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\n \n\n \n \n \n \n \n \n Global enhanced vegetation photosynthesis in urban environment and its drivers revealed by satellite solar-induced chlorophyll fluorescence data.\n \n \n \n \n\n\n \n Wang, S.; Cescatti, A.; Zhang, Y.; Zhou, Y.; Song, L.; and Li, J.\n\n\n \n\n\n\n Agricultural and Forest Meteorology, 340: 109622. September 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{wang_global_2023,\n\ttitle = {Global enhanced vegetation photosynthesis in urban environment and its drivers revealed by satellite solar-induced chlorophyll fluorescence data},\n\tvolume = {340},\n\tissn = {0168-1923},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0168192323003131},\n\tdoi = {10.1016/j.agrformet.2023.109622},\n\tabstract = {Investigation on the future impacts of climatic and environmental change on vegetation photosynthesis has been largely restricted to controlled field experiments, which can hardly be extended to global scale due to limited spatial, species and ecosystem coverages. However, in urban areas plants experience altered environments that mimic potential future conditions, with higher air temperature, atmospheric carbon dioxide (CO2) concentration and pollution levels. Cities can therefore be used as global, unplanned experiments for assessing the photosynthetic response to multiple climatic and environmental drivers. Following this logic, here we investigate the urbanization impact on vegetation primary productivity and its drivers at global 160 mega-cities, using high-spatial resolution satellite solar-induced chlorophyll fluorescence (SIF) data as the proxy of photosynthesis. SIF enhancements were observed across most of the urban-rural gradients, accounting for more than 85\\% of the investigated land pixels. More importantly, SIF enhancements due to indirect urbanization impact (i.e., the impacts of climatic and environmental factors on vegetation growth) offset approximately 47\\% of the SIF reductions due to land cover change, a value significantly higher than that observed for a greenness spectral index (Enhanced Vegetation Index, EVI) (30\\%). Atmospheric CO2, air temperature, radiation and atmospheric nitrogen dioxide (NO2) were found to be the main drivers accounting for the enhanced SIF in urban areas. These results prove a dominant and global enhancement of vegetation photosynthesis in urban conditions, and reveal the specific contribution of climatic and environmental factors. Our findings can help to forecast the impacts of future environmental conditions on vegetation photosynthesis, and enhance our knowledge on the overall response of terrestrial biomes to climate change.},\n\turldate = {2023-10-16},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Wang, Songhan and Cescatti, Alessandro and Zhang, Yongguang and Zhou, Yuyu and Song, Lian and Li, Ji},\n\tmonth = sep,\n\tyear = {2023},\n\tkeywords = {Environmental factors, Satellite observations, Solar-induced chlorophyll fluorescence, Urbanization impact, Vegetation index, Vegetation photosynthesis},\n\tpages = {109622},\n}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part I—Harnessing theory.\n \n \n \n \n\n\n \n Sun, Y.; Gu, L.; Wen, J.; van der Tol, C.; Porcar-Castell, A.; Joiner, J.; Chang, C. Y.; Magney, T.; Wang, L.; Hu, L.; Rascher, U.; Zarco-Tejada, P.; Barrett, C. B.; Lai, J.; Han, J.; and Luo, Z.\n\n\n \n\n\n\n Global Change Biology, 29(11): 2926–2952. 2023.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.16634\n\n\n\n
\n\n\n\n \n \n $\"From$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{sun_remotely_2023,\n\ttitle = {From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: {Part} {I}—{Harnessing} theory},\n\tvolume = {29},\n\tcopyright = {© 2023 John Wiley \\& Sons Ltd.},\n\tissn = {1365-2486},\n\tshorttitle = {From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.16634},\n\tdoi = {10.1111/gcb.16634},\n\tabstract = {Solar-induced chlorophyll fluorescence (SIF) is a remotely sensed optical signal emitted during the light reactions of photosynthesis. The past two decades have witnessed an explosion in availability of SIF data at increasingly higher spatial and temporal resolutions, sparking applications in diverse research sectors (e.g., ecology, agriculture, hydrology, climate, and socioeconomics). These applications must deal with complexities caused by tremendous variations in scale and the impacts of interacting and superimposing plant physiology and three-dimensional vegetation structure on the emission and scattering of SIF. At present, these complexities have not been overcome. To advance future research, the two companion reviews aim to (1) develop an analytical framework for inferring terrestrial vegetation structures and function that are tied to SIF emission, (2) synthesize progress and identify challenges in SIF research via the lens of multi-sector applications, and (3) map out actionable solutions to tackle these challenges and offer our vision for research priorities over the next 5–10 years based on the proposed analytical framework. This paper is the first of the two companion reviews, and theory oriented. It introduces a theoretically rigorous yet practically applicable analytical framework. Guided by this framework, we offer theoretical perspectives on three overarching questions: (1) The forward (mechanism) question—How are the dynamics of SIF affected by terrestrial ecosystem structure and function? (2) The inference question: What aspects of terrestrial ecosystem structure, function, and service can be reliably inferred from remotely sensed SIF and how? (3) The innovation question: What innovations are needed to realize the full potential of SIF remote sensing for real-world applications under climate change? The analytical framework elucidates that process complexity must be appreciated in inferring ecosystem structure and function from the observed SIF; this framework can serve as a diagnosis and inference tool for versatile applications across diverse spatial and temporal scales.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2023-10-16},\n\tjournal = {Global Change Biology},\n\tauthor = {Sun, Ying and Gu, Lianhong and Wen, Jiaming and van der Tol, Christiaan and Porcar-Castell, Albert and Joiner, Joanna and Chang, Christine Y. and Magney, Troy and Wang, Lixin and Hu, Leiqiu and Rascher, Uwe and Zarco-Tejada, Pablo and Barrett, Christopher B. and Lai, Jiameng and Han, Jimei and Luo, Zhenqi},\n\tyear = {2023},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.16634},\n\tkeywords = {NPQ, SIF, climate change, ecosystem function, ecosystem structure, photosynthesis, redox state, terrestrial carbon cycle},\n\tpages = {2926--2952},\n}\n\n

