Sun-induced Fluorescence and Near Infrared Reflectance of vegetation track the seasonal dynamics of gross primary production over Africa

Mengistu, Anteneh Getachew; Tsidu, Gizaw Mengistu; Koren, Gerbrand; Kooreman, Maurits L.; Boersma, K. Folkert; Tagesson, Torbern; Ardö, Jonas; Nouvellon, Yann; Peters, Wouter

doi:10.5194/bg-2020-242

Cited by 8 publications

(7 citation statements)

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“…The comparison of the spatial patterns in these two maps with those of SIF in Fig. 6 confirms the high correlation between SIF and NIRv that has already been reported in recent studies (Badgley et al, 2017;Zeng et al, 2019;Mengistu et al, 2020). In contrast, the dynamic range of NDVI appears to be smaller than that of NIRv and SIF, since several densely vegetated areas around the world reach the highest NDVI values.…”

Section: Evaluation Of Reflectance-based Indicessupporting

confidence: 87%

The TROPOSIF global sun-induced fluorescence dataset from the Sentinel-5P TROPOMI mission

Guanter¹,

Bacour²,

Schneider³

et al. 2021

Preprint

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Abstract. The first satellite-based global retrievals of terrestrial sun-induced chlorophyll fluorescence (SIF) were achieved in 2011. Since then, a number of global SIF datasets with different spectral, spatial and temporal sampling characteristics have become available to the scientific community. These datasets have been useful to monitor the dynamics and productivity of a range of vegetated areas worldwide, but the coarse spatio-temporal sampling and low signal-to-noise ratio of the data hamper their application over small or fragmented ecosystems. The recent advent of the Copernicus Sentinel-5P TROPOMI mission and the high quality of its data products promise to alleviate this situation, as TROPOMI provides daily global measurements at a much denser spatial and temporal sampling than earlier satellite instruments. In this work, we present a global SIF dataset produced from TROPOMI measurements within the TROPOSIF project funded by the European Space Agency. The current version of the TROPOSIF dataset covers the time period between May 2018 and April 2021. Baseline SIF retrievals are derived from the 743–758 nm window. A secondary SIF dataset derived from an extended fitting window (735–758 nm window) is included. This provides an enhanced signal-to-noise ratio at the expense of a higher sensitivity to atmospheric effects. Spectral reflectance spectra at seven 3-nm windows devoid of atmospheric absorption within the 665–785 nm range are also included in the TROPOSIF dataset as an important ancillary variable to be used in combination with SIF. The methodology to derive SIF and ancillary data as well as results from an initial data quality assessment are presented in this work. The TROPOSIF dataset is available through the following Digital Object Identifier (DOI) https://doi.org/10.5270/esa-s5p_innovation-sif-20180501_20210320-v2.1-202104 (Guanter et al., 2021).

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Section: Evaluation Of Reflectance-based Indicessupporting

confidence: 87%

The TROPOSIF global sun-induced fluorescence dataset from the Sentinel-5P TROPOMI mission

Guanter¹,

Bacour²,

Schneider³

et al. 2021

Preprint

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“…This explanation, however, seems less likely given that several site-level studies find good correlation between SIF and GPP in a variety of tropical biomes (e.g., C. Wang et al, 2019;Mengistu et al, 2021). Satellite measurements may not detect all photosynthetic activity in tropical forests, including in the understory, mid-canopy, and the dense canopy.…”

Section: The Tropicsmentioning

confidence: 99%

Solar-induced fluorescence products show variable skill in constraining global patterns in biospheric CO2 fluxes

