| Home > Publications database > SIFMap: A python toolbox for creating solar-induced fluorescence maps from UAV-borne snapshot imaging data |
| Journal Article | FZJ-2026-04931 |
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2026
Elsevier
Amsterdam [u.a.]
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Please use a persistent id in citations: doi:10.1016/j.softx.2026.103065 doi:10.34734/FZJ-2026-04931
Abstract: Solar-induced chlorophyll fluorescence (SIF) is a rich source of information for understanding plant photosynthetic processes, carbon assimilation, functional status, and early stress symptoms of plants. As plants assimilate sunlight, they do so across the spectral range of photosynthetically active radiation. Subsequently, during photosynthesis, the excess solar energy is radiated as heat and emitted as chlorophyll fluorescence in the red to far-red spectral range. Recently, SIFcam, a novel drone-based snapshot and filter-based camera array tuned to retrieve SIF at the centimeter scale has been introduced. The absence of transparency regarding methodologies and algorithms in commercial map creation software complicates the investigation of the impacts of various parameter configurations in the SIFcam processing chain. Consequently, we have engineered a Python toolbox that replicates the primary features of commercial software in a transparent and adaptable fashion. To this end, we follow a feature-based image mosaicking pipeline and present modifications to create SIF maps. Moreover, it provides detailed and insightful information while being fully controllable by the user. The toolbox comprises different primary functions, with each function corresponding to a specific stage within the map generation pipeline. The modular design of the toolbox facilitates straightforward adjustments or substitutions of individual steps.
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