\n\n\n

\n Solar-induced chlorophyll fluorescence (SIF) is a remotely sensed optical signal emitted during the light reactions of photosynthesis. The past two decades have witnessed an explosion in availability of SIF data at increasingly higher spatial and temporal resolutions, sparking applications in diverse research sectors (e.g., ecology, agriculture, hydrology, climate, and socioeconomics). These applications must deal with complexities caused by tremendous variations in scale and the impacts of interacting and superimposing plant physiology and three-dimensional vegetation structure on the emission and scattering of SIF. At present, these complexities have not been overcome. To advance future research, the two companion reviews aim to (1) develop an analytical framework for inferring terrestrial vegetation structures and function that are tied to SIF emission, (2) synthesize progress and identify challenges in SIF research via the lens of multi-sector applications, and (3) map out actionable solutions to tackle these challenges and offer our vision for research priorities over the next 5–10 years based on the proposed analytical framework. This paper is the first of the two companion reviews, and theory oriented. It introduces a theoretically rigorous yet practically applicable analytical framework. Guided by this framework, we offer theoretical perspectives on three overarching questions: (1) The forward (mechanism) question—How are the dynamics of SIF affected by terrestrial ecosystem structure and function? (2) The inference question: What aspects of terrestrial ecosystem structure, function, and service can be reliably inferred from remotely sensed SIF and how? (3) The innovation question: What innovations are needed to realize the full potential of SIF remote sensing for real-world applications under climate change? The analytical framework elucidates that process complexity must be appreciated in inferring ecosystem structure and function from the observed SIF; this framework can serve as a diagnosis and inference tool for versatile applications across diverse spatial and temporal scales.\n

\n\n\n

\n \n\n \n \n \n \n \n \n The roles of radiative, structural and physiological information of sun-induced chlorophyll fluorescence in predicting gross primary production of a corn crop at various temporal scales.\n \n \n \n \n\n\n \n Yang, P.; Liu, X.; Liu, Z.; van der Tol, C.; and Liu, L.\n\n\n \n\n\n\n Agricultural and Forest Meteorology, 342: 109720. November 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{yang_roles_2023,\n\ttitle = {The roles of radiative, structural and physiological information of sun-induced chlorophyll fluorescence in predicting gross primary production of a corn crop at various temporal scales},\n\tvolume = {342},\n\tissn = {0168-1923},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0168192323004100},\n\tdoi = {10.1016/j.agrformet.2023.109720},\n\tabstract = {Extensive research suggests that sun-induced chlorophyll fluorescence (SIF) and gross primary productivity (GPP) have a near-linear relationship, providing a promising avenue for estimating the carbon uptake of ecosystems. However, the factors influencing the relationship are not yet clear. This study examines the roles of SIF's radiative, structural, and physiological information in predicting GPP, based on four years of field observations of a corn canopy at various temporal scales. We quantified SIF's radiative component by measuring the intensity of incident photosynthetically active radiation (iPAR), and separated the structural and physiological components from SIF observations using the fluorescence correction vegetation index (FCVI). Our results show that the R2 values between SIF and GPP, as estimated by linear models, increased from 0.66 at a half-hour resolution to 0.86 at a one-month resolution. In comparison, the product of FCVI and iPAR, representing the non-physiological information of SIF, performed consistently well in predicting GPP with R2{\\textgreater}0.84 at various temporal scales, suggesting a limited contribution of the physiological information of SIF for GPP estimation.. The results further reveal that SIF's radiative and structural components positively impacted the SIF-GPP linearity, while the physiological component had a negative impact on the linearity for most cases, changing from 0.6 \\% to -27.5 \\%. As for the temporal dependency, the controls of the SIF-GPP relationship moved from radiation at diurnal scales to structure at the seasonal scales. The structural contribution changed from 14.8 \\% at a half-hour resolution to 92.4 \\% at a one-month resolution, while the radiative contribution decreased from 118.0 \\% at a half-hour resolution to 11.7 \\% at a one-month resolution. This study contributes to enhancing our understanding of the physiological information conveyed by SIF and the factors influencing the temporal dependency of the SIF-GPP relationship.},\n\turldate = {2023-10-16},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Yang, Peiqi and Liu, Xinjie and Liu, Zhigang and van der Tol, Christiaan and Liu, Liangyun},\n\tmonth = nov,\n\tyear = {2023},\n\tkeywords = {Corn, Gross primary productivity, Plant physiology, Sun-induced chlorophyll fluorescence, Temporal scale},\n\tpages = {109720},\n}\n\n

\n\n\n

\n Extensive research suggests that sun-induced chlorophyll fluorescence (SIF) and gross primary productivity (GPP) have a near-linear relationship, providing a promising avenue for estimating the carbon uptake of ecosystems. However, the factors influencing the relationship are not yet clear. This study examines the roles of SIF's radiative, structural, and physiological information in predicting GPP, based on four years of field observations of a corn canopy at various temporal scales. We quantified SIF's radiative component by measuring the intensity of incident photosynthetically active radiation (iPAR), and separated the structural and physiological components from SIF observations using the fluorescence correction vegetation index (FCVI). Our results show that the R2 values between SIF and GPP, as estimated by linear models, increased from 0.66 at a half-hour resolution to 0.86 at a one-month resolution. In comparison, the product of FCVI and iPAR, representing the non-physiological information of SIF, performed consistently well in predicting GPP with R2\\textgreater0.84 at various temporal scales, suggesting a limited contribution of the physiological information of SIF for GPP estimation.. The results further reveal that SIF's radiative and structural components positively impacted the SIF-GPP linearity, while the physiological component had a negative impact on the linearity for most cases, changing from 0.6 % to -27.5 %. As for the temporal dependency, the controls of the SIF-GPP relationship moved from radiation at diurnal scales to structure at the seasonal scales. The structural contribution changed from 14.8 % at a half-hour resolution to 92.4 % at a one-month resolution, while the radiative contribution decreased from 118.0 % at a half-hour resolution to 11.7 % at a one-month resolution. This study contributes to enhancing our understanding of the physiological information conveyed by SIF and the factors influencing the temporal dependency of the SIF-GPP relationship.\n

