Academic literature on the topic 'Solar Induced Fluorescence (SIF)'

Abstract. Solar-induced chlorophyll fluorescence (SIF) is an indicator of plant photosynthesis which could be detected by satellite. However，some existing algorithms are easily affected by the inaccuracy of satellite data which will causing deviation in the retrieval of SIF. To avoid "outliers" with inaccuracy affecting the retrieval results, a random sample consensus algorithm (RANSAC) was introduced to retrieve SIF in this paper. The results show that the chlorophyll fluorescence value obtained by this method is consistent with the OCO-2 SIF product (R2 = 0.81), and also consistent with the MODIS vegetation index (R2 = 0.87 with NDVI, R2 = 0.85 with EVI). Compared with the existing SIF products (OCO-2 SIF), the SIF retrieved in this paper was better in spatial details and outlier distribution.

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.

Abstract. The retrieval of solar-induced chlorophyll fluorescence (SIF) from space is a relatively new advance in Earth observation science, having only become feasible within the last decade. Interest in SIF data has grown exponentially, and the retrieval of SIF and the provision of SIF data products has become an important and formal component of spaceborne Earth observation missions. Here, we describe the global Level 2 SIF Lite data products for the Greenhouse Gases Observing Satellite (GOSAT), the Orbiting Carbon Observatory-2 (OCO-2), and Orbiting Carbon Observatory-3 (OCO-3) platforms, which are provided for each platform in daily netCDF files (Frankenberg, 2022, https://doi.org/10.22002/D1.8771; OCO-2 Science Team et al., 2020, https://doi.org/10.5067/XO2LBBNPO010; OCO-3 Science Team et al., 2020, https://doi.org/10.5067/NOD1DPPBCXSO). We also outline the methods used to retrieve SIF and estimate uncertainty, describe all the data fields, and provide users with the background information necessary for the proper use and interpretation of the data, such as considerations of retrieval noise, sun sensor geometry, the indirect relationship between SIF and photosynthesis, and differences among the three platforms and their respective data products. OCO-2 and OCO-3 have the highest spatial resolution of spaceborne SIF retrievals to date, and the target and snapshot area mode observation modes of OCO-2 and OCO-3 are unique. These modes provide hundreds to thousands of SIF retrievals at biologically diverse global target sites during a single overpass, and provide an opportunity to better inform our understanding of canopy-scale vegetation SIF emission across biomes.

Abstract. Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) has shown great potential to monitor the photosynthetic activity of terrestrial ecosystems. However, several issues, including low spatial and temporal resolution of the gridded datasets and high uncertainty of the individual retrievals, limit the applications of SIF. In addition, inconsistency in measurement footprints also hinders the direct comparison between gross primary production (GPP) from eddy covariance (EC) flux towers and satellite-retrieved SIF. In this study, by training a neural network (NN) with surface reflectance from the MODerate-resolution Imaging Spectroradiometer (MODIS) and SIF from Orbiting Carbon Observatory-2 (OCO-2), we generated two global spatially contiguous SIF (CSIF) datasets at moderate spatiotemporal (0.05∘ 4-day) resolutions during the MODIS era, one for clear-sky conditions (2000–2017) and the other one in all-sky conditions (2000–2016). The clear-sky instantaneous CSIF (CSIFclear-inst) shows high accuracy against the clear-sky OCO-2 SIF and little bias across biome types. The all-sky daily average CSIF (CSIFall-daily) dataset exhibits strong spatial, seasonal and interannual dynamics that are consistent with daily SIF from OCO-2 and the Global Ozone Monitoring Experiment-2 (GOME-2). An increasing trend (0.39 %) of annual average CSIFall-daily is also found, confirming the greening of Earth in most regions. Since the difference between satellite-observed SIF and CSIF is mostly caused by the environmental down-regulation on SIFyield, the ratio between OCO-2 SIF and CSIFclear-inst can be an effective indicator of drought stress that is more sensitive than the normalized difference vegetation index and enhanced vegetation index. By comparing CSIFall-daily with GPP estimates from 40 EC flux towers across the globe, we find a large cross-site variation (c.v. = 0.36) of the GPP–SIF relationship with the highest regression slopes for evergreen needleleaf forest. However, the cross-biome variation is relatively limited (c.v. = 0.15). These two contiguous SIF datasets and the derived GPP–SIF relationship enable a better understanding of the spatial and temporal variations of the GPP across biomes and climate.

