Assessment of the dynamics of terrestrial vegetation using satellite observations of greenness and sun-induced chlorophyll fluorescence

Photosynthesis is one of the most fundamental processes on Earth fuelling life by providing food and energy. Moreover, terrestrial vegetation is a key element in the climate system as it importantly affects exchange processes of carbon, water and energy between the land surface and the atmosphere. In times of a changing climate there is urgent need for detailed knowledge on the factors driving plant activity and for reliable observational systems of the terrestrial vegetation. Satellite remote sensing is the only means to obtain measurements with global coverage, including remote and inaccessible regions, in a spatially and temporally continuous manner. This thesis presents an assess- ment of our current observational capabilities of vegetation dynamics from space. Three complementary approaches of spaceborne ecosystem monitoring are inter-compared: 1) Spectral measurements of the land surface reflectance in the optical range give an indica- tion of the amount of green biomass (as an integrative signal of leaf quantity and quality) and hence of the potential to perform photosynthesis. 2) In the red and far-red spectral regions, satellite instruments register a very small additive signal to the reflected radiance which originates from photosynthetically active chlorophyll pigments, termed sun-induced chlorophyll fluorescence (SIF). 3) Carbon fluxes measured in-situ are upscaled to a global data set of model gross photosynthetic carbon uptake (known as GPP - gross primary production) using empirical relationships with remotely sensed land surface and environ- mental variables. Three case studies focus i) on the spring phenology in boreal forests, ii) on the peak growing season in circumpolar treeless regions, and iii) on phenological changes in ecosystems with varying abundances of trees globally in times of fluctuations in soil moisture availability. The results of all three case studies highlight the intrinsic differences between greenness on the one hand and photosynthetic activity on the other hand. Specifically – for the first time on synoptic scales – a decoupling of photosynthesis (as indicated by SIF and model GPP) and greenness (approximated by various indices derived from spectral reflectance measurements) could be observed in evergreen needleleaf forests during spring recovery. Similarly, a temporal mismatch occurs in northern hemi- sphere forests during the growing season. There, changes in incoming light co-vary with soil moisture and immediately affect photosynthetic performance but barely greenness. Moreover, it has emerged that the timing of peak photosynthesis and peak greenness are asynchronous in tundra areas, which is indicative of differing dynamics. Conversely, there is high consistency between the three approaches regarding the length of growing season in deciduous forests and moisture-related phenological shifts in non-forested ecosystems. The work in this thesis demonstrates that SIF represents an asset for the monitoring of the dynamics of photosynthesis and carbon uptake compared to greenness-based ap- proaches. There are further indications of SIF to track changes in photosynthetic yields. However, despite these promising results for the accurate tracking of photosynthesis from space, further research is required to provide higher resolution data sets with clearer sig- nals. Further, ground-based validation efforts are necessary to improve our mechanistic understanding of physiological and radiative transfer processes controlling the SIF signal

Overview of Global Monitoring of Terrestrial Chlorophyll Fluorescence from Space

Despite the critical importance of photosynthesis for the Earth system, understanding how it is influenced by factors such as climate variability, disturbance history, and water or nutrient availability remains a challenge because of the complex interactions and the lack of GPP measurements at various temporal and spatial scales. Space observations of the sun-induced chlorophyll fluorescence (SIF) electromagnetic signal emitted by plants in the 650-850nm spectral range hold the promise of providing a new view of vegetation photosynthesis on a global basis. Global retrievals of SIF from space have recently been achieved from a number of spaceborne spectrometers originally intended for atmospheric research. Despite not having been designed for land applications, such instruments have turned out to provide the necessary spectral and radiometric sensitivity for SIF retrieval from space. The first global measurements of SIF were achieved in 2011 from spectra acquired by the Japanese GOSAT mission launched in 2009. The retrieval takes advantage of the high spectral resolution provided by GOSATs Fourier Transform Spectrometer (FTS) which allows the evaluation of the in-filling of solar Fraunhofer lines by SIF. Unfortunately, GOSAT only provides a sparse spatial sampling with individual soundings separated by several hundred kilometers. Complementary, the Global Ozone Monitoring Experiment-2 (GOME-2) instruments onboard MetOp-A and MetOp-B enable SIF retrievals since 2007 with a continuous and global spatial coverage. GOME-2 measures in the red and near-infrared (NIR) spectral regions with a spectral resolution of 0.5 nm and a pixel size of up to 40x40 km2. Most recently, another global and spatially continuous data set of SIF retrievals at 740 nm spanning the 2003-2012 time frame has been produced from ENVISATSCIAMACHY. This observational scenario has been completed by the first fluorescence data from the NASA-JPL OCO-2 mission (launched in July 2014) and the upcoming Copernicus' Sentinel 5-Precursor to be launched in early 2016. OCO-2 and TROPOMI offer the possibility of monitoring SIF globally with a 100-fold improvement in spatial and temporal resolution with respect to the current measurements from the GOSAT, GOME-2 and SCIAMACHY missions. In this contribution, we will provide an overview of existing global SIF data sets derived from space-based atmospheric spectrometers and will demonstrate the potential of such data to improve our knowledge of vegetation photosynthesis and gross primary production at the synoptic scale. We will show examples of ongoing research exploiting SIF data for an improved monitoring of photosynthetic activity in different ecosystems, including large crop belts worldwide, the Amazon rainforest and boreal evergreen forests