\n\n\n

\n \n\n \n \n \n \n \n \n SIF and Vegetation Indices in the US Midwestern Agroecosystems, 2016-2021.\n \n \n \n \n\n\n \n Wu, G.; Guan, K.; Kimm, H.; Miao, G.; and Jiang, C.\n\n\n \n\n\n\n ORNL DAAC. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"SIF$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wu_sif_2023,\n\ttitle = {{SIF} and {Vegetation} {Indices} in the {US} {Midwestern} {Agroecosystems}, 2016-2021},\n\turl = {https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2136},\n\tdoi = {10.3334/ORNLDAAC/2136},\n\tabstract = {ORNL DAAC: This dataset contains half-hourly ground solar-induced chlorophyll fluorescence (SIF) and vegetation indices including NDVI, EVI, Red edge chlorophyll index, green chlorophyll index, and photochemical reflectance index at seven crop sites in Nebraska and Illinois for the period 2016-2021. Four sites were located at Eddy Covariance (EC) tower sites (sites US-Ne2, US-Ne3, US-UiB, and US-UiC), and three sites were located on private farms (sites Reifsteck, Rund, and Reinhart). The sites were either miscanthus, corn-soybean rotation or corn-corn-soybean rotation. The spectral data for SIF retrieval and hyperspectral reflectance for vegetation index calculation were collected by the FluoSpec2 system, installed near planting, and uninstalled after harvest to collect whole growing-season data. Raw nadir SIF at 760 nm from different algorithms (sFLD, 3FLD, iFLD, SFM) are included. SFM\\_nonlinear and SFM\\_linear represent the Spectral fitting method (SFM) with the assumption that fluorescence and reflectance change with wavelength non-linearly and linearly, respectively. Additional data include two SIF correction factors including calibration coefficient adjustment factor (f\\_cal\\_corr\\_QEPRO) and upscaling nadir SIF to eddy covariance footprint factor (ratio\\_EC footprint, SIF pixel), and measured FPAR from quantum sensors and Rededge NDVI calculated FPAR. The data are provided in comma-separated values (CSV) format.},\n\tlanguage = {en-US},\n\turldate = {2023-10-16},\n\tjournal = {ORNL DAAC},\n\tauthor = {Wu, G. and Guan, K. and Kimm, H. and Miao, G. and Jiang, C.},\n\tmonth = mar,\n\tyear = {2023},\n}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n From remotely-sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II—Harnessing data.\n \n \n \n \n\n\n \n Sun, Y.; Wen, J.; Gu, L.; Joiner, J.; Chang, C. Y.; van der Tol, C.; Porcar-Castell, A.; Magney, T.; Wang, L.; Hu, L.; Rascher, U.; Zarco-Tejada, P.; Barrett, C. B.; Lai, J.; Han, J.; and Luo, Z.\n\n\n \n\n\n\n Global Change Biology, 29(11): 2893–2925. 2023.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.16646\n\n\n\n
\n\n\n\n \n \n $\"From$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{sun_remotely-sensed_2023,\n\ttitle = {From remotely-sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: {Part} {II}—{Harnessing} data},\n\tvolume = {29},\n\tcopyright = {© 2023 John Wiley \\& Sons Ltd.},\n\tissn = {1365-2486},\n\tshorttitle = {From remotely-sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.16646},\n\tdoi = {10.1111/gcb.16646},\n\tabstract = {Although our observing capabilities of solar-induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency of SIF datasets are still in an active stage of research and development. As a result, there are considerable inconsistencies among diverse SIF datasets at all scales and the widespread applications of them have led to contradictory findings. The present review is the second of the two companion reviews, and data oriented. It aims to (1) synthesize the variety, scale, and uncertainty of existing SIF datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities laid out in (Sun et al., 2023) may impact process interpretation of various applications and contribute to inconsistent findings. We emphasize that accurate interpretation of the functional relationships between SIF and other ecological indicators is contingent upon complete understanding of SIF data quality and uncertainty. Biases and uncertainties in SIF observations can significantly confound interpretation of their relationships and how such relationships respond to environmental variations. Built upon our syntheses, we summarize existing gaps and uncertainties in current SIF observations. Further, we offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change, including enhancing in-situ SIF observing capability especially in “data desert” regions, improving cross-instrument data standardization and network coordination, and advancing applications by fully harnessing theory and data.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2023-10-16},\n\tjournal = {Global Change Biology},\n\tauthor = {Sun, Ying and Wen, Jiaming and Gu, Lianhong and Joiner, Joanna and Chang, Christine Y. and van der Tol, Christiaan and Porcar-Castell, Albert and Magney, Troy and Wang, Lixin and Hu, Leiqiu and Rascher, Uwe and Zarco-Tejada, Pablo and Barrett, Christopher B. and Lai, Jiameng and Han, Jimei and Luo, Zhenqi},\n\tyear = {2023},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.16646},\n\tkeywords = {SIF, carbon cycle, climate change, photosynthesis, precision agriculture, retrievals, stress monitoring and early warning, vegetation index},\n\tpages = {2893--2925},\n}\n\n

