Littérature scientifique sur le sujet « Solar Induced Fluorescence (SIF) »
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Articles de revues sur le sujet "Solar Induced Fluorescence (SIF)"
Badie, J. M., G. Flamant, T. Guillard et D. Laplaze. « Solar-induced fluorescence (SIF) of C2 radical ». Chemical Physics Letters 358, no 3-4 (mai 2002) : 199–206. http://dx.doi.org/10.1016/s0009-2614(02)00445-1.
Texte intégralZhou, Y., X. Lu, Y. Huang, Z. Gao et Y. Zheng. « NEW SOLAR-INDUCED CHLOROPHYLL FLUORESCENCE RETRIEVAL ALGORITHM BASED ON TANSAT SATELLITE DATA ». ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (3 août 2020) : 209–14. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-209-2020.
Texte intégralJoiner, Joanna, Yasuko Yoshida, Philipp Köehler, Petya Campbell, Christian Frankenberg, Christiaan van der Tol, Peiqi Yang, Nicholas Parazoo, Luis Guanter et Ying Sun. « Systematic Orbital Geometry-Dependent Variations in Satellite Solar-Induced Fluorescence (SIF) Retrievals ». Remote Sensing 12, no 15 (22 juillet 2020) : 2346. http://dx.doi.org/10.3390/rs12152346.
Texte intégralDoughty, Russell, Thomas P. Kurosu, Nicholas Parazoo, Philipp Köhler, Yujie Wang, Ying Sun et Christian Frankenberg. « Global GOSAT, OCO-2, and OCO-3 solar-induced chlorophyll fluorescence datasets ». Earth System Science Data 14, no 4 (5 avril 2022) : 1513–29. http://dx.doi.org/10.5194/essd-14-1513-2022.
Texte intégralZhang, Yao, Joanna Joiner, Seyed Hamed Alemohammad, Sha Zhou et Pierre Gentine. « A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks ». Biogeosciences 15, no 19 (2 octobre 2018) : 5779–800. http://dx.doi.org/10.5194/bg-15-5779-2018.
Texte intégralDu, Shanshan, Liangyun Liu, Xinjie Liu, Jian Guo, Jiaochan Hu, Shaoqiang Wang et Yongguang Zhang. « SIFSpec : Measuring Solar-Induced Chlorophyll Fluorescence Observations for Remote Sensing of Photosynthesis ». Sensors 19, no 13 (8 juillet 2019) : 3009. http://dx.doi.org/10.3390/s19133009.
Texte intégralZhang, Lifu, Na Qiao, Changping Huang et Siheng Wang. « Monitoring Drought Effects on Vegetation Productivity Using Satellite Solar-Induced Chlorophyll Fluorescence ». Remote Sensing 11, no 4 (13 février 2019) : 378. http://dx.doi.org/10.3390/rs11040378.
Texte intégralXu, Shan, Zhigang Liu, Shuai Han, Zhuang Chen, Xue He, Huarong Zhao et Sanxue Ren. « Exploring the Sensitivity of Solar-Induced Chlorophyll Fluorescence at Different Wavelengths in Response to Drought ». Remote Sensing 15, no 4 (16 février 2023) : 1077. http://dx.doi.org/10.3390/rs15041077.
Texte intégralHe, Katherine, Wenhong Li et Ruoying He. « Variability of Remotely Sensed Solar-Induced Chlorophyll Fluorescence in Relation to Climate Indices ». Environments 9, no 9 (19 septembre 2022) : 121. http://dx.doi.org/10.3390/environments9090121.
Texte intégralPaynter, Ian, Bruce Cook, Lawrence Corp, Jyoteshwar Nagol et Joel McCorkel. « Characterization of FIREFLY, an Imaging Spectrometer Designed for Remote Sensing of Solar Induced Fluorescence ». Sensors 20, no 17 (19 août 2020) : 4682. http://dx.doi.org/10.3390/s20174682.
Texte intégralThèses sur le sujet "Solar Induced Fluorescence (SIF)"
DI, NINNI PAOLA. « A statistical method for the retrieval of the Solar Induced Fluorescence of vegetation by means of radiance spectra from space : fundamentals, performance and robustness analysis ». Doctoral thesis, Università di Siena, 2017. http://hdl.handle.net/11365/1013500.