Solar-induced chlorophyll fluorescence (SIF) is regarded as a proxy for photosynthesis in terrestrial vegetation. Tower-based long-term observations of SIF are very important for gaining further insight into the ecosystem-specific seasonal dynamics of photosynthetic activity, including gross primary production (GPP). Here, we present the design and operation of the tower-based automated SIF measurement (SIFSpec) system. This system was developed with the aim of obtaining synchronous SIF observations and flux measurements across different terrestrial ecosystems, as well as to validate the increasing number of satellite SIF products using in situ measurements. Details of the system components, instrument installation, calibration, data collection, and processing are introduced. Atmospheric correction is also included in the data processing chain, which is important, but usually ignored for tower-based SIF measurements. Continuous measurements made across two growing cycles over maize at a Daman (DM) flux site (in Gansu province, China) demonstrate the reliable performance of SIF as an indicator for tracking the diurnal variations in photosynthetically active radiation (PAR) and seasonal variations in GPP. For the O2–A band in particular, a high correlation coefficient value of 0.81 is found between the SIF and seasonal variations of GPP. It is thus concluded that, in coordination with continuous eddy covariance (EC) flux measurements, automated and continuous SIF observations can provide a reliable approach for understanding the photosynthetic activity of the terrestrial ecosystem, and are also able to bridge the link between ground-based optical measurements and airborne or satellite remote sensing data.

Around the world, the increasing drought, which is exacerbated by climate change, has significant impacts on vegetation carbon assimilation. Identifying how short-term climate anomalies influence vegetation productivity in a timely and accurate manner at the satellite scale is crucial to monitoring drought. Satellite solar-induced chlorophyll fluorescence (SIF) has recently been reported as a direct proxy of actual vegetation photosynthesis and has more advantages than traditional vegetation indices (e.g., the Normalized Difference Vegetation Index, NDVI and the Enhanced Vegetation Index, EVI) in monitoring vegetation vitality. This study aims to evaluate the feasibility of SIF in interpreting drought effects on vegetation productivity in Victoria, Australia, where heat stress and drought are often reported. Drought-induced variations in SIF and absorbed photosynthetically active radiation (APAR) estimations based on NDVI and EVI were investigated and validated against results indicated by gross primary production (GPP). We first compared drought responses of GPP and vegetation proxies (SIF and APAR) during the 2009 drought event, considering potential biome-dependency. Results showed that SIF exhibited more consistent declines with GPP losses induced by drought than did APAR estimations during the 2009 drought period in space and time, where APAR had obvious lagged responses compared with SIF, especially in evergreen broadleaf forest land. We then estimated the sensitivities of the aforementioned variables to meteorology anomalies using the ARx model, where memory effects were considered, and compared the correlations of GPP anomaly with the anomalies of vegetation proxies during a relatively long period (2007–2013). Compared with APAR, GPP and SIF are more sensitive to temperature anomalies for the general Victoria region. For crop land, GPP and vegetation proxies showed similar sensitivities to temperature and water availability. For evergreen broadleaf forest land, SIF anomaly was explained better by meteorology anomalies than APAR anomalies. GPP anomaly showed a stronger linear relationship with SIF anomaly than with APAR anomalies, especially for evergreen broadleaf forest land. We showed that SIF might be a promising tool for effectively evaluating short-term drought impacts on vegetation productivity, especially in drought-vulnerable areas, such as Victoria.

Due to the mechanistic coupling between solar-induced chlorophyll fluorescence (SIF) and photosynthesis, SIF has an advantage over greenness-based vegetation indices in detecting drought. Since photosystem I (PSI) contributes very little to red SIF, red SIF is assumed to be more responsive to environmental stress than far-red SIF. However, in addition to affecting photosynthesis, drought also has an impact on vegetation chlorophyll concentration and thus affects the reabsorption process of red SIF. When these responses are entangled, the sensitivity of SIF in the red and far-red regions in response to drought is not yet clear. In this study, we conducted a water stress experiment on maize in the field and measured the upward and downward leaf SIF spectra by a spectrometer assembled with a leaf clip. Simultaneously, leaf-level active fluorescence was measured with a pulse-amplified modulation (PAM) fluorometer. We found that SIF, after normalization by photosynthetically active radiation (PAR) and dark-adapted minimal fluorescence (Fo), is a better estimation of SIF yield. By comparing the wavelength-dependent link between SIF yield and nonphotochemical quenching (NPQ) across the range of 660 to 800 nm, the results show that red SIF and far-red SIF have different sensitivities in response to drought. SIF yield in the far-red region has a strong and stable correlation with NPQ. Drought not only reduces red SIF due to photosynthetic regulation, but it also increases red SIF by reducing chlorophyll content (weakening the reabsorption effect). The co-existence of these two contradictory effects makes the red SIF of leaf level unable to reliably indicate NPQ. In addition, the red:far-red ratio of downward SIF and the ratio between the downward SIF and upward SIF at the red peak can be good indicators of chlorophyll content. These findings can help to interpret SIF variations in remote sensing techniques and fully exploit SIF information in red and far-red regions when monitoring plant water stress.