\n\n\n\n\n\n

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\n \n\n \n \n \n \n \n \n Cloud-Free Global Maps of Essential Vegetation Traits Processed from the TOA Sentinel-3 Catalogue in Google Earth Engine.\n \n \n \n \n\n\n \n Kovács, D. D.; Reyes-Muñoz, P.; Salinero-Delgado, M.; Mészáros, V. I.; Berger, K.; and Verrelst, J.\n\n\n \n\n\n\n Remote Sensing, 15(13): 3404. January 2023.\n Number: 13 Publisher: Multidisciplinary Digital Publishing Institute\n\n\n\n
\n\n\n\n \n \n $\"Cloud-Free$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{kovacs_cloud-free_2023,\n\ttitle = {Cloud-{Free} {Global} {Maps} of {Essential} {Vegetation} {Traits} {Processed} from the {TOA} {Sentinel}-3 {Catalogue} in {Google} {Earth} {Engine}},\n\tvolume = {15},\n\tcopyright = {http://creativecommons.org/licenses/by/3.0/},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/15/13/3404},\n\tdoi = {10.3390/rs15133404},\n\tabstract = {Global mapping of essential vegetation traits (EVTs) through data acquired by Earth-observing satellites provides a spatially explicit way to analyze the current vegetation states and dynamics of our planet. Although significant efforts have been made, there is still a lack of global and consistently derived multi-temporal trait maps that are cloud-free. Here we present the processing chain for the spatiotemporally continuous production of four EVTs at a global scale: (1) fraction of absorbed photosynthetically active radiation (FAPAR), (2) leaf area index (LAI), (3) fractional vegetation cover (FVC), and (4) leaf chlorophyll content (LCC). The proposed workflow presents a scalable processing approach to the global cloud-free mapping of the EVTs. Hybrid retrieval models, named S3-TOA-GPR-1.0-WS, were implemented into Google Earth Engine (GEE) using Sentinel-3 Ocean and Land Color Instrument (OLCI) Level-1B for the mapping of the four EVTs along with associated uncertainty estimates. We used the Whittaker smoother (WS) for the temporal reconstruction of the four EVTs, which led to continuous data streams, here applied to the year 2019. Cloud-free maps were produced at 5 km spatial resolution at 10-day time intervals. The consistency and plausibility of the EVT estimates for the resulting annual profiles were evaluated by per-pixel intra-annually correlating against corresponding vegetation products of both MODIS and Copernicus Global Land Service (CGLS). The most consistent results were obtained for LAI, which showed intra-annual correlations with an average Pearson correlation coefficient (R) of 0.57 against the CGLS LAI product. Globally, the EVT products showed consistent results, specifically obtaining higher correlation than R{\\textgreater} 0.5 with reference products between 30 and 60° latitude in the Northern Hemisphere. Additionally, intra-annual goodness-of-fit statistics were also calculated locally against reference products over four distinct vegetated land covers. As a general trend, vegetated land covers with pronounced phenological dynamics led to high correlations between the different products. However, sparsely vegetated fields as well as areas near the equator linked to smaller seasonality led to lower correlations. We conclude that the global gap-free mapping of the four EVTs was overall consistent. Thanks to GEE, the entire OLCI L1B catalogue can be processed efficiently into the EVT products on a global scale and made cloud-free with the WS temporal reconstruction method. Additionally, GEE facilitates the workflow to be operationally applicable and easily accessible to the broader community.},\n\tlanguage = {en},\n\tnumber = {13},\n\turldate = {2023-10-16},\n\tjournal = {Remote Sensing},\n\tauthor = {Kovács, Dávid D. and Reyes-Muñoz, Pablo and Salinero-Delgado, Matías and Mészáros, Viktor Ixion and Berger, Katja and Verrelst, Jochem},\n\tmonth = jan,\n\tyear = {2023},\n\tnote = {Number: 13\nPublisher: Multidisciplinary Digital Publishing Institute},\n\tkeywords = {FAPAR, FVC, Gaussian process regression, Google Earth Engine, LAI, LCC, Sentinel-3, TOA radiance, Whittaker, essential vegetation traits, machine learning, temporal reconstruction, time series},\n\tpages = {3404},\n}\n\n

\n\n\n

\n Global mapping of essential vegetation traits (EVTs) through data acquired by Earth-observing satellites provides a spatially explicit way to analyze the current vegetation states and dynamics of our planet. Although significant efforts have been made, there is still a lack of global and consistently derived multi-temporal trait maps that are cloud-free. Here we present the processing chain for the spatiotemporally continuous production of four EVTs at a global scale: (1) fraction of absorbed photosynthetically active radiation (FAPAR), (2) leaf area index (LAI), (3) fractional vegetation cover (FVC), and (4) leaf chlorophyll content (LCC). The proposed workflow presents a scalable processing approach to the global cloud-free mapping of the EVTs. Hybrid retrieval models, named S3-TOA-GPR-1.0-WS, were implemented into Google Earth Engine (GEE) using Sentinel-3 Ocean and Land Color Instrument (OLCI) Level-1B for the mapping of the four EVTs along with associated uncertainty estimates. We used the Whittaker smoother (WS) for the temporal reconstruction of the four EVTs, which led to continuous data streams, here applied to the year 2019. Cloud-free maps were produced at 5 km spatial resolution at 10-day time intervals. The consistency and plausibility of the EVT estimates for the resulting annual profiles were evaluated by per-pixel intra-annually correlating against corresponding vegetation products of both MODIS and Copernicus Global Land Service (CGLS). The most consistent results were obtained for LAI, which showed intra-annual correlations with an average Pearson correlation coefficient (R) of 0.57 against the CGLS LAI product. Globally, the EVT products showed consistent results, specifically obtaining higher correlation than R\\textgreater 0.5 with reference products between 30 and 60° latitude in the Northern Hemisphere. Additionally, intra-annual goodness-of-fit statistics were also calculated locally against reference products over four distinct vegetated land covers. As a general trend, vegetated land covers with pronounced phenological dynamics led to high correlations between the different products. However, sparsely vegetated fields as well as areas near the equator linked to smaller seasonality led to lower correlations. We conclude that the global gap-free mapping of the four EVTs was overall consistent. Thanks to GEE, the entire OLCI L1B catalogue can be processed efficiently into the EVT products on a global scale and made cloud-free with the WS temporal reconstruction method. Additionally, GEE facilitates the workflow to be operationally applicable and easily accessible to the broader community.\n