Texte intégralCESANA, ILARIA. « Solar-induced chlorophyll fluorescence signal retrieval in terrestrial vegetation and inland waters from hyperspectral proximal sensing ». Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/366236.
Texte intégralThe PhD research aimed to develop novel strategies able to better retrieve and interpret the chlorophyll Solar-Induced Fluorescence (SIF) signal emitted by terrestrial vegetation and inland waters at ground level, to advance the understanding of ecosystems structure and functioning. SIF metrics were defined taking advantage of the full SIF spectrum available from the recently developed “spectrum-fitting” algorithm (SpecFit). The metrics were designed to characterize the SIF spectrum, in terms of red and far-red peaks maximum values (SIFred, SIFfar-red), corresponding wavelengths and the spectrally integrated value (SIFINT). SIF typically evaluated in the O2-A (SIF760) and O2-B (SIF687) bands and reflectance indices (used as proxies for canopy biophysical parameters) have been compared to the SIF spectrum. The reflectance indices selected are the NDVIred-edge, CIred-edge, NIRv and PRI. The analysis has been carried out at seasonal/diurnal scales, exploiting top-of canopy (TOC) spectral measurements acquired over three crops. The SIF evaluated at the peaks always show a strong correlation with the corresponding O2 bands values, while the SIFINT represents a more complete parameter and shows peculiar dynamics. At diurnal scale, the combined use of reflectance indices and TOC SIF metrics allows to gain a better knowledge of the crops dynamics. Seasonally, the SIF and reflectance indices show more similar temporal evolution along the growth-phases because they are mainly driven by changes in the overall canopy biomass, chlorophyll content and incident light. The reabsorption of the SIF within the canopy-leaf system affects the overall SIF spectral shape and magnitude at this temporal scale. As demonstrated on the synthetic dataset, the reabsorption effect prevents an accurate evaluation of the fluorescence quantum yield (SIFyield). Correcting the TOC SIF spectrum for the reabsorption is pivotal. In this regard, two different approaches have been developed and tested. The parametric method enables to correct SIF for the reabsorption (SIFRC) establishing parametric relationships with spectral variables routinely measured at TOC. The method accuracy depends on the plant growth phase, showing better results for medium-dense canopies. This behavior compromises the application of the method on the full seasonal analysis. The second approach based on Fourier-Machine Learning algorithm retrieves the SIFRC, and biophysical parameters (LAI, Cab, SIFyield, aPAR) with a better accuracy for all the conditions. The two approaches have been compared by considering synthetic simulations and real field measurements. Two methods were developed and tested starting from different assumptions: the parametric method can be used in a simpler way but it lacks accuracy for sparse conditions; while the Fourier-Machine Learning algorithm is more complex but offer better results. Regarding clear lake waters, a novel version of the Fluorescence Line Height approach has been implemented. The SIF proxy obtained agree with the temporal evolution of other conventional spectral indices (EPAR, R550 and [Chl-a]). Novel phytoplankton primary production models have been defined and tested adapting the vegetation Light Use Efficiency model for inland waters. Promising results have been obtained when the SIFFLH and a novel photosynthesis efficiency proxy here introduced are considered. In conclusion, the results obtained highlight the relevance to retrieve the SIF spectrum and the importance to employ SIF reabsorption correction methods to obtain relevant parameters better related with terrestrial vegetation functioning and less affected from canopy structure. This study has demonstrated that the hyperspectral and frequency measurements allow to follow the phytoplankton dynamics, particularly in clear sky days. Furthermore, the use of parameters linked to the SIF represents a promising approach for monitoring the phytoplankton primary production in lakes.
MacBean, Natasha, Fabienne Maignan, Cédric Bacour, Philip Lewis, Philippe Peylin, Luis Guanter, Philipp Köhler, Jose Gómez-Dans et Mathias Disney. « Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data ». NATURE PUBLISHING GROUP, 2018. http://hdl.handle.net/10150/627071.
Texte intégralKhosravi, Narges [Verfasser], John P. [Akademischer Betreuer] Burrows, John P. [Gutachter] Burrows et Justus [Gutachter] Notholt. « Space-Borne Retrieval of Solar-Induced Plant Fluorescence and its Relationship to Photosynthetic Parameters / Narges Khosravi ; Gutachter : John P. Burrows, Justus Notholt ; Betreuer : John P. Burrows ». Bremen : Staats- und Universitätsbibliothek Bremen, 2017. http://d-nb.info/116577223X/34.
