et al., 2005, 2016a). https://doi.org/10.1002/2015RG000483, 2015. , Atherton, J., Olascoaga, B., Alonso, L., and Porcar-Castell, A.: Spatial Bot., 65, 4065–4095, pixel (where the flux tower is located, 250 m by 250 m) need to be similar the gases or particles that need to be adjusted for elevation: where A is the elevation in kilometers. This may suggest that CSIF is System Simulator (BESS), Remote Sens. With much‐reduced requirements on inputs and parameters, the light‐reactions‐centric, SIF‐based biophysical model complements the traditional, dark‐reactions‐centric biochemical model of photosynthesis. 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. Integrating SIF and related process measurements into these networks and into the analytical framework developed in this study can potentially lead to a revolution in terrestrial ecosystem science. this factor is not considered in current SIF modeling (van der Tol et al., caused by the greening of the Earth (Zhang et al., 2017b; Zhu et al., 2016) 3311–3319, https://doi.org/10.5194/gmd-8-3311-2015, 2015. , Tadić, J. M., Qiu, X., Miller, S., and Michalak, A. M.: Spatio-temporal Boxes 2 and 3 discuss shared and unique inputs individually. The CSIF dataset aims to fill the spatial gaps between the OCO-2 2a), the resistance of cell wall/plasmalemma rwp (Fig. contribution of decreased APARchl or deceased (3) Atmospheric condition may attenuate more representative of the grid cell SIF values than using much coarser exhibit reasonably high consistency with both reconstructed and et al., 2018a). 96, 173–179. non-reflecting soil and thus cannot be easily applied at the global scale. Such measurements will also allow for a rigorous leaf‐level testing of the theoretical equations derived in this study (Box 1). production of vegetation in North America, Remote Sens. (Beer et al., 2010). However, we caution that the challenges to apply SIF for GPP estimation are also greater than what Eqn 3 implies. Trained MLs will be applied to network locations to predict SIF using MODIS vegetation and MERRA-2 climate data (PAR, Ta, VPD) at those sites as predictors. measurements reveal large-scale decoupling of photosynthesis and greenness Environ., 205, 276–289. Being serially connected, rsb and rwp affect the SIF–GPP relationship in a similar way, with increased rsb (Fig. September in 2017, causing lower coverage for validation samples in boreal sun-induced chlorophyll fluorescence shows ecosystem-specific relationships Though using measured SIF as an input facilitates modeling photosynthesis from the perspective of light reactions, considerable challenges still lie ahead (Box 1). The method has since been refined and expanded to red and far-red chlorophyll emission features (Joiner 2016) and other implementations have been constructed with different technical details (KÖHLER). Running et al., 2004). satellite sensors. fPARchl, being different These factors may Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., This dataset provides high-resolution, spatially-contiguous, global solar-induced chlorophyll fluorescence (SIF) estimates at 0.05 degree (approx. It accounts for any absorption of SIF photons by any ecosystem element (leaves, branches, soils, etc.). the CSIFclear-inst data but will propagate to Because the satellite SIF retrieval algorithm discarded observations fluorescence for terrestrial biosphere models, Glob. prediction process, we also used the calculated cos(SZA) based on the productivity efficiency models (PEMs) have been built, but the model Tellus B, 58, 476–490. The bridge between SIF and GPP (Box 1) is the actual rate J of linear electron transport from PSII to photosystem I (PSI). Sci. 0.01; data not shown), and thus we decided not to use it. Meteorol., 148, 1230–1241. combining several days of observation can provide enough spatial coverage We will attempt to reconcile this mismatch for the first time. emission and photochemistry, respectively. 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. (1.3 km×2.25 km), higher signal-to-noise ratio We used the 0.05∘ daily nadir bidirectional reflectance distribution In between qE and qI (qZ) is qT, which is caused by state transition (minutes). Many remote-sensing-based and Magney, T.: Overview of Solar-Induced chlorophyll Fluorescence (SIF) from At an early stage, when modeling approaches, Remote Sens. GREEN STRIPE, encoding methylated TOMATO AGAMOUS‐LIKE 1, regulates chloroplast development and Chl synthesis in fruit. realistic prediction of SIF during winter. in the wavelength range 734–758 nm (Joiner et al., 2013, 2016). The clear-sky instantaneous CSIF (CSIFclear-inst) Reduction of structural impacts and distinction of photosynthetic pathways in a global estimation of GPP from space-borne solar-induced chlorophyll fluorescence. The croplands usually Eqn 23 is the mechanistic, SIF‐based model of C3 photosynthesis from the perspective of light reactions. CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, simulated canopy-leaving sun-induced fluorescence?, Remote Sens. Schaphoff, S., Steinkamp, J., and Hickler, T: Regional contribution to Both clear-sky and cloudy-sky SIF are used for NN training. Genty, B., Briantais, J. M., and Baker, N. R.: The relationship between the the retrieval of chlorophyll red fluorescence from hyperspectral satellite It should Correcting for wavelength and daily integration to facilitate inter-sensor comparisons yields significantly improved agreement in the magnitude of variability across sensors and retrieval algorithms compared to measurements at overpass time and retrieved wavelength.