Literatura académica sobre el tema "Solar Induced Fluorescence (SIF)"
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Artículos de revistas sobre el tema "Solar Induced Fluorescence (SIF)"
Badie, J. M., G. Flamant, T. Guillard y D. Laplaze. "Solar-induced fluorescence (SIF) of C2 radical". Chemical Physics Letters 358, n.º 3-4 (mayo de 2002): 199–206. http://dx.doi.org/10.1016/s0009-2614(02)00445-1.
Texto completoZhou, Y., X. Lu, Y. Huang, Z. Gao y 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 de agosto de 2020): 209–14. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-209-2020.
Texto completoJoiner, Joanna, Yasuko Yoshida, Philipp Köehler, Petya Campbell, Christian Frankenberg, Christiaan van der Tol, Peiqi Yang, Nicholas Parazoo, Luis Guanter y Ying Sun. "Systematic Orbital Geometry-Dependent Variations in Satellite Solar-Induced Fluorescence (SIF) Retrievals". Remote Sensing 12, n.º 15 (22 de julio de 2020): 2346. http://dx.doi.org/10.3390/rs12152346.
Texto completoDoughty, Russell, Thomas P. Kurosu, Nicholas Parazoo, Philipp Köhler, Yujie Wang, Ying Sun y Christian Frankenberg. "Global GOSAT, OCO-2, and OCO-3 solar-induced chlorophyll fluorescence datasets". Earth System Science Data 14, n.º 4 (5 de abril de 2022): 1513–29. http://dx.doi.org/10.5194/essd-14-1513-2022.
Texto completoZhang, Yao, Joanna Joiner, Seyed Hamed Alemohammad, Sha Zhou y Pierre Gentine. "A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks". Biogeosciences 15, n.º 19 (2 de octubre de 2018): 5779–800. http://dx.doi.org/10.5194/bg-15-5779-2018.
Texto completoDu, Shanshan, Liangyun Liu, Xinjie Liu, Jian Guo, Jiaochan Hu, Shaoqiang Wang y Yongguang Zhang. "SIFSpec: Measuring Solar-Induced Chlorophyll Fluorescence Observations for Remote Sensing of Photosynthesis". Sensors 19, n.º 13 (8 de julio de 2019): 3009. http://dx.doi.org/10.3390/s19133009.
Texto completoZhang, Lifu, Na Qiao, Changping Huang y Siheng Wang. "Monitoring Drought Effects on Vegetation Productivity Using Satellite Solar-Induced Chlorophyll Fluorescence". Remote Sensing 11, n.º 4 (13 de febrero de 2019): 378. http://dx.doi.org/10.3390/rs11040378.
Texto completoXu, Shan, Zhigang Liu, Shuai Han, Zhuang Chen, Xue He, Huarong Zhao y Sanxue Ren. "Exploring the Sensitivity of Solar-Induced Chlorophyll Fluorescence at Different Wavelengths in Response to Drought". Remote Sensing 15, n.º 4 (16 de febrero de 2023): 1077. http://dx.doi.org/10.3390/rs15041077.
Texto completoHe, Katherine, Wenhong Li y Ruoying He. "Variability of Remotely Sensed Solar-Induced Chlorophyll Fluorescence in Relation to Climate Indices". Environments 9, n.º 9 (19 de septiembre de 2022): 121. http://dx.doi.org/10.3390/environments9090121.
Texto completoPaynter, Ian, Bruce Cook, Lawrence Corp, Jyoteshwar Nagol y Joel McCorkel. "Characterization of FIREFLY, an Imaging Spectrometer Designed for Remote Sensing of Solar Induced Fluorescence". Sensors 20, n.º 17 (19 de agosto de 2020): 4682. http://dx.doi.org/10.3390/s20174682.
Texto completoTesis sobre el tema "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.
Texto completoCESANA, 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.
Texto completoThe 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 y 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.
Texto completoKhosravi, Narges [Verfasser], John P. [Akademischer Betreuer] Burrows, John P. [Gutachter] Burrows y 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.
Texto completoCapítulos de libros sobre el tema "Solar Induced Fluorescence (SIF)"
Zarco-Tejada, Pablo J., John R. Miller y Gina H. Mohammed. "Remote Sensing of Solar-Induced Chlorophyll Fluorescence from Vegetation Hyperspectral Reflectance and Radiative Transfer Simulation". En 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.
Texto completoFrankenberg, C. y J. Berry. "Solar Induced Chlorophyll Fluorescence: Origins, Relation to Photosynthesis and Retrieval". En Comprehensive Remote Sensing, 143–62. Elsevier, 2018. http://dx.doi.org/10.1016/b978-0-12-409548-9.10632-3.
Texto completoActas de conferencias sobre el tema "Solar Induced Fluorescence (SIF)"
Fan, Joshua, Di Chen, Jiaming Wen, Ying Sun y Carla Gomes. "Monitoring Vegetation From Space at Extremely Fine Resolutions via Coarsely-Supervised Smooth U-Net". En 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.
Texto completoRoscher, U., K. Acebron, J. Bendig, J. Kramer, V. Krieger, J. Quiros-Vargas, B. Siegmann y 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". En IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9554870.
Texto completoSmorenburg, 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". En International Symposium on Remote Sensing, editado por Manfred Owe y Guido D'Urso. SPIE, 2002. http://dx.doi.org/10.1117/12.454193.
Texto completoGomezChova, L. "Solar induced fluorescence measurements using a field spectroradiometer". En EARTH OBSERVATION FOR VEGETATION MONITORING AND WATER MANAGEMENT. AIP, 2006. http://dx.doi.org/10.1063/1.2349354.
Texto completoQiu, Ruonan, Ge Han, Xin Ma y Wei Gong. "Solar-Induced Chlorophyll Fluorescence is Very Sensitive to Drought". En IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9553404.
Texto completoBarducci, A., D. Guzzi, C. Lastri, P. Marcoionni, V. Nardino, I. Pippi, V. Raimondi y P. Sandri. "High spectral resolution imager for solar induced fluorescence observation". En SPIE Remote Sensing. SPIE, 2011. http://dx.doi.org/10.1117/12.898225.
Texto completoShunshi Hu, Lifu Zhang y Qingxi Tong. "Estimation of Solar Induced Chlorophyll Fluorescence from EO-1 Hyperion". En 2012 Second International Workshop on Earth Observation and Remote Sensing Applications (EORSA). IEEE, 2012. http://dx.doi.org/10.1109/eorsa.2012.6261191.
Texto completoMiddletona, E. M., L. A. Corp y P. K. E. Campbell. "Canopy level solar induced fluorescence for vegetation in controlled experiments". En 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4423661.
Texto completoPagán, Brianna, Brecht Martens, Wouter Maes y Diego Miralles. "Satellite observed solar induced fluorescence to monitor global plant stress". En First International Electronic Conference on the Hydrological Cycle. Basel, Switzerland: MDPI, 2017. http://dx.doi.org/10.3390/chycle-2017-04874.
Texto completoLi, Rong y Feng Zhao. "Accuracy assessment on reconstruction algorithms of solar-induced Fluorescence Spectrum". En 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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