From Canopy‐Leaving to Total Canopy Far‐Red Fluorescence Emission for Remote Sensing of Photosynthesis: First Results From TROPOMI

Zhang, Zhaoying; Chen, Jing M.; Guanter, Luis; He, Li; Zhang, Yongguang

doi:10.1029/2019gl084832

Cited by 78 publications

(45 citation statements)

References 61 publications

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“…In this study, α is set as 1.21 (Figure 1i) for all pixels for simplicity instead of applying biome‐specific α , which avoid the use of land cover map and its associated uncertainty. The monthly GPP SIF‐CS , GPP SIF‐AS , and GPP SIF‐ASE are integrated over 2019 to obtain yearly averaged GPP values, which are further compared to global GPP from the Vegetation Photosynthesis Model (VPM) (Zhang et al, 2017), the Soil Moisture Active Passive (SMAP) mission Level 4 Carbon product (Jones et al, 2017), the FluxSat level 3 product (Joiner et al, 2018), and the Penman‐Monteith‐Leuning (PML) evapotranspiration model (Zhang, 2019; Zhang, Chen, et al, 2019; Zhang, Peña‐Arancibia, et al, 2016). Details about these GPP products can be found in the references cited here.…”

Section: Methodsmentioning

confidence: 99%

Correcting Clear‐Sky Bias in Gross Primary Production Modeling From Satellite Solar‐Induced Chlorophyll Fluorescence Data

Zhang

et al. 2020

JGR Biogeosciences

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Satellite solar-induced chlorophyll fluorescence (SIF) has been demonstrated the potential to monitor photosynthesis, quantified as gross primary production (GPP), for large areas. However, satellite SIF retrievals are only reliable for clear-sky conditions, creating overestimation in estimating GPP for all-sky conditions, called clear-sky bias. Clouds can reduce the total radiation and increase the diffuse radiation, which can have counteracting effects on photosynthesis since plants are more efficient in using diffuse radiation. To mitigate this uncertainty in estimating global GPP from satellite SIF, we propose a clear-sky bias correction approach that considers both the increase in light use efficiency (LUE) and the decrease in photosynthetically active radiation (PAR) under cloudy conditions. An enhancement factor (α) of LUE under cloudy conditions is first parameterized from FLUXNET data set, which is found to converge to 1.21 among all vegetation types. Then, a correction factor of clear-sky bias is developed to account for the variations of LUE under cloudy conditions, which is used to estimate global terrestrial GPP in 2019 with spaceborne SIF retrievals from TROPOspheric Monitoring Instrument (TROPOMI). We estimate global annual terrestrial GPP of 135.47 ± 3.36 and 142.03 ± 3.53 PgC/year, respectively, with and without correcting clear-sky bias. This approach is also able to identify regions with large uncertainties in GPP estimation from satellite SIF data due to the effects of clear-sky bias, such as eastern America, western Europe, and southern China. Our results highlight the importance of considering clear-sky bias in estimating terrestrial photosynthesis from satellite SIF data. Plain Language Summary Photosynthesis is an important process by which plants remove the atmosphere of carbon dioxide (CO 2) and fuel the life on Earth. Satellite solar-induced chlorophyll fluorescence (SIF) shows great potential in monitoring terrestrial photosynthesis. However, satellite SIF retrievals are only reliable for clear-sky days, creating biases in estimating photosynthesis for all-sky condition. Cloud can reduce the total radiation and increase the diffused radiation, which can have counteracting effects on photosynthesis since plants are more efficient in using diffused radiation. Under slightly cloudy conditions, the decrease and increase from these two effects are approximately equal. However, under extremely cloudy conditions, the decreased radiation outweighs the efficiency enhancement, leading to an overall negative effect on photosynthesis. In this study, we propose a practical approach to mitigate this uncertainty in estimating global photosynthesis from SIF. Using the TROPOspheric Monitoring Instrument (TROPOMI) SIF during the year of 2019, we estimate a global photosynthesis for all sky conditions to be 135.47 ± 3.36 PgC/year. We also recognize three regions including eastern America, western Europe, and southern China, where clear-sky bias is clearly corrected with our approach. Mitigating clear-sky...

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Section: Methodsmentioning

confidence: 99%

Correcting Clear‐Sky Bias in Gross Primary Production Modeling From Satellite Solar‐Induced Chlorophyll Fluorescence Data

Zhang

et al. 2020

JGR Biogeosciences

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“…On the contrary, for these configurations with a non-reflecting background spectrum, because there is no more contamination caused by the background reflectance, the estimation accuracy of SIF escape probability was mostly reduced (Figure 6a). However, it should be noted that the above analysis was based on the assumption that G(θ) was fixed to 0.5, as for global-scale remote sensing applications [13,14,25,33], and LAI and CI were set to simulation values (i.e., 'real' value).…”

Section: Influence Of Remotely Sensed I O and Fapar On The Estimationmentioning

confidence: 99%

“…However, whether or not a universal linear relationship exists across biomes at ecosystem-scale is still an open question [6,10]. Usually, GPP is calculated as the difference between NEE (Net Ecosystem Exchange) and ER (Ecosystem Respiration) [11], while the satellite-observed TOC SIF is calculated based on the directional canopy spectrum [12][13][14]. TOC SIF is only a part of the total emitted SIF of vegetation and is influenced by canopy structure and solar-viewing geometry.…”