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\n \n\n \n \n \n \n \n \n Regional-Scale Wilting Point Estimation Using Satellite SIF, Radiative-Transfer Inversion, and Soil-Vegetation-Atmosphere Transfer Simulation: A Grassland Study.\n \n \n \n \n\n\n \n Kiyono, T.; Noda, H. M.; Kumagai, T.; Oshio, H.; Yoshida, Y.; Matsunaga, T.; and Hikosaka, K.\n\n\n \n\n\n\n Journal of Geophysical Research: Biogeosciences, 128(4): e2022JG007074. 2023.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022JG007074\n\n\n\n
\n\n\n\n \n \n $\"Regional-Scale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{kiyono_regional-scale_2023,\n\ttitle = {Regional-{Scale} {Wilting} {Point} {Estimation} {Using} {Satellite} {SIF}, {Radiative}-{Transfer} {Inversion}, and {Soil}-{Vegetation}-{Atmosphere} {Transfer} {Simulation}: {A} {Grassland} {Study}},\n\tvolume = {128},\n\tcopyright = {© 2023. The Authors.},\n\tissn = {2169-8961},\n\tshorttitle = {Regional-{Scale} {Wilting} {Point} {Estimation} {Using} {Satellite} {SIF}, {Radiative}-{Transfer} {Inversion}, and {Soil}-{Vegetation}-{Atmosphere} {Transfer} {Simulation}},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2022JG007074},\n\tdoi = {10.1029/2022JG007074},\n\tabstract = {Although water availability strongly controls gross primary production (GPP), the impact of soil moisture content (SMC) (wilting point) is poorly quantified on regional and global scales. In this study, we used 10 years of observations of solar-induced chlorophyll fluorescence (SIF) from the Greenhouse gases Observing Satellite (GOSAT) satellite to estimate the wilting point of a semiarid grassland on the Mongolian Plateau. Radiative-transfer model inversion and soil-vegetation-atmosphere transfer simulation were sequentially conducted to distinguish the drought impacts on plant physiology from the changes in the leaf-canopy optical properties. We modified an existing inversion algorithm and the widely used Soil-Canopy Observation of Photosynthesis and Energy fluxes model to adequately evaluate dryland features, for example, sparse canopy and strong convection. The modified model, with retrieved parameters and calibration to GOSAT SIF, predicted realistic GPP values. We found that (a) the SIF yield estimated from GOSAT showed a clear sigmoidal pattern in relation to drought, and the estimated wilting point matched ground-based observations in the literature within ∼0.01 m3 m−3 for the SMC, (b) tuning the maximum carboxylation rate improved the SIF prediction after considering the changes in the leaf-canopy optical properties, implying that GOSAT detected drought stress in leaf-level photosynthesis, and (c) the surface energy balance significantly impacted the grassland's SIF; the modified model reproduced observed SIF well (mean bias = 0.004 mW m−2 nm−1 sr−1 in summer), whereas the original model predicted substantially low values under weak horizontal wind conditions. Some model-observation mismatches in the SIF suggest that more research is needed for fluorescence parametrization (e.g., photoinhibition) and for additional observation constraints.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-10-16},\n\tjournal = {Journal of Geophysical Research: Biogeosciences},\n\tauthor = {Kiyono, T. and Noda, H. M. and Kumagai, T. and Oshio, H. and Yoshida, Y. and Matsunaga, T. and Hikosaka, K.},\n\tyear = {2023},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022JG007074},\n\tkeywords = {GOSAT, drought, gross primary production (GPP), radiative transfer model inversion, soil-vegetation-atmosphere transfer, solar-induced chlorophyll fluorescence (SIF)},\n\tpages = {e2022JG007074},\n}\n\n

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\n \n\n \n \n \n \n \n \n Contributions of the understory and midstory to total canopy solar-induced chlorophyll fluorescence in a ground-based study in conjunction with seasonal gross primary productivity in a cool-temperate deciduous broadleaf forest.\n \n \n \n \n\n\n \n Morozumi, T.; Kato, T.; Kobayashi, H.; Sakai, Y.; Nakashima, N.; Buareal, K.; Nasahara, K. N.; Akitsu, T. K.; Murayama, S.; Noda, H. M.; and Muraoka, H.\n\n\n \n\n\n\n Remote Sensing of Environment, 284: 113340. January 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Contributions$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{morozumi_contributions_2023,\n\ttitle = {Contributions of the understory and midstory to total canopy solar-induced chlorophyll fluorescence in a ground-based study in conjunction with seasonal gross primary productivity in a cool-temperate deciduous broadleaf forest},\n\tvolume = {284},\n\tissn = {0034-4257},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0034425722004461},\n\tdoi = {10.1016/j.rse.2022.113340},\n\tabstract = {Solar-induced chlorophyll fluorescence (SIF) can represent gross primary productivity (GPP) in many types of terrestrial vegetation. In principle, the chlorophyll-a fluorescence signal responds to the amount of light absorption and the fraction of energy distribution in photosystems. Therefore, it is mechanistically linked with CO2 assimilation. Recently, radiative transfer models have traced the processes of emission, interception and reabsorption within a canopy in heterogeneous tree stands. These processes influence the expected relationship between SIF and GPP. We inferred that the vertical profile of SIF could reveal those processes. In addition, the sum of SIF emission from different pathways within the canopy must be equivalent to the total SIF emission from all leaves. The purposes of this study were: (1) to clarify the seasonal and diurnal variations of ground-based SIF observed above the overstory, midstory and understory layers (strata); (2) to examine whether the sum of SIF observed from the three layers could represent total SIF emissions, and (3) to examine whether SIF above the understory could reveal the photosynthetic activity within the vertical profile of a forest. To estimate how much SIF was present after its emission from leaves within the canopy, we conducted spectral radiation observations in three vertical layers: above the understory, midstory and overstory (8, 14 and 18 m from the ground, respectively) to retrieve the red and far-red SIF in a deciduous broadleaf forest ecosystem in Japan from April to November 2020. We found that SIF above the overstory and the sum of SIF from the three layers increased sharply at the leaf-onset of the overstory and was correlated with each season's GPP. The sum of far-red SIF from the three layers was proportional to the total SIF estimated using the escape ratio approach for SIF above the overstory. In this study we demonstrated that SIF above the understory could detect the increased photosynthesis of the understory in spring, compared with the GPP and the vertical profile of the CO2 concentrations. In summary, SIF observed in three forest layers detected the seasonal change of GPP within the canopy. Thus, a multi-layer approach can be used to understand the relationship between SIF and photosynthesis within the canopy.},\n\turldate = {2023-10-16},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Morozumi, Tomoki and Kato, Tomomichi and Kobayashi, Hideki and Sakai, Yuma and Nakashima, Naohisa and Buareal, Kanokrat and Nasahara, Kenlo Nishida and Akitsu, Tomoko Kawaguchi and Murayama, Shohei and Noda, Hibiki M. and Muraoka, Hiroyuki},\n\tmonth = jan,\n\tyear = {2023},\n\tkeywords = {Forest stratification, GPP, Multi-vertical layer spectroscopy, Solar-induced chlorophyll fluorescence},\n\tpages = {113340},\n}\n\n

