Retrieval of sun-induced plant fluorescence in the O$_2$-A absorption band from DESIS imagery
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915019240767488 |
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| author | Buffat, Jim Pato, Miguel Alonso, Kevin Auer, Stefan Carmona, Emiliano Maier, Stefan Müller, Rupert Rademske, Patrick Rascher, Uwe Scharr, Hanno |
| author_facet | Buffat, Jim Pato, Miguel Alonso, Kevin Auer, Stefan Carmona, Emiliano Maier, Stefan Müller, Rupert Rademske, Patrick Rascher, Uwe Scharr, Hanno |
| contents | We provide the first method allowing to retrieve spaceborne SIF maps at 30 m ground resolution with a strong correlation ($r^2=0.6$) to high-quality airborne estimates of sun-induced fluorescence (SIF). SIF estimates can provide explanatory information for many tasks related to agricultural management and physiological studies. While SIF products from airborne platforms are accurate and spatially well resolved, the data acquisition of such products remains science-oriented and limited to temporally constrained campaigns. Spaceborne SIF products on the other hand are available globally with often sufficient revisit times. However, the spatial resolution of spaceborne SIF products is too small for agricultural applications. In view of ESA's upcoming FLEX mission we develop a method for SIF retrieval in the O$_2$-A band of hyperspectral DESIS imagery to provide first insights for spaceborne SIF retrieval at high spatial resolution. To this end, we train a simulation-based self-supervised network with a novel perturbation based regularizer and test performance improvements under additional supervised regularization of atmospheric variable prediction. In a validation study with corresponding HyPlant derived SIF estimates at 740 nm we find that our model reaches a mean absolute difference of 0.78 mW / nm / sr / m$^2$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08925 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Retrieval of sun-induced plant fluorescence in the O$_2$-A absorption band from DESIS imagery Buffat, Jim Pato, Miguel Alonso, Kevin Auer, Stefan Carmona, Emiliano Maier, Stefan Müller, Rupert Rademske, Patrick Rascher, Uwe Scharr, Hanno Computer Vision and Pattern Recognition Artificial Intelligence Geophysics We provide the first method allowing to retrieve spaceborne SIF maps at 30 m ground resolution with a strong correlation ($r^2=0.6$) to high-quality airborne estimates of sun-induced fluorescence (SIF). SIF estimates can provide explanatory information for many tasks related to agricultural management and physiological studies. While SIF products from airborne platforms are accurate and spatially well resolved, the data acquisition of such products remains science-oriented and limited to temporally constrained campaigns. Spaceborne SIF products on the other hand are available globally with often sufficient revisit times. However, the spatial resolution of spaceborne SIF products is too small for agricultural applications. In view of ESA's upcoming FLEX mission we develop a method for SIF retrieval in the O$_2$-A band of hyperspectral DESIS imagery to provide first insights for spaceborne SIF retrieval at high spatial resolution. To this end, we train a simulation-based self-supervised network with a novel perturbation based regularizer and test performance improvements under additional supervised regularization of atmospheric variable prediction. In a validation study with corresponding HyPlant derived SIF estimates at 740 nm we find that our model reaches a mean absolute difference of 0.78 mW / nm / sr / m$^2$. |
| title | Retrieval of sun-induced plant fluorescence in the O$_2$-A absorption band from DESIS imagery |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Geophysics |
| url | https://arxiv.org/abs/2411.08925 |