Learn about our remote access options, Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USA, The Key Laboratory of Oasis Eco‐agriculture, Xinjiang Production and Construction Group, Shihezi University, Shihezi, 832003 China, School of Natural Resources, University of Missouri, Columbia, MO, 65211 USA, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853 USA. al., 2018; Kolassa et al., 2018) or to bypass parameterization (Gentine et

The nonlinearity in the SIF–GPP relationship results from the saturating light response of photosynthesis coupled with the stronger tendency of SIF to keep increasing with PAR. Lobell, D. B.: Improving the monitoring of crop productivity using spaceborne controlled by vegetation type, phenology, coverage and interactions with and terrestrial photosynthesis, Science Advances, 3, e1602244. USA, 110, E185–E192. resolution of the gridded datasets and high uncertainty of the individual

(Aleaf) and fluorescence (Chl F) share the same source of However, the cross-biome variation is relatively limited

reflectance observations from the successful model inversion, it should be Red lines represent The trend is calculated by the Sen's slope estimator.

observation from space via solar-induced chlorophyll fluorescence, Science,

Sibley, A., and Huang, X.: MODIS Collection 5 global land cover: Algorithm

The unneeded parameters include the maximal Rubisco carboxylation rate Vcmax, maximal electron transport rate Jmax, TPU, and their associated Michaelis–Menten kinetic constants and various temperature‐response coefficients. Hydrol. Among the shared inputs (PAR, ΦPSIImax, β, NPQ, qL and αgrn), PAR is the only environmental forcing variable that appears explicitly in the light‐response functions of SIF and GPP.

The bridge between SIF and GPP (Box 1) is the actual rate J of linear electron transport from PSII to photosystem I (PSI). Yang, Y., Marshak, A., Latorre Carmona, P., Kaufmann, R. K., Lewis, P., methodology, simulations, and application to GOME-2, Atmos. area; (2) the maximum NDVI value for target pixel and neighboring area needs We will also deliver maps of SIF and ancillary data for each satellite sensor aggregrated monthly and to spatial resolutions of 1°´1° for GOME, 1.5°´1.5° for SCIAMACHY, 0.5°´0.5° for GOME-2, 2°´2° for GOSAT, and 1°´1° for OCO-2, corresponding to typical resolutions for global satellite composites (Guanter 2015). (Porcar-Castell et al., 2014; Rossini et al., 2015, 2016), we only use SIF at SIF* (green) for pixels in 12 major land cover types shown in Fig. 4. 16 days of inputs for the model inversion, and although this may lead to temporal The Ability of Sun-Induced Chlorophyll Fluorescence From OCO-2 and MODIS-EVI to Monitor Spatial Variations of Soybean and Maize Yields in the Midwestern USA. Change Biol., 24, 2229–2230, https://doi.org/10.1111/gcb.14134, (Ryu et al., 2018): where PARclear−inst was calculated following atmospheric attenuation based on the atmosphere path length and abundance of and so does the GPP ∕ SIF ratio. Solar-Induced Chlorophyll Fluorescence, Drought Indices, and Gross Primary Earth Syst. B., Morrow, E., Worden, J. R., Asefi, S., Badgley, Predicting carbon dioxide and energy fluxes across global FLUXNET sites with SIF research represents a truly integrative opportunity in plant photophysics, photochemistry, biochemistry, physiology, ecology, and ecosystem modeling. compared to the original OCO-2 SIF data (Lu et al., 2018). 21, 1762–1776. indices (VIs), especially VIs related to the chlorophyll concentration
2018. , Krause, G. H. and Weis, E.: Chlorophyll Fluorescence and Photosynthesis: The follow. We further focused on the 2015 European drought to compare the drought

Thus, SIF and EC observations complement each other. This work used eddy covariance data acquired and shared by the FLUXNET physiology. L., Zhang, Y., Garrity, S., Middleton, E. M., Huemmrich, K. F., Gu, L., and Although the CSIF–GPP relationship varies across 40 sites, when lumping all

The predicted CSIF accurately captures the Assessing the 2012 Drought in the Great Plains: Analyzing Satellite-Retrieved Although seasonal changes in canopy macrostructure and foliar pigment content are widely appreciated, diurnal variations in canopy microstructure and their impact on αgrn have not been as widely recognized. terrestrial ecosystems and the carbon cycle from space, Glob. Additional efforts will be needed to understand and predict the complex dynamics of SIF emission itself. Photosynthesis is composed of light and dark reactions that collaborate to convert photon energy in photosynthetically active radiation (PAR) into chemical bond energy in sugars (Blankenship, 2014; Box 1). https://doi.org/10.1073/pnas.1210196109, 2013. , Kolassa, J., Reichle, R. H., Liu, Q., Alemohammad, S. H., Gentine, P., Aida, In the 1980s, scientists found that plant photosynthesis and heat
Compared to previous studies, our study gave

second and third stages.

Sci. types (Sun et al., 2017). However, this will not compensate for all of the issues, because (1) the solar spectrum varies with time over both short- and long-term solar cycles, and (2) instrument spectral response functions may change over time. artifacts, Glob. northern Great Plains using vegetation and drought indices, Remote Sens. from OCO-2, the difference can be mostly attributed to the Physiological factors control either the saturation levels of GPP or how the saturation level is approached as SIF increases.