Global remote sensing of solar-induced fluorescence (SIF), a proxy for plant photosynthetic activity, represents a breakthrough in the systematic observation of global-scale gross primary production and other ecosystem functions. Here, we hypothesize that all earth ecosystem variabilities, including SIF, are affected by climate variations. The main contribution of this study is to apply a global empirical orthogonal function (EOF) analysis of SIF to quantify the relations between the large-scale GPP variability and climate variations. We used 2007–2019 SIF data derived from the Global Ozone Monitoring Experiment-2 (GOME-2) satellite sensor observations and a rotated empirical orthogonal function (EOF) analysis to explore global SIF variability over years and decades. The first leading EOF mode captures the well-known ENSO pattern, with most of the variance over continents in the tropical Pacific and Indian Oceans. The second and third leading EOF modes in SIF variability are significantly related to the NAO and PDO climate indices, respectively. Our analysis also shows that the 2011 La Niña (2015 El Niño) elevated (decreased) global SIF.

Solar induced fluorescence (SIF) is an ecological variable of interest to remote sensing retrievals, as it is directly related to vegetation composition and condition. FIREFLY (fluorescence imaging of red and far-red light yield) is a high performance spectrometer for estimating SIF. FIREFLY was flown in conjunction with NASA Goddard’s lidar, hyperspectral, and thermal (G-LiHT) instrument package in 2017, as a technology demonstration for airborne retrievals of SIF. Attributes of FIREFLY relevant to SIF retrieval, including detector response and linearity; full-width at half maximum (FWHM); stray light; dark current; and shot noise were characterized with a combination of observations from Goddard’s laser for absolute measurement of radiance calibration facility; an integrating sphere; controlled acquisitions of known targets; in-flight acquisitions; and forward modelling. FWHM, stray light, and dark current were found to be of acceptable magnitude, and characterized to within acceptable limits for SIF retrieval. FIREFLY observations were found to represent oxygen absorption features, along with a large number of solar absorption features. Shot noise was acceptable for direct SIF retrievals at native resolution, but indirect SIF retrievals from absorption features would require spatial aggregation, or repeated observations of targets.

During the last decade, a growing interest has arisen in the Solar Induced Fluorescence (SIF) emitted by terrestrial plants, with particular reference to the development of new methods for its retrieval from satellite data. This could pave the way to the monitoring of vegetation SIF at a global scale and its exploitation as a key parameter of dynamical global vegetation models used for carbon cycle studies. Besides, SIF retrieval from satellite could greatly contribute to the monitoring of the health status of vegetation with relation to several environmental stress factors. In this PhD thesis, a new method for the retrieval of the SIF of vegetation is proposed. The method is based on a statistical approach and it was developed in order to overcome some limits that typically affect the methods proposed up to now and that can affect the accuracy of the retrieved SIF. Specifically, this method provides as output both the SIF spectrum and the in-band averaged or integrated SIF intensity from the top of atmosphere radiance spectra measured by means of a passive remote sensing technique. In detail, in this PhD thesis the fundamentals of the proposed method have been discussed from a mathematical point of view and the implementation has been accurately described. Besides, performance assessment and robustness analysis of the ML-SIF method have been also carried out. The lack of both spectra of reflectance and SIF at canopy level for several vegetation species and the related SIF radiance spectra prevented the direct implementation of the learning procedure with actual measurements. As a consequence, this study relied on the use of a tool Soil Canopy Observation, Photochemistry and Energy Balance (SCOPE) and an in-house developed radiance image simulator. The latter has been specifically implemented during this work to simulate realistic radiance spectra containing SIF contributions.