\n\n\n

\n Solar-induced chlorophyll fluorescence (SIF) can represent gross primary productivity (GPP) in many types of terrestrial vegetation. In principle, the chlorophyll-a fluorescence signal responds to the amount of light absorption and the fraction of energy distribution in photosystems. Therefore, it is mechanistically linked with CO2 assimilation. Recently, radiative transfer models have traced the processes of emission, interception and reabsorption within a canopy in heterogeneous tree stands. These processes influence the expected relationship between SIF and GPP. We inferred that the vertical profile of SIF could reveal those processes. In addition, the sum of SIF emission from different pathways within the canopy must be equivalent to the total SIF emission from all leaves. The purposes of this study were: (1) to clarify the seasonal and diurnal variations of ground-based SIF observed above the overstory, midstory and understory layers (strata); (2) to examine whether the sum of SIF observed from the three layers could represent total SIF emissions, and (3) to examine whether SIF above the understory could reveal the photosynthetic activity within the vertical profile of a forest. To estimate how much SIF was present after its emission from leaves within the canopy, we conducted spectral radiation observations in three vertical layers: above the understory, midstory and overstory (8, 14 and 18 m from the ground, respectively) to retrieve the red and far-red SIF in a deciduous broadleaf forest ecosystem in Japan from April to November 2020. We found that SIF above the overstory and the sum of SIF from the three layers increased sharply at the leaf-onset of the overstory and was correlated with each season's GPP. The sum of far-red SIF from the three layers was proportional to the total SIF estimated using the escape ratio approach for SIF above the overstory. In this study we demonstrated that SIF above the understory could detect the increased photosynthesis of the understory in spring, compared with the GPP and the vertical profile of the CO2 concentrations. In summary, SIF observed in three forest layers detected the seasonal change of GPP within the canopy. Thus, a multi-layer approach can be used to understand the relationship between SIF and photosynthesis within the canopy.\n

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\n \n\n \n \n \n \n \n \n An exploratory steady-state redox model of photosynthetic linear electron transport for use in complete modelling of photosynthesis for broad applications.\n \n \n \n \n\n\n \n Gu, L.; Grodzinski, B.; Han, J.; Marie, T.; Zhang, Y.; Song, Y. C.; and Sun, Y.\n\n\n \n\n\n\n Plant, Cell & Environment, 46(5): 1540–1561. 2023.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/pce.14563\n\n\n\n
\n\n\n\n \n \n $\"An$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{gu_exploratory_2023,\n\ttitle = {An exploratory steady-state redox model of photosynthetic linear electron transport for use in complete modelling of photosynthesis for broad applications},\n\tvolume = {46},\n\tcopyright = {© 2023 The Authors. Plant, Cell \\& Environment published by John Wiley \\& Sons Ltd.},\n\tissn = {1365-3040},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1111/pce.14563},\n\tdoi = {10.1111/pce.14563},\n\tabstract = {A photochemical model of photosynthetic electron transport (PET) is needed to integrate photophysics, photochemistry, and biochemistry to determine redox conditions of electron carriers and enzymes for plant stress assessment and mechanistically link sun-induced chlorophyll fluorescence to carbon assimilation for remotely sensing photosynthesis. Towards this goal, we derived photochemical equations governing the states and redox reactions of complexes and electron carriers along the PET chain. These equations allow the redox conditions of the mobile plastoquinone pool and the cytochrome b6f complex (Cyt) to be inferred with typical fluorometry. The equations agreed well with fluorometry measurements from diverse C3/C4 species across environments in the relationship between the PET rate and fraction of open photosystem II reaction centres. We found the oxidation of plastoquinol by Cyt is the bottleneck of PET, and genetically improving the oxidation of plastoquinol by Cyt may enhance the efficiency of PET and photosynthesis across species. Redox reactions and photochemical and biochemical interactions are highly redundant in their complex controls of PET. Although individual reaction rate constants cannot be resolved, they appear in parameter groups which can be collectively inferred with fluorometry measurements for broad applications. The new photochemical model developed enables advances in different fronts of photosynthesis research.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2023-10-16},\n\tjournal = {Plant, Cell \\& Environment},\n\tauthor = {Gu, Lianhong and Grodzinski, Bernard and Han, Jimei and Marie, Telesphore and Zhang, Yong-Jiang and Song, Yang C. and Sun, Ying},\n\tyear = {2023},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/pce.14563},\n\tkeywords = {cytochrome b6f complex, photosynthesis model, photosystems, plastoquinone, redox reactions},\n\tpages = {1540--1561},\n}\n\n