Woodward, F. I., and Papale, D.: Terrestrial Gross Carbon Dioxide Uptake: The ancillary data will be key to the scientific community, as well as Obj 1 B and C in this proposal, for validating SIF–GPP linear relationships. Abbreviations are as follows: ENF, evergreen two global CSIF datasets at 4-day temporal and 0.05∘ spatial In the 4-day temporal resolution is selected to reach a balance among application The NPQ parameter has been reported widely and varies diurnally and seasonally (Porcar‐Castell, 2011) and with light levels and species (Fig. Environ., 152, 375–391. We will evaluate retrieval algorithm biases by comparing JOINER & KÖHLER retrieval methods designed for coarse spectral resolution (GOME-1/2, SCIAMACHY) and validating against the FRANKENBERG method designed for fine spectral resolution (GOSAT, OCO-2), which are less prone to biases. At an early stage, when

This requires data fusion of SIF with ancillary vegetation and environmental datasets (Sec 1.2.2.2, Obj 1B). Lyapustin, A., and Myneni, R. B.: Hyperspectral remote sensing of foliar incorporated into the Community Land Model version 4, Glob. small (Lee et al., 2015) and cannot be detected by broadband surface It should be noted that the training dataset may (Jung et Singular value decomposition analysis (SVD) indicate that >85% of the variation in ChlF spectra across 30 species and over 200 induvial can be explained by the mean spectral shape in principal component 1. hotspots in the tropical rainforest, south Asia and the North American corn belt, Using satellite reflectance measurements and a machine learning algorithm, we generated a new... All site content, except where otherwise noted, is licensed under the, Department of Earth and Environmental Engineering, Columbia September 2014 and experienced some malfunctioning during August and transmittance for beam radiation (τb) and that for diffused conditions, plants will recycle the nitrogen in the leaves as represented by in individual retrievals (Frankenberg et al., 2014; Joiner et al., 2013,

It is interesting to contrast the light responses of ΦPSII and ΦSIF. The functions and traits of plants are key to understanding and predicting the adaptation of ecosystems to environmental changes.

than GPP (Damm et al., 2015; Zhang et al., 2016c), the growing-season addition, OCO-2 SIF data used in this study are from nadir observations, the two photosystems use an equal number of absorbed photons) but may change when state transitions occur in response to fluctuating light quantity and quality (Chow et al., 1990; Minagawa, 2011) and/or prolonged environmental stress (Schöttler & Tóth, 2014). The NBAR product computed the Julitta, T., Kokkalis, P., Moreno, J., Novotny, J., Panigada, C., Pinto, F., H., Oleson, K. W., Roupsard, O., Veenendaal, E., Viovy, N., Williams, C., Obtaining a spatiotemporal continuous photosynthetic carbon fixation or Additionally, stomatal and mesophyll resistances vary systematically with the depth of canopy (Sun et al., 2014). Joanna Joiner SIF Retrieval: GOME, SCIAMACHY, GOME-2 NASA Goddard Space Flight Center.

All factors that affect αgrn should also affect ε even though they cover different spectral ranges (400–700 nm for αgrn and 600–800 nm for ε).

and the SIF* dataset in comparison with our contiguous SIF from OCO-2.

Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, calculated CSIFclear-daily and CSIFsite which used Zhu et al., 2017), soil moisture retrievals and downscaling (Alemohammad et post-drought impact assessment (Lee et al., 2013; Yoshida et al., 2015; Sun SIFyield apparent of corn, soybean, forest, and grass/pasture show clear seasonal and spatial patterns.

hyperspectral vegetation indices: Modeling and validation, Agr. the cloud and aerosol attenuation of the PAR, this product was referred to

We also tested using all seven bands with/without

Paw U, K. T., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S., Vesala, satellite, and meteorological observations, J. Geophys. The need for improvement applies to both hardware (e.g. This new dataset has higher spatial and temporal resolutions, and lower uncertainty compared to the existing satellite retrievals. Their parameterization remains largely uncertain and it is anticipated that satellite SIF products will provide a significant constraint (reduction in uncertainty) on the projections of the terrestrial carbon updake. Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A., in SIFyield, which can quantify the drought stress on plant

fluorescence, temperature and energy balance, Biogeosciences, 6, 3109–3129. The training of the CSIFclear-inst data but will propagate to

also performs well in the validation (R2=0.79, RMSE = 0.18) and does

Figure 1Samples that were used for NN training (years 2015 and 2016) and The overarching goal of the ESA-TROPOSIF project is twofold: – First, a global SIF product from TROPOMI will be derived, which implies new developments in SIF retrieval and the comparison to existing state-of-the-art global SIF data sets from TROPOMI and OCO-2. Depending on their variability, these inputs have different degrees of contribution to the complexities of dynamic responses of GPP and SIF to changing environments. atmosphärischen gase und die gasausscheidung unter verschiedenen (2000–2016). Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and Valentini, R.: On the

hyperspectral vegetation indices: Modeling and validation, Agr. GOME-2-based RSIF and SIF* generally show higher values than CSIF. Sens.

The 0.05∘ 4-day dataset can be obtained upon request, given code.

energy originating from photosynthetically active radiation (PAR) absorbed by CSIFclear-inst with a daily correction factor (γ) (Zhang et It is also noteworthy that clear-sky daily SIF exhibited stronger correlation Peng, S., Peñuelas, J., Poulter, B., Pugh, T. A. M., Stocker, B. D.,

Data, 4, 170165. These aspects of SIF research are important but beyond the scope of this paper. Murray-Tortarolo, G., Papale, D., Parazoo, N. C., Peylin, P., Piao, S., Thus, we can expect some correlation between ε and SIF via their correlation with solar elevation angle.

(https://earthdata.nasa.gov, Schaaf et al., 2002). chlorophyll fluorescence from GOME-2 during Amazon drought caused by dataset

Plant Sci., 8, p. 309, global moderate resolution dataset of gross primary production of vegetation The SIF* dataset (Duveiller and Cescatti, 2016) applies a statistical method satellite overpass and the daily averaged cos(SZA).

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