La seguente ricerca di dottorato ha come obiettivo lo sviluppo di nuove strategie in grado di migliorare la stima e l’interpretazione del segnale di fluorescenza indotto dalla luce solare (SIF) emesso dalla clorofilla. In particolare la SIF emessa a livello del suolo è stata utilizzata per migliorare la comprensione del funzionamento della vegetazione terrestre ed acque interne. Nuove metriche di fluorescenza sono state definite a partire dello spettro completo di SIF ottenuto tramite l’algoritmo SpecFit applicato a misure spettrali di campo Top-Of-Canopy. Tali metriche sono state concepite per caratterizzare lo spettro totale di SIF, in termini di valori massimi di emissione (SIFred, SIFfar-red), posizione dei picchi ed integrale di fluorescenza (SIFINT). Il loro comportamento su scale stagionale e giornaliera è stato dunque confrontato con la SIF valutata nelle bande di assorbimento dell’ossigeno atmosferico (SIF760, SIF687) ed indici di riflettanza (NDVIred-edge, CIred-edge, NIRv, PRI), questi ultimi usati come proxy di parametri biofisici di vegetazione. Il SIF valutato ai picchi mostra sempre una forte correlazione con i corrispondenti valori delle bande O2, mentre il SIFINT rappresenta un parametro più completo e mostra dinamiche peculiari. A scala diurna, l'uso combinato di indici di riflettanza e metriche SIF permette una migliore caratterizzazione delle dinamiche delle diverse colture investigate. Stagionalmente, gli indici di SIF e di riflettanza sono caratterizzati dalla medesima evoluzione temporale in quanto entrambi guidati da cambiamenti della luce incidente e nella biomassa della canopy, del suo contenuto di clorofilla. Tuttavia, a tale scala, il riassorbimento della fluorescenza che avviene a livello fogliare e di chioma affligge la forma spettrale e intensità della SIF. In accordo con l’analisi condotta su misure spettrali simulate, il riassorbimento impedisce la corretta stima del rendimento di fluorescenza (SIFyield): pertanto, correggere lo spettro SIF per questo processo è cruciale. Con questo obiettivo, sono stati sviluppati due possibili approcci. Il metodo parametrico consente di correggere la SIF per il riassorbimento sfruttando relazioni parametriche con variabili spettrali misurate a livello TOC. Tuttavia, l’accuratezza di questo metodo dipende dallo stadio di sviluppo della pianta: migliori risultati sono ottenuti per chiome di media densità. Tale comportamento preclude l’utilizzo del metodo a scale stagionale. Il secondo approccio accoppia l’analisi della trasformata di Fourier con tecniche di Machine Learning. In questo caso, sia lo spettro SIF corretto che parametri biofisici quali LAI, Cab, SIFyield e aPAR, sono calcolati con maggiore accuratezza, indipendentemente dalla vegetazione considerata. In generale, il metodo parametrico può essere utilizzato in modo più semplice, ma manca di accuratezza quando applicato a vegetazioni rade, mentre l'algoritmo Fourier-ML è più complesso, ma offre risultati migliori. Per quanto concerne le acque interne, l’algoritmo Fluorescence Line Height è stato modificato per poter essere applicato a tali ambienti sfruttando misure iperspettrali di campo. L’evoluzione temporale del proxy di SIF così ottenuto concorda con l’andamento osservato per indici spettrali convenzionali (EPAR, R550, [Chl-a]), specialmente nei giorni caratterizzati da cielo sereno. Nuovi modelli di produzione primaria del fitoplancton sono stati inoltre definiti adattando per le acque interne il modello LUE (Light Use Efficiency) sviluppato per la vegetazione. Risultati promettenti sono stati ottenuti quando la SIFFLH e un nuovo proxy di efficienza di fotosintesi sono contemplati nel modello.
The PhD research aimed to develop novel strategies able to better retrieve and interpret the chlorophyll Solar-Induced Fluorescence (SIF) signal emitted by terrestrial vegetation and inland waters at ground level, to advance the understanding of ecosystems structure and functioning. SIF metrics were defined taking advantage of the full SIF spectrum available from the recently developed “spectrum-fitting” algorithm (SpecFit). The metrics were designed to characterize the SIF spectrum, in terms of red and far-red peaks maximum values (SIFred, SIFfar-red), corresponding wavelengths and the spectrally integrated value (SIFINT). SIF typically evaluated in the O2-A (SIF760) and O2-B (SIF687) bands and reflectance indices (used as proxies for canopy biophysical parameters) have been compared to the SIF spectrum. The reflectance indices selected are the NDVIred-edge, CIred-edge, NIRv and PRI. The analysis has been carried out at seasonal/diurnal scales, exploiting top-of canopy (TOC) spectral measurements acquired over three crops. The SIF evaluated at the peaks always show a strong correlation with the corresponding O2 bands values, while the SIFINT represents a more complete parameter and shows peculiar dynamics. At diurnal scale, the combined use of reflectance indices and TOC SIF metrics allows to gain a better knowledge of the crops dynamics. Seasonally, the SIF and reflectance indices show more similar temporal evolution along the growth-phases because they are mainly driven by changes in the overall canopy biomass, chlorophyll content and incident light. The reabsorption of the SIF within the canopy-leaf system affects the overall SIF spectral shape and magnitude at this temporal scale. As demonstrated on the synthetic dataset, the reabsorption effect prevents an accurate evaluation of the fluorescence quantum yield (SIFyield). Correcting the TOC SIF spectrum for the reabsorption is pivotal. In this regard, two different approaches have been developed and tested. The parametric method enables to correct SIF for the reabsorption (SIFRC) establishing parametric relationships with spectral variables routinely measured at TOC. The method accuracy depends on the plant growth phase, showing better results for medium-dense canopies. This behavior compromises the application of the method on the full seasonal analysis. The second approach based on Fourier-Machine Learning algorithm retrieves the SIFRC, and biophysical parameters (LAI, Cab, SIFyield, aPAR) with a better accuracy for all the conditions. The two approaches have been compared by considering synthetic simulations and real field measurements. Two methods were developed and tested starting from different assumptions: the parametric method can be used in a simpler way but it lacks accuracy for sparse conditions; while the Fourier-Machine Learning algorithm is more complex but offer better results. Regarding clear lake waters, a novel version of the Fluorescence Line Height approach has been implemented. The SIF proxy obtained agree with the temporal evolution of other conventional spectral indices (EPAR, R550 and [Chl-a]). Novel phytoplankton primary production models have been defined and tested adapting the vegetation Light Use Efficiency model for inland waters. Promising results have been obtained when the SIFFLH and a novel photosynthesis efficiency proxy here introduced are considered. In conclusion, the results obtained highlight the relevance to retrieve the SIF spectrum and the importance to employ SIF reabsorption correction methods to obtain relevant parameters better related with terrestrial vegetation functioning and less affected from canopy structure. This study has demonstrated that the hyperspectral and frequency measurements allow to follow the phytoplankton dynamics, particularly in clear sky days. Furthermore, the use of parameters linked to the SIF represents a promising approach for monitoring the phytoplankton primary production in lakes.

