High spatial and spectral remote sensing for detailed mapping of potato plant parameters
Stephanie Delalieux, Dries Raymaekers, K. Nackaerts, Eija Honkavaara, Jussi Soukkamäki, J. Borne
Abstract
Stephanie Delalieux, Dries Raymaekers, K. Nackaerts, Eija Honkavaara, Jussi Soukkamäki, J. Borne
Abstract
This preliminary study shows the potential of highly flexible drones and hyperspectral technology to make detailed chlorophyll maps of an experimental potato field. A novel, innovative hyperspectral frame camera (Rikola Ltd) was employed to gather the spectral information (24 bands) at 5 cm spatial resolution. A first challenge therefore was to setup a dedicated preprocessing chain for the images coming from this novel sensor. Coregistration of the images was successful resulting in an image displacement of only 1–2 pixels. The chlorophyll map created from the Rikola data corresponded well to the field measurements. R2values of 0.70 were found for a linear relation between the averaged field chlorophyll measurements and the mean of the (R780-R550)/(R780+R550) index calculated for all vegetated Rikola pixels within an experimental potato cultivar plot. These chlorophyll maps which are directly linked to the vegetation status of the crops can be used by the farmer for better management decision making.
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This preliminary study shows the potential of highly flexible drones and hyperspectral technology to make detailed chlorophyll maps of an experimental potato field. A novel, innovative hyperspectral frame camera (Rikola Ltd) was employed to gather the spectral information (24 bands) at 5 cm spatial resolution. A first challenge therefore was to setup a dedicated preprocessing chain for the images coming from this novel sensor. Coregistration of the images was successful resulting in an image displacement of only 1–2 pixels. The chlorophyll map created from the Rikola data corresponded well to the field measurements. R2values of 0.70 were found for a linear relation between the averaged field chlorophyll measurements and the mean of the (R780-R550)/(R780+R550) index calculated for all vegetated Rikola pixels within an experimental potato cultivar plot. These chlorophyll maps which are directly linked to the vegetation status of the crops can be used by the farmer for better management decision making.
Key concepts: Hyperspectral imaging, Remote sensing, Pixel, Image resolution, Computer science, Preprocessor, Field (mathematics), Chlorophyll