Section: Introductionmentioning

confidence: 99%

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Simulation-Based Evaluation of the Estimation Methods of Far-Red Solar-Induced Chlorophyll Fluorescence Escape Probability in Discontinuous Forest Canopies

et al. 2020

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The escape probability of Solar-induced chlorophyll fluorescence (SIF) can be remotely estimated using reflectance measurements based on spectral invariants theory. This can then be used to correct the effects of canopy structure on canopy-leaving SIF. However, the feasibility of these estimation methods is untested in heterogeneous vegetation such as the discontinuous forest canopy layer under evaluation here. In this study, the Discrete Anisotropic Radiative Transfer (DART) model is used to simulate canopy-leaving SIF, canopy total emitted SIF, canopy interceptance, and the fraction of absorbed photosynthetically active radiation (fAPAR) in order to evaluate the estimation methods of SIF escape probability in discontinuous forest canopies. Our simulation results show that the normalized difference vegetation index (NDVI) can be used to partly eliminate the effects of background reflectance on the estimation of SIF escape probability in most cases, but fails to produce accurate estimations if the background is partly or totally covered by vegetation. We also found that SIF escape probabilities estimated at a high solar zenith angle have better estimation accuracy than those estimated at a lower solar zenith angle. Our results show that additional errors will be introduced to the estimation of SIF escape probability with the use of satellite products, especially when the product of leaf area index (LAI) and clumping index (CI) was underestimated. In other results, fAPAR has comparable estimation accuracy of SIF escape probability when compared to canopy interceptance. Additionally, fAPAR for the entire canopy has better estimation accuracy of SIF escape probability than fPAR for leaf only in sparse forest canopies. These results help us to better understand the current estimation results of SIF escape probability based on spectral invariants theory, and to improve its estimation accuracy in discontinuous forest canopies.

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“…Several approaches have been proposed and/or implemented to more fully account for sun-satellite geometrical dependencies of satellite SIF measurements (e.g., [25,30,[37][38][39][42][43][44][45]52]). These methods rely on various ancillary data including reflectances along with theoretical and/or machine learning constructs (see e.g., [25] for a review).…”

Section: Introductionmentioning

confidence: 99%

Systematic Orbital Geometry-Dependent Variations in Satellite Solar-Induced Fluorescence (SIF) Retrievals

et al. 2020

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While solar-induced fluorescence (SIF) shows promise as a remotely-sensed measurement directly related to photosynthesis, interpretation and validation of satellite-based SIF retrievals remains a challenge. SIF is influenced by the fraction of absorbed photosynthetically-active radiation at the canopy level that depends upon illumination geometry as well as the escape of SIF through the canopy that depends upon the viewing geometry. Several approaches to estimate the effects of sun-sensor geometry on satellite-based SIF have been proposed, and some have been implemented, most relying upon satellite reflectance measurements and/or other ancillary data sets. These approaches, designed to ultimately estimate intrinsic or physiological components of SIF related to photosynthesis, have not generally been applied globally to satellite measurements. Here, we examine in detail how SIF and related reflectance-based indices from wide swath polar orbiting satellites in low Earth orbit vary systematically due to the host satellite orbital characteristics. We compare SIF and reflectance-based parameters from the Global Ozone Mapping Experiment 2 (GOME-2) on the MetOp-B platform and from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel 5 Precursor satellite with a focus on high northern latitudes in summer where observations at similar geometries and local times occur. We show that GOME-2 and TROPOMI SIF observations agree nearly to within estimated uncertainties when they are compared at similar observing geometries. We show that the cross-track dependence of SIF normalized by PAR and related reflectance-based indices are highly correlated for dense canopies, but diverge substantially as the vegetation within a field-of-view becomes more sparse. This has implications for approaches that utilize reflectance measurements to help account for SIF geometrical dependences in satellite measurements. To further help interpret the GOME-2 and TROPOMI SIF observations, we simulated cross-track dependences of PAR normalized SIF and reflectance-based indices with the one dimensional Soil-Canopy Observation Photosynthesis and Energy fluxes (SCOPE) canopy radiative transfer model at sun–satellite geometries that occur across the wide swaths of these instruments and examine the geometrical dependencies of the various components (e.g., fraction of absorbed PAR, SIF yield, and escape of SIF from the canopy) of the observed SIF signal. The simulations show that most of the cross-track variations in SIF result from the escape of SIF through the scattering canopy and not the illumination.

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From Canopy‐Leaving to Total Canopy Far‐Red Fluorescence Emission for Remote Sensing of Photosynthesis: First Results From TROPOMI

Cited by 78 publications

References 61 publications

Correcting Clear‐Sky Bias in Gross Primary Production Modeling From Satellite Solar‐Induced Chlorophyll Fluorescence Data

Correcting Clear‐Sky Bias in Gross Primary Production Modeling From Satellite Solar‐Induced Chlorophyll Fluorescence Data

Simulation-Based Evaluation of the Estimation Methods of Far-Red Solar-Induced Chlorophyll Fluorescence Escape Probability in Discontinuous Forest Canopies

Systematic Orbital Geometry-Dependent Variations in Satellite Solar-Induced Fluorescence (SIF) Retrievals

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