Accurate terrestrial biosphere model (TBM) simulations of gross carbon uptake (gross primary productivity - GPP) are essential for reliable future terrestrial carbon sink projections. However, uncertainties in TBM GPP estimates remain. Newly-available satellite-derived sun-induced chlorophyll fluorescence (SIF) data offer a promising direction for addressing this issue by constraining regional-to-global scale modelled GPP. Here, we use monthly 0.5 degrees GOME-2 SIF data from 2007 to 2011 to optimise GPP parameters of the ORCHIDEE TBM. The optimisation reduces GPP magnitude across all vegetation types except C4 plants. Global mean annual GPP therefore decreases from 194 +/- 57 PgCyr(-1) to 166 +/- 10 PgCyr(-1), bringing the model more in line with an up-scaled flux tower estimate of 133 PgCyr(-1). Strongest reductions in GPP are seen in boreal forests: the result is a shift in global GPP distribution, with a similar to 50% increase in the tropical to boreal productivity ratio. The optimisation resulted in a greater reduction in GPP than similar ORCHIDEE parameter optimisation studies using satellite-derived NDVI from MODIS and eddy covariance measurements of net CO2 fluxes from the FLUXNET network. Our study shows that SIF data will be instrumental in constraining TBM GPP estimates, with a consequent improvement in global carbon cycle projections.

Monitoring vegetation productivity at extremely fine resolutions is valuable for real-world agricultural applications, such as detecting crop stress and providing early warning of food insecurity. Solar-Induced Chlorophyll Fluorescence (SIF) provides a promising way to directly measure plant productivity from space. However, satellite SIF observations are only available at a coarse spatial resolution, making it impossible to monitor how individual crop types or farms are doing. This poses a challenging coarsely-supervised regression (or downscaling) task; at training time, we only have SIF labels at a coarse resolution (3km), but we want to predict SIF at much finer spatial resolutions (e.g. 30m, a 100x increase). We also have additional fine-resolution input features, but the relationship between these features and SIF is unknown. To address this, we propose Coarsely-Supervised Smooth U-Net (CS-SUNet), a novel method for this coarse supervision setting. CS-SUNet combines the expressive power of deep convolutional networks with novel regularization methods based on prior knowledge (such as a smoothness loss) that are crucial for preventing overfitting. Experiments show that CS-SUNet resolves fine-grained variations in SIF more accurately than existing methods.