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\n \n\n \n \n \n \n \n \n OLCI-A/B tandem phase: evaluation of FLuorescence EXplorer (FLEX)-like radiances and estimation of systematic differences between OLCI-A and OLCI-FLEX.\n \n \n \n \n\n\n \n Jänicke, L. K.; Preusker, R.; Celesti, M.; Tudoroiu, M.; Fischer, J.; Schüttemeyer, D.; and Drusch, M.\n\n\n \n\n\n\n Atmospheric Measurement Techniques, 16(12): 3101–3121. June 2023.\n Publisher: Copernicus GmbH\n\n\n\n
\n\n\n\n \n \n $\"OLCI-A/B$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{janicke_olci-b_2023,\n\ttitle = {{OLCI}-{A}/{B} tandem phase: evaluation of {FLuorescence} {EXplorer} ({FLEX})-like radiances and estimation of systematic differences between {OLCI}-{A} and {OLCI}-{FLEX}},\n\tvolume = {16},\n\tissn = {1867-1381},\n\tshorttitle = {{OLCI}-{A}/{B} tandem phase},\n\turl = {https://amt.copernicus.org/articles/16/3101/2023/},\n\tdoi = {10.5194/amt-16-3101-2023},\n\tabstract = {During the tandem phase of Sentinel-3A and Sentinel-3B in summer 2018 the Ocean and Land Colour Imager (OLCI) mounted on the Sentinel-3B satellite was reprogrammed to mimics ESA's eighth Earth Explorer, the FLuorescence EXplorer (FLEX). The OLCI in FLEX configuration (OLCI-FLEX) had 45 spectral bands between 500 and 792 nm. The new data set with high-spectral-resolution measurements (bandwidth: 1.7–3.7 nm) serves as preparation for the FLEX mission. Spatially co-registered measurements of both instruments are used for the atmospheric correction and the retrieval of surface parameters, e.g. the fluorescence or the leaf area index. For such combined products, it is essential that both instruments are radiometrically consistent. We developed a transfer function to compare radiance measurements from different optical sensors and to monitor their consistency.\n\n In the presented study, the transfer function shifts information gained from high-resolution “FLEX-mode” settings to information convolved with the spectral response of the normal (lower) spectral resolution of the OLCI sensor. The resulting reconstructed low-resolution radiance is representative of the high-resolution data (OLCI-FLEX), and it can be compared with the measured low-resolution radiance (OLCI-A measurements). This difference is used to quantify systematic differences between the instruments. Applying the transfer function, we could show that OLCI-A is about 2 \\% brighter than OLCI-FLEX for most bands of the OLCI-FLEX spectral domain. At the longer wavelengths (\\&gt; 770 nm) OLCI-A is about 5 \\% darker. Sensitivity studies showed that the parameters affecting the quality of the comparison of OLCI-A and OLCI-FLEX with the transfer function are mainly the surface reflectance and secondarily the aerosol composition. However, the aerosol composition can be simplified as long as it is treated consistently in all steps in the transfer function.\n\n Generally, the transfer function enables direct comparison of instruments with different spectral responses even with different observation geometries or different levels of observation. The method is sensitive to measurement biases and errors resulting from the processing. One application could be the quality control of the FLEX mission; presently it is also useful for the quality control of the OLCI-FLEX data.},\n\tlanguage = {English},\n\tnumber = {12},\n\turldate = {2023-10-16},\n\tjournal = {Atmospheric Measurement Techniques},\n\tauthor = {Jänicke, Lena Katharina and Preusker, Rene and Celesti, Marco and Tudoroiu, Marin and Fischer, Jürgen and Schüttemeyer, Dirk and Drusch, Matthias},\n\tmonth = jun,\n\tyear = {2023},\n\tnote = {Publisher: Copernicus GmbH},\n\tpages = {3101--3121},\n}\n\n

\n\n\n

\n During the tandem phase of Sentinel-3A and Sentinel-3B in summer 2018 the Ocean and Land Colour Imager (OLCI) mounted on the Sentinel-3B satellite was reprogrammed to mimics ESA's eighth Earth Explorer, the FLuorescence EXplorer (FLEX). The OLCI in FLEX configuration (OLCI-FLEX) had 45 spectral bands between 500 and 792 nm. The new data set with high-spectral-resolution measurements (bandwidth: 1.7–3.7 nm) serves as preparation for the FLEX mission. Spatially co-registered measurements of both instruments are used for the atmospheric correction and the retrieval of surface parameters, e.g. the fluorescence or the leaf area index. For such combined products, it is essential that both instruments are radiometrically consistent. We developed a transfer function to compare radiance measurements from different optical sensors and to monitor their consistency. In the presented study, the transfer function shifts information gained from high-resolution “FLEX-mode” settings to information convolved with the spectral response of the normal (lower) spectral resolution of the OLCI sensor. The resulting reconstructed low-resolution radiance is representative of the high-resolution data (OLCI-FLEX), and it can be compared with the measured low-resolution radiance (OLCI-A measurements). This difference is used to quantify systematic differences between the instruments. Applying the transfer function, we could show that OLCI-A is about 2 % brighter than OLCI-FLEX for most bands of the OLCI-FLEX spectral domain. At the longer wavelengths (> 770 nm) OLCI-A is about 5 % darker. Sensitivity studies showed that the parameters affecting the quality of the comparison of OLCI-A and OLCI-FLEX with the transfer function are mainly the surface reflectance and secondarily the aerosol composition. However, the aerosol composition can be simplified as long as it is treated consistently in all steps in the transfer function. Generally, the transfer function enables direct comparison of instruments with different spectral responses even with different observation geometries or different levels of observation. The method is sensitive to measurement biases and errors resulting from the processing. One application could be the quality control of the FLEX mission; presently it is also useful for the quality control of the OLCI-FLEX data.\n

