation indices (VI) and CCC and GGC% that allow spatial estimation of canopy characteristics from remote sensing imagery. However, the use of VIs is not suitable for an operational production of CCC and GGC% maps due to the limited transferability of derived empirical relationships to other regions. Thus, the operational value of crop status maps derived from remotely sensed data would be much higher if there was no need for reparametrization of the approach for different situations.

This paper reports on the suitability of high-resolution RapidEye data for estimating crop development status of winter wheat over the growing season, and demonstrates two different approaches for mapping CCC and GGC%, which do not rely on empirical relationships. The final CCC map represents relative differences in CCC, which can be quickly calibrated to field specific conditions using SPAD chlorophyll meter readings at a few points. The prediction model is capable of predicting SPAD readings with an average accuracy of 77%. The GGC% map provides absolute values at any point in the field. A high R² value of 80% was obtained for the relationship between estimated and observed GGC%. The mean absolute error for each of the two acquisition dates was 5.3% and 8.7%, respectively.