A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity

Sun-induced chlorophyll fluorescence (SIF) retrieved from satellite spectrometers can be a highly valuable proxy for photosynthesis. The SIF signal is very small and notoriously difficult to measure, requiring sub-nanometre spectral-resolution measurements, which to date are only available from atmospheric spectrometers sampling at low spatial resolution. For example, the widely used SIF dataset derived from the GOME-2 mission is typically provided in 0.5∘ composites. This paper presents a new SIF dataset based on GOME-2 satellite observations with an enhanced spatial resolution of 0.05∘ and an 8 d time step covering the period 2007–2018. It leverages on a proven methodology that relies on using a light-use efficiency (LUE) modelling approach to establish a semi-empirical relationship between SIF and various explanatory variables derived from remote sensing at higher spatial resolution. An optimal set of explanatory variables is selected based on an independent validation with OCO-2 SIF observations, which are only sparsely available but have a high accuracy and spatial resolution. After bias correction, the resulting downscaled SIF data show high spatio-temporal agreement with the first SIF retrievals from the new TROPOMI mission, opening the path towards establishing a surrogate archive for this promising new dataset. We foresee this new SIF dataset becoming a valuable asset for Earth system science in general and for monitoring vegetation productivity in particular. The dataset is available at https://doi.org/10.2905/21935FFC-B797-4BEE-94DA-8FEC85B3F9E1 (Duveiller et al., 2019)

The contemporary "Trojan Horse"

Pathogens frequently associated with multi-drug resistant (MDR) phenotypes, including extended-spectrum beta-lactamase (ESBL)-producing Enterobacteriaceae (ESBL-E) and Acinetobacter baumannii isolated from horses admitted to horse clinics, pose a risk for animal patients and personnel in horse clinics. To estimate current rates of colonization, a total of 341 equine patients were screened for carriage of zoonotic indicator pathogens at hospital admission. Horses showing clinical signs associated with colic (n = 233) or open wounds (n = 108) were selected for microbiological examination of nostril swabs, faecal samples and wound swabs taken from the open wound group. The results showed alarming carriage rates of Gram-negative MDR pathogens in equine patients: 10.7% (34 of 318) of validated faecal specimens were positive for ESBL-E (94%: ESBL-producing Escherichia coli), with recorded rates of 10.5% for the colic and 11% for the open wound group. 92.7% of the ESBL-producing E. coli were phenotypically resistant to three or more classes of antimicrobials. A. baumannii was rarely detected (0.9%), and all faecal samples investigated were negative for Salmonella, both directly and after two enrichment steps. Screening results for the equine nostril swabs showed detection rates for ESBL-E of 3.4% among colic patients and 0.9% in the open wound group, with an average rate of 2.6% (9/340) for both indications. For all 41 ESBL-producing E. coli isolated, a broad heterogeneity was revealed using pulsed-field gel electrophoresis (PFGE) patterns and whole genome sequencing (WGS) -analysis. However, a predominance of sequence type complex (STC)10 and STC1250 was observed, including several novel STs. The most common genes associated with ESBL-production were identified as blaCTX-M-1 (31/41; 75.6%) and blaSHV-12 (24.4%). The results of this study reveal a disturbingly large fraction of multi-drug resistant and ESBL-producing E. coli among equine patients, posing a clear threat to established hygiene management systems and work-place safety of veterinary staff in horse clinics

