Statistical-based retrieval of solar-induced chlorophyll fluorescence at proximal and airborne scales using (imaging) spectroscopy data

Siheng Wang; Dong Yang; Rong Yang; Ting Li; Hezhi Sun

doi:10.1117/12.2575939

5 November 2020 Statistical-based retrieval of solar-induced chlorophyll fluorescence at proximal and airborne scales using (imaging) spectroscopy data

Siheng Wang, Dong Yang, Rong Yang, Ting Li, Hezhi Sun

Author Affiliations +

Proceedings Volume 11566, AOPC 2020: Optical Spectroscopy and Imaging; and Biomedical Optics; 115660A (2020) https://doi.org/10.1117/12.2575939
Event: Applied Optics and Photonics China (AOPC 2020), 2020, Beijing, China

Abstract

Solar-induced chlorophyll fluorescence (SIF) is a weak optical signal emitted by chlorophyll under natural illumination. SIF ranges from 600 nm to 800 nm and is assumed as a direct proxy for actual photosynthesis. Due to recent advances in spectroscopy and retrieval techniques, SIF can be retrieved from hyperspectral remote sensing data. Statistical-based approach, typically the singular value decomposition (SVD) method, is one of the two practical strategies for SIF retrieval. A statistical-based approach collects SIF-free measurements of Fraunhofer Lines as training dataset, extracts their spectral features by a statistical approach and then applies the extracted features in the forward SIF retrieval model. In this paper, we first evaluated the performance of the SVD approach in SIF retrieval at proximal scale. Good consistency was found between diurnal SIF cycles given by the SVD method and a 3-FLD method, with SVD-based SIF values higher than those given by 3-FLD. We then applied the SVD method on HyPlant imaging spectroscopy airborne data. Spatial distribution of SIF was successfully depicted using the SVD method. SIF was in a good spatial accordance with NDVI, but the former exhibited a stronger heterogeneity. For both proximal and airborne scales, the in-filling of the Fraunhofer Lines by SIF was successfully detected by the SVD method. However, whether SVD could induce a systematic error should be further studied. It can be concluded that a statistical-based SIF retrieval method is a reasonable alternative to traditional O₂-lines-based methods, especially when synchronous SIF-free spectrum or pixelwise atmospheric correction is unavailable.

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Siheng Wang, Dong Yang, Rong Yang, Ting Li, and Hezhi Sun "Statistical-based retrieval of solar-induced chlorophyll fluorescence at proximal and airborne scales using (imaging) spectroscopy data", Proc. SPIE 11566, AOPC 2020: Optical Spectroscopy and Imaging; and Biomedical Optics, 115660A (5 November 2020); https://doi.org/10.1117/12.2575939

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KEYWORDS

Feature extraction

Vegetation

Imaging spectroscopy

Remote sensing

Spectroscopy

Spectral resolution

Statistical modeling

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