Zhang

Berry

Shiga

et al. 2022

Preprint

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Solar-induced fluorescence (SIF) shows enormous promise as a proxy for photosynthesis and as a tool for modeling variability in gross primary productivity (GPP) and net biosphere exchange (NBE). In this study, we explore the skill of SIF and other vegetation indicators in predicting variability in global atmospheric CO2 observations, and thus global variability in NBE. We do so using a four-year record of global CO2 observations from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite and using a geostatistical inverse model. We find that existing SIF products closely correlate with space-time variability in atmospheric CO2 observations in the extra-tropics but show weaker explanatory power across the tropics. In the extra-tropics, all SIF products exhibit greater skill in explaining variability in atmospheric CO2 observations compared to an ensemble of process-based CO2 flux models and other vegetation indicators. Furthermore, we find that using SIF as a predictor variable in the geosatistical inverse model shifts the seasonal cycle of estimated NBE and yields an earlier end to the growing season relative to other vegetation indicators. In tropical biomes, by contrast, the seasonal cycles of SIF products and estimated NBE are out of phase, and existing respiration and biomass burning estimates do not reconcile this discrepancy. Overall, our results highlight several advantages and challenges of using SIF products to help predict global variability in GPP and NBE.

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“…The ensemble uses five NEE priors and three fire emission drivers but with a common set of atmospheric constraints and transport model (Schaefer et al, 2008;Bodesheim et al, 2018;van Schaik et al, 2018;Haynes et al, 2019;Koren , 2020). By using a range of priors it covers the uncertainty in the seasonal variation of C fluxes in tropical regions (Saleska et al, 2003;Restrepo-Coupe et al, 2013;Koren et al, 2018;Mengistu et al, 2020). In the remainder of this text the CTE and CT-SAM datasets are collectively referred to as CTE.…”

Section: Wood Cmentioning

confidence: 99%

Parameter uncertainty dominates C cycle forecast errors over most of Brazil for the 21st Century

Smallman¹,

Milodowski²,

Sousa‐Neto³

et al. 2021

Preprint

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Abstract. Identification of terrestrial carbon (C) sources and sinks is critical for understanding the earth system and to mitigate and adapt to climate change results from greenhouse gas emissions. Predicting whether a given location will act as a C source or sink using terrestrial ecosystem models (TEMs) is challenging due to net flux being the difference between far larger, spatially and temporally variable fluxes with large uncertainties. Uncertainty in projections of future dynamics, critical for policy evaluation, has been determined using multi-TEM intercomparisons, for various emissions scenarios. This approach quantifies structural and forcing errors. However, the role of parameter error within models has not been determined. TEMs typically have defined parameters for specific plant functional types generated from the literature. To ascertain the importance of parameter error in forecasts we present a Bayesian analysis that uses data on historical and current C cycling for Brazil to parameterise five TEMs of varied complexity with a retrieval of model error covariance at 1 degree spatial resolution. After evaluation against data from 2001–2017, the parameterised models are simulated to 2100 under four climate change scenarios spanning the likely range of climate projections. Using multiple models, each with per pixel parameter ensembles, we partition forecast uncertainties. Parameter uncertainty dominates across most of Brazil when simulating future stock changes in biomass C and dead organic matter (DOM). Uncertainty of simulated biomass change is most strongly correlated with net primary productivity allocation to wood (NPPwood) and wood mean residence times (MRTwood). Uncertainty of simulated DOM change is most strongly correlated with MRTsoil and NPPwood. Due to the coupling between these variables and C stock dynamics being bi-directional we argue that using repeat estimates of woody biomass will provide a valuable constraint needed to refine predictions of the future carbon cycle. Finally, evaluation of our multi-model analysis shows that wood litter contributes substantially to fire emissions necessitating a greater understanding of wood litter C-cycling than is typically considered in large-scale TEMs.

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Sun-induced Fluorescence and Near Infrared Reflectance of vegetation track the seasonal dynamics of gross primary production over Africa

Cited by 8 publications

References 0 publications

The TROPOSIF global sun-induced fluorescence dataset from the Sentinel-5P TROPOMI mission

The TROPOSIF global sun-induced fluorescence dataset from the Sentinel-5P TROPOMI mission

Solar-induced fluorescence products show variable skill in constraining global patterns in biospheric CO2 fluxes

Parameter uncertainty dominates C cycle forecast errors over most of Brazil for the 21st Century

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