Environment-sensitivity functions for gross primary productivity in light use efficiency models

The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products.publishersversionpublishe

A unified vegetation index for quantifying the terrestrial biosphere

Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross primary productivity, and sun-induced chlorophyll fluorescence. Results suggest that the statistical approach maximally exploits the spectral information and addresses long-standing problems in satellite Earth Observation of the terrestrial biosphere. The nonlinear NDVI will allow more accurate measures of terrestrial carbon source/sink dynamics and potentials for stabilizing atmospheric CO2 and mitigating global climate change

A unified vegetation index for quantifying the terrestrial biosphere

[EN] Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross primary productivity, and sun-induced chlorophyll fluorescence. Results suggest that the statistical approach maximally exploits the spectral information and addresses long-standing problems in satellite Earth Observation of the terrestrial biosphere. The nonlinear NDVI will allow more accurate measures of terrestrial carbon source/sink dynamics and potentials for stabilizing atmospheric CO2 and mitigating global climate change.G.C.-V. was supported by the European Research Council (ERC) under the ERC Consolidator Grant 2014 project SEDAL (647423). M.C.-T. and F.J.G.-H. were supported by the EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA-SAF). SR research was financially supported by the NASA Earth Observing System MODIS project (grant NNX08AG87A). J.A.G. acknowledges the support of NASA ABoVE award number NNX15AT78A. S.W. acknowledges funding from the Emmy Noether Programme (GlobFluo project) of the German Research Foundation (GU 1276/1-1) as well as funding from the European Union's Horizon 2020 research and innovation program under grant agreement 776186 (CHE project) and agreement 776810 (VERIFY project).Camps-Valls, G.; Campos-Taberner, M.; Moreno-Martínez, Á.; Walther, S.; Duveiller, G.; Cescatti, A.; Mahecha, MD.... (2021). A unified vegetation index for quantifying the terrestrial biosphere. Science Advances. 7(9):1-11. https://doi.org/10.1126/sciadv.abc74471117

Scaling carbon fluxes from eddy covariance sites to globe: Synthesis and evaluation of the FLUXCOM approach

FLUXNET assembles globally-distributed eddy covariance-based estimates of carbon fluxes between the biosphere and the atmosphere. Since eddy covariance flux towers have a relatively small footprint and are distributed unevenly across the world, upscaling the observations is necessary in order to obtain global-scale estimates of biosphere-atmosphere exchange from the flux tower network. Based on cross-consistency checks with atmospheric inversions, sun-induced fluorescence (SIF) and dynamic global vegetation models (DGVM), we provide here a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods, forcing datasets, and sets of predictor variables were employed. Spatial patterns of mean GPP are consistent among FLUXCOM and DGVM ensembles (R2 > 0.94 at 1° spatial resolution) while the majority of DGVMs are outside the FLUXCOM range for 70 % of the land surface. Global mean GPP magnitudes for 2008–2010 from FLUXCOM members vary within 106 and 130 PgC yr−1 with the largest uncertainty in the tropics. Seasonal variations of independent SIF estimates agree better with FLUXCOM GPP (mean global pixel-wise R2 ~ 0.75) than with GPP from DGVMs (mean global pixel wise R2 ~ 0.6). Seasonal variations of FLUXCOM NEE show good consistency with atmospheric inversion-based net land carbon fluxes, particularly for temperate and boreal regions (R2 > 0.92). Interannual variability of global NEE in FLUXCOM is underestimated compared to inversions and DGVMs. The FLUXCOM version which uses also meteorological inputs shows a strong co-variation of interannual patterns with inversions (R2 = 0.88 for 2001–2010). Mean regional NEE from FLUXCOM shows larger uptake than inversion and DGVM-based estimates, particularly in the tropics with discrepancies of up to several hundred gC m2 yr−1. These discrepancies can only partly be reconciled by carbon loss pathways that are implicit in inversions but not captured by the flux tower measurements such as carbon emissions from fires and water bodies. We hypothesize that a combination of systematic biases in the underlying eddy covariance data, in particular in tall tropical forests, and a lack of site-history effects on NEE in FLUXCOM are likely responsible for the too strong tropical carbon sink estimated by FLUXCOM. Furthermore, as FLUXCOM does not account for CO2 fertilization effects carbon flux trends are not realistic. Overall, current FLUXCOM estimates of mean annual and seasonal cycles of GPP as well as seasonal NEE variations provide useful constraints of global carbon cycling, while interannual variability patterns from FLUXCOM are valuable but require cautious interpretation. Exploring the diversity of Earth Observation data and of machine learning concepts along with improved quality and quantity of flux tower measurements will facilitate further improvements of the FLUXCOM approach overall

