FORSCHUNGSZENTRUM JUELICH GMBH (DE)
In few years from now, ESA’s BIOMASS and FLEX Earth Explorers satellite missions will open a new opportunity to enhance our knowledge of the global carbon cycle. In particular, the scientific exploitation of BIOMASS and FLEX in synergy with the Sentinel satellite series and other existing and future missions (e.g. CMOS, GEDI, NISAR, Tandem-X/L) will provide an unprecedented opportunity to better understand and characterize the different components of the carbon cycle and its dynamics. Preparing for the fast exploitation of this unique and unprecedented observational capacity, ESA has launched the Carbon Science Constellation Initiative. This initiative will be implemented through a cluster of different studies, research activities, campaigns and tool development efforts dedicated to support the scientific community to explore the potential synergies between different Earth Observation approaches and maximize the scientific impact of this unique set of sensors for carbon cycle research.
With the PhotoProxy project, we address relevant open aspects that are related to the quantitative assessment of vegetation photosynthesis and vegetation stress from space. In the past years the fluorescence signal that is emitted from the core of the photosynthetic apparatus during photosynthetic energy conversion, has become the most promising indicator of actual photosynthetic rates. In 2012, the European Space Agency has selected the FLEX satellite mission to become ESA’s 8th Earth Explorer mission (Drusch et al. 2017). FLEX will be the first dedicated fluorescence mission that will provide global maps of both peak of the fluorescence signal on a high spatial resolution and relevant revisiting time.
In addition to fluorescence, which can be measured across various scales ranging from the single leaf to the ecosystem (Rascher et al 2015, Wieneke et al 2018), in recent years, alternative approaches to the remote detection of photosynthetic carbon fluxes (photosynthesis or gross primary productivity, GPP) have been proposed. These approaches include reflectance-based measures by NIRv (Badgely et al. 2017) and CCI (Gamon et al. 2016), which are both related to pigment and structurally-based changes in vegetation [see Fig below as an example for the complementary information content of the different remote sensing measures].
Together, these remote sensing approaches offer a way to revolutionize our assessment of photosynthetic carbon uptake and vegetation health from space. However, major questions remain regarding the exact function of each of these signals and their relationship to each other. There are several indications that fluorescence may be the best remote sensing parameter to constrain predictions of CO₂ uptake rates, but we expect that a combination of the different measures will provide the best estimates of actual vegetation function.
Thus with this activity we are working in an international consortium to address the following objectives:
Related publications
Badgley G, Field C.B. and Berry J.A. (2017) Canopy near-infrared reflectance and terrestrial photosynthesis. Science Advances, 3; e1602244
Drusch M., Moreno J., Del Bello U., Franco R., Goulas Y., Huth A., Kraft S., Middleton E., Miglietta F., Mohammed G., Nedbal L., Rascher U., Schüttemeyer D. & Verhoef W. (2017) The FLuorescence EXplorer mission concept – ESA’s Earth Explorer 8. IEEE Transactions on Geoscience and Remote Sensing, 55, 1273-1284
Gamon J.A., Huemmrich K.F., Wong C.Y.S, Ensminger I., Garrity S., Hollinger D.Y., Noormets A., Peñuelas J. (2016) Photosynthetic phenology of evergreen conifers. Proceedings of the National Academy of Sciences, 113 (46), 13087-13092
Rascher U., Alonso L., Burkart A., Cilia C., Cogliati S., Colombo R., Damm A., Drusch M., Guanter L., Hanus J., Hyvärinen T., Julitta T., Jussila J., Kataja K., Kokkalis P., Kraft S., Kraska T., Matveeva M., Moreno J., Muller O., Panigada C., Pikl M., Pinto F., Prey L., Pude R., Rossini M., Schickling A., Schurr U., Schüttemeyer D., Verrelst J. & Zemek F. (2015) Sun-induced fluorescence – a new probe of photosynthesis: First maps from the imaging spectrometer HyPlant. Global Change Biology, 21, 4673–4684
Wieneke S., Burkart, A., Cendrero-Mateo M. P., Julitta T., Rossini M., Schickling A., Schmidt M., Rascher U. (2018) Linking photosynthesis and sun-induced fluorescence at sub-daily to seasonal scales. Remote sensing of environment, 219, 247 – 258
[Book chapter]: J. Quiros-Vargas, B. Siegmann, A. Damm, R. Wang, J. Gamon, V. Krieger, B.S.D. Sagar, O. Muller, U. Rascher, “Fractal Geometry and the Downscaling of Sun-induced Chlorophyll Fluorescence Imagery” in Encyclopaedia of Mathematical Geosciences, B.S. Daya Sagar, Q. Cheng, J. McKinley, F. Agterberg, Eds. (Springer Nature, 2022)Martini D., Pacheco Labrador J., El-Madany T.S., Sakowska K., van der Tol C., Julitta T., Biriukova K., Rossini M. & Migliavacca M. (2019) – Sun-Induced Fluorescence under a heat wave. Evidence from a tree-grass ecosystem. AGU Fall Meeting 2019, San Francisco: B11Q-2282
Quirós J., Brogi C., Krieger V., Siegmann B., Celesti M., Rossini M., Cogliati S., Weihermüller L., Rascher U. (2020) – Solar Induced Chlorophyll Fluorescence and Vegetation Indices for Heat Stress Assessment in Three Crops at Different Geophysics-Derived Soil Units. AGU Fall Meeting 2020, Virtual, doi: 10.1002/essoar.10504968.1
J. Quiros-Vargas, B. Siegmann, A. Damm, V. Krieger, O. Muller, U. Rascher, “Spatial dependency of Solar-induced Chlorophyll Fluorescence (SIF)-emitting objects in the footprint of a FLuorescence EXplorer (FLEX) pixel: a SIF-downscaling perspective” in Proceedings of the European Geophysical Union (EGU, Vienna, Austria, 2022)
Remote Sensing of Environment (2024)
ISPRS Journal of Photogrammetry and Remote Sensing (2023)
New Phytologist (2023)
Heatwave breaks down the linearity between sun-induced fluorescence and gross primary production
New Phytologist (2022)
Remote Sensing of Environment (2022)
Remote Sensing of Environment (2022)
Remote Sensing of Environment (2022)
Remote Sensing of Environment (2021)
Remote Sensing of Environment (2021)
Estimating near-infrared reflectance of vegetation from hyperspectral data
Remote Sensing of Environment (2021)
Agricultural and Forest Meteorology (2020)
Biogeosciences (2020)
Remote Sensing (2019)