Thursday, January 9, 2025

About SIF (Solar-Induced Chlorophyll Fluorescence)

"What is SIF?  SIF (Solar induced chlorophyll fluorescence) is an electromagnetic signal emitted by the chlorophyll a of assimilating plants: part of the energy absorbed by chlorophyll a is not used for photosynthesis, but emitted at longer wavelengths as a two-peak spectrum roughly covering the 650–850 nm spectral range." (SENTINEL-5P+ INNOVATION)

650-850nm is the spectral range of SIF.  People collect the data of 687nm, 730-780nm, 760nm, etc.. Some papers mentioned that the emission peaks are 685nm and 740nm. TROPOMI on Sentinel-5P+ collects SIF data emitted from two fitting windows: 743-758 nm window (baseline product) and 735-758 nm.

1. Ground far-red sun-induced chlorophyll fluorescence and vegetation indices in the US Midwestern agroecosystems (https://www.nature.com/articles/s41597-024-03004-w)
This paper should be a good reference of SIF.

2. Phenotyping Plant Responses to Biotic Stress by Chlorophyll Fluorescence Imaging
(https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2019.01135/full)

3. SIF Learn a New Approach to Remote Sensing of Vegetation (NASA, March 3, 2021)
(https://appliedsciences.nasa.gov/our-impact/news/solar-induced-fluorescence-learn-new-approach-remote-sensing-vegetation)

Training:
ARSET - Use of Solar Induced Fluorescence and LIDAR to Assess Vegetation Change and Vulnerability (NASA, March 16, 18, 23, & 25, 2021)
(https://appliedsciences.nasa.gov/get-involved/training/english/arset-use-solar-induced-fluorescence-and-lidar-assess-vegetation)

4. Sentinel-5P+ Innovation SIF (https://eo4society.esa.int/projects/sentinel-5p-innovation-solar-induced-chlorophyll-fluorescence-sif/)

Scientific Papers
(1) The influence of plant water stress on vegetation-atmosphere exchanges: Implications for ozone modelling (Emmerichs T.; Lu Y.-S.; Taraborrelli D., Biogeosciences (2024))
(2) An Operational Downscaling Method of Solar-Induced Chlorophyll Fluorescence (SIF) for Regional Drought Monitoring (Hong Z., Hu Y., Cui C., Yang X., Tao C., Luo W., Zhang W., Li L., Meng L., Agriculture (Switzerland) (2022))
(3) TROPOMI SIF reveals large uncertainty in estimating the end of plant growing season from vegetation indices data in the Tibetan Plateau (Yang J., Xiao X., Doughty R., Zhao M., Zhang Y., Köhler P., Wu X., Frankenberg C., Dong J., Remote Sensing of Environment (2022))

5. Google AI Overview





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