The consolidated European synthesis of CO2 emissions and removals for the European Union and United Kingdom:1990-2020

Quantification of land surface-atmosphere fluxes of carbon dioxide (CO2) and their trends and uncertainties is essential for monitoring progress of the EU27+UK bloc as it strives to meet ambitious targets determined by both international agreements and internal regulation. This study provides a consolidated synthesis of fossil sources (CO2 fossil) and natural (including formally managed ecosystems) sources and sinks over land (CO2 land) using bottom-up (BU) and top-down (TD) approaches for the European Union and United Kingdom (EU27+UK), updating earlier syntheses (Petrescu et al., 2020, 2021). Given the wide scope of the work and the variety of approaches involved, this study aims to answer essential questions identified in the previous syntheses and understand the differences between datasets, particularly for poorly characterized fluxes from managed and unmanaged ecosystems. The work integrates updated emission inventory data, process-based model results, data-driven categorical model results, and inverse modeling estimates, extending the previous period 1990-2018 to the year 2020 to the extent possible. BU and TD products are compared with the European national greenhouse gas inventory (NGHGI) reported by parties including the year 2019 under the United Nations Framework Convention on Climate Change (UNFCCC). The uncertainties of the EU27+UK NGHGI were evaluated using the standard deviation reported by the EU member states following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), originate from within-model uncertainty related to parameterization as well as structural differences between models. By comparing the NGHGI with other approaches, key sources of differences between estimates arise primarily in activities. System boundaries and emission categories create differences in CO2 fossil datasets, while different land use definitions for reporting emissions from land use, land use change, and forestry (LULUCF) activities result in differences for CO2 land. The latter has important consequences for atmospheric inversions, leading to inversions reporting stronger sinks in vegetation and soils than are reported by the NGHGI. For CO2 fossil emissions, after harmonizing estimates based on common activities and selecting the most recent year available for all datasets, the UNFCCC NGHGI for the EU27+UK accounts for 926g±g13gTggCgyr-1, while eight other BU sources report a mean value of 948 [937,961]gTggCgyr-1 (25th, 75th percentiles). The sole top-down inversion of fossil emissions currently available accounts for 875gTggC in this same year, a value outside the uncertainty of both the NGHGI and bottom-up ensemble estimates and for which uncertainty estimates are not currently available. For the net CO2 land fluxes, during the most recent 5-year period including the NGHGI estimates, the NGHGI accounted for -91g±g32gTggCgyr-1, while six other BU approaches reported a mean sink of -62 [-117,-49]gTggCgyr-1, and a 15-member ensemble of dynamic global vegetation models (DGVMs) reported -69 [-152,-5]gTggCgyr-1. The 5-year mean of three TD regional ensembles combined with one non-ensemble inversion of -73gTggCgyr-1 has a slightly smaller spread (0th-100th percentiles of [-135,+45]gTggCgyr-1), and it was calculated after removing net land-atmosphere CO2 fluxes caused by lateral transport of carbon (crop trade, wood trade, river transport, and net uptake from inland water bodies), resulting in increased agreement with the NGHGI and bottom-up approaches. Results at the category level (Forest Land, Cropland, Grassland) generally show good agreement between the NGHGI and category-specific models, but results for DGVMs are mixed. Overall, for both CO2 fossil and net CO2 land fluxes, we find that current independent approaches are consistent with the NGHGI at the scale of the EU27+UK. We conclude that CO2 emissions from fossil sources have decreased over the past 30 years in the EU27+UK, while land fluxes are relatively stable: positive or negative trends larger (smaller) than 0.07 (-0.61)gTggCgyr-2 can be ruled out for the NGHGI. In addition, a gap on the order of 1000gTggCgyr-1 between CO2 fossil emissions and net CO2 uptake by the land exists regardless of the type of approach (NGHGI, TD, BU), falling well outside all available estimates of uncertainties. However, uncertainties in top-down approaches to estimate CO2 fossil emissions remain uncharacterized and are likely substantial, in addition to known uncertainties in top-down estimates of the land fluxes. The data used to plot the figures are available at 10.5281/zenodo.8148461 (McGrath et al., 2023).</p