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| Hauptseite > Publikationsdatenbank > The potential of spatial aggregation to extract remotely sensed sun-induced fluorescence (SIF) of small-sized experimental plots for applications in crop phenotyping |
| Hauptseite > Publikationsdatenbank > The potential of spatial aggregation to extract remotely sensed sun-induced fluorescence (SIF) of small-sized experimental plots for applications in crop phenotyping |
| Journal Article | FZJ-2021-04066 |
; ; ; ;
2021
Elsevier Science
Amsterdam [u.a.]
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Please use a persistent id in citations: doi:10.1016/j.jag.2021.102565
Abstract: Airborne measurements of sun-induced chlorophyll fluorescence (SIF) are a promising tool for monitoring plantfunctioning on different scales. However, currently operational airborne imaging spectrometers for SIF measurementsstill have limited spatial resolution and pointing accuracy. This is challenging in terms of the practicaluse of SIF maps for crop breeding and plant phenotyping. We developed and tested two spatial aggregationapproaches to make airborne SIF data usable in experimental settings with a high number of small experimentalplots. The two aggregation approaches generating representative SIF values for experimental plots demonstratedthe potential to be used in crop phenotyping. The first aggregation approach (Approach A) aggregates pixelvalues directly on SIF maps, whereas the second approach (Approach B) aggregates at-sensor radiance before SIFretrieval. The statistical analysis showed that Approaches A and B led to significantly different SIF products forsingle experimental plots (p < 0.001). To evaluate the usability of the two approaches, aggregated SIF productswere fitted against ground-based reference measurements. We found that Approach B provided a better representationof ground truth SIF760 (R2 = 0.61, p < 0.001) than Approach A (R2 = 0.55, p < 0.001) when combinedwith weighted averaging and robust outlier detection. Furthermore, our results suggest that a slight decrease inthe spatial resolution of the image data improves accuracy of aggregation.
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