Texte intégralChapitres de livres sur le sujet "Solar Induced Fluorescence (SIF)"
Zarco-Tejada, Pablo J., John R. Miller et Gina H. Mohammed. « Remote Sensing of Solar-Induced Chlorophyll Fluorescence from Vegetation Hyperspectral Reflectance and Radiative Transfer Simulation ». Dans From Laboratory Spectroscopy to Remotely Sensed Spectra of Terrestrial Ecosystems, 233–69. Dordrecht : Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-017-1620-8_11.
Texte intégralFrankenberg, C., et J. Berry. « Solar Induced Chlorophyll Fluorescence : Origins, Relation to Photosynthesis and Retrieval ». Dans Comprehensive Remote Sensing, 143–62. Elsevier, 2018. http://dx.doi.org/10.1016/b978-0-12-409548-9.10632-3.
Texte intégralActes de conférences sur le sujet "Solar Induced Fluorescence (SIF)"
Fan, Joshua, Di Chen, Jiaming Wen, Ying Sun et Carla Gomes. « Monitoring Vegetation From Space at Extremely Fine Resolutions via Coarsely-Supervised Smooth U-Net ». Dans Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California : International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/703.
Texte intégralRoscher, U., K. Acebron, J. Bendig, J. Kramer, V. Krieger, J. Quiros-Vargas, B. Siegmann et O. Muller. « Measuring and Understanding the Dynamics of Solar-Induced Fluorescence (SIF) and its Relation to Photochemical and Non-Photochemical Energy Dissipation - Scaling Leaf Level Regulation to Canopy and Ecosystem Remote Sensing ». Dans IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9554870.
Texte intégralSmorenburg, Kees, Gregory B. Courreges-Lacoste, Michael Berger, Claus Buschman, Andrew J. Court, Umberto Del Bello, Gabriele Langsdorf et al. « Remote sensing of solar-induced fluorescence of vegetation ». Dans International Symposium on Remote Sensing, sous la direction de Manfred Owe et Guido D'Urso. SPIE, 2002. http://dx.doi.org/10.1117/12.454193.
Texte intégralGomezChova, L. « Solar induced fluorescence measurements using a field spectroradiometer ». Dans EARTH OBSERVATION FOR VEGETATION MONITORING AND WATER MANAGEMENT. AIP, 2006. http://dx.doi.org/10.1063/1.2349354.
Texte intégralQiu, Ruonan, Ge Han, Xin Ma et Wei Gong. « Solar-Induced Chlorophyll Fluorescence is Very Sensitive to Drought ». Dans IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9553404.
Texte intégralBarducci, A., D. Guzzi, C. Lastri, P. Marcoionni, V. Nardino, I. Pippi, V. Raimondi et P. Sandri. « High spectral resolution imager for solar induced fluorescence observation ». Dans SPIE Remote Sensing. SPIE, 2011. http://dx.doi.org/10.1117/12.898225.
Texte intégralShunshi Hu, Lifu Zhang et Qingxi Tong. « Estimation of Solar Induced Chlorophyll Fluorescence from EO-1 Hyperion ». Dans 2012 Second International Workshop on Earth Observation and Remote Sensing Applications (EORSA). IEEE, 2012. http://dx.doi.org/10.1109/eorsa.2012.6261191.
Texte intégralMiddletona, E. M., L. A. Corp et P. K. E. Campbell. « Canopy level solar induced fluorescence for vegetation in controlled experiments ». Dans 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4423661.
Texte intégralPagán, Brianna, Brecht Martens, Wouter Maes et Diego Miralles. « Satellite observed solar induced fluorescence to monitor global plant stress ». Dans First International Electronic Conference on the Hydrological Cycle. Basel, Switzerland : MDPI, 2017. http://dx.doi.org/10.3390/chycle-2017-04874.
Texte intégralLi, Rong, et Feng Zhao. « Accuracy assessment on reconstruction algorithms of solar-induced Fluorescence Spectrum ». Dans IGARSS 2016 - 2016 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2016. http://dx.doi.org/10.1109/igarss.2016.7729442.
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