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\n \n\n \n \n \n \n \n \n Addressing validation challenges for TROPOMI solar-induced chlorophyll fluorescence products using tower-based measurements and an NIRv-scaled approach.\n \n \n \n \n\n\n \n Du, S.; Liu, X.; Chen, J.; Duan, W.; and Liu, L.\n\n\n \n\n\n\n Remote Sensing of Environment, 290: 113547. May 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Addressing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{du_addressing_2023,\n\ttitle = {Addressing validation challenges for {TROPOMI} solar-induced chlorophyll fluorescence products using tower-based measurements and an {NIRv}-scaled approach},\n\tvolume = {290},\n\tissn = {0034-4257},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0034425723000986},\n\tdoi = {10.1016/j.rse.2023.113547},\n\tabstract = {Several satellite-based solar-induced chlorophyll fluorescence (SIF) products have progressively emerged and have been developed in recent years. However, till date, no direct validation has been conducted on existing satellite-based SIF products. In this study, validation of two groups of TROPOspheric Monitoring Instrument (TROPOMI) SIF products, namely TROPOSIFCaltech (containing far-red and red TROPOSIFCaltech datasets) and TROPOSIFESA (containing TROPOSIF735 and TROPOSIF743 datasets that are retrieved from two different retrieval windows), was conducted using tower-based SIF measurements over seven sites. Several issues and potential obstacles emerged while matching satellite-based and in situ SIF retrievals, including spatial scale mismatch. To overcome the spatial scale mismatch between the satellite data and ground observations, a near-infrared reflectance of vegetation (NIRv)-scaled approach was employed to mitigate the spatial difference between the locations of specific sites and the matched TROPOSIF samples using Sentinel-2 imagery. Other issues related to retrival methods and instrument differences were examined. Subsequently, the 3FLD retrieval method was chosed for the in situ data. The validation results showed that the three far-red TROPOSIF datasets exhibit slightly different performances in terms of the validation accuracy; the R2 for TROPOSIFCaltech, TROPOSIF735, and TROPOSIF743 was 0.43, 0.33 and 0.40, respectively, which is asociated with root-mean-square error(RMSE) values of 0.59, 0.42 and 0.57 mW m−2 sr−1 nm−1, respectively. However, red TROPOSIFCaltech exhibited no significant correlation with tower-based SIF with R2 of 0.02 and RMSE of 0.34 mW m−2 sr−1 nm−1. Furthermore, the validation results at different sites varied, with R2 ranging from 0.01 to 0.70. Uncertainties still exist in the validation of the four TROPOSIF datasets, which are attributed to some unresolved issues, such as the limited quality of in situ SIF retrievals and the spatial scaling difference. Thus, to fully utilize satellite-based SIF products for wide ranging applications, further improvements in SIF product quality are urgently required at both ground and satellite scales.},\n\turldate = {2023-10-16},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Du, Shanshan and Liu, Xinjie and Chen, Jidai and Duan, Weina and Liu, Liangyun},\n\tmonth = may,\n\tyear = {2023},\n\tkeywords = {Near-infrared reflectance of vegetation (NIRv), Solar-induced chlorophyll fluorescence (SIF), TROPOMI, Validation},\n\tpages = {113547},\n}\n\n

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\n Several satellite-based solar-induced chlorophyll fluorescence (SIF) products have progressively emerged and have been developed in recent years. However, till date, no direct validation has been conducted on existing satellite-based SIF products. In this study, validation of two groups of TROPOspheric Monitoring Instrument (TROPOMI) SIF products, namely TROPOSIFCaltech (containing far-red and red TROPOSIFCaltech datasets) and TROPOSIFESA (containing TROPOSIF735 and TROPOSIF743 datasets that are retrieved from two different retrieval windows), was conducted using tower-based SIF measurements over seven sites. Several issues and potential obstacles emerged while matching satellite-based and in situ SIF retrievals, including spatial scale mismatch. To overcome the spatial scale mismatch between the satellite data and ground observations, a near-infrared reflectance of vegetation (NIRv)-scaled approach was employed to mitigate the spatial difference between the locations of specific sites and the matched TROPOSIF samples using Sentinel-2 imagery. Other issues related to retrival methods and instrument differences were examined. Subsequently, the 3FLD retrieval method was chosed for the in situ data. The validation results showed that the three far-red TROPOSIF datasets exhibit slightly different performances in terms of the validation accuracy; the R2 for TROPOSIFCaltech, TROPOSIF735, and TROPOSIF743 was 0.43, 0.33 and 0.40, respectively, which is asociated with root-mean-square error(RMSE) values of 0.59, 0.42 and 0.57 mW m−2 sr−1 nm−1, respectively. However, red TROPOSIFCaltech exhibited no significant correlation with tower-based SIF with R2 of 0.02 and RMSE of 0.34 mW m−2 sr−1 nm−1. Furthermore, the validation results at different sites varied, with R2 ranging from 0.01 to 0.70. Uncertainties still exist in the validation of the four TROPOSIF datasets, which are attributed to some unresolved issues, such as the limited quality of in situ SIF retrievals and the spatial scaling difference. Thus, to fully utilize satellite-based SIF products for wide ranging applications, further improvements in SIF product quality are urgently required at both ground and satellite scales.\n

\n\n\n

\n \n\n \n \n \n \n \n \n A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands.\n \n \n \n \n\n\n \n Bartold, M.; and Kluczek, M.\n\n\n \n\n\n\n Remote Sensing, 15(9): 2392. January 2023.\n Number: 9 Publisher: Multidisciplinary Digital Publishing Institute\n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{bartold_machine_2023,\n\ttitle = {A {Machine} {Learning} {Approach} for {Mapping} {Chlorophyll} {Fluorescence} at {Inland} {Wetlands}},\n\tvolume = {15},\n\tcopyright = {http://creativecommons.org/licenses/by/3.0/},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/15/9/2392},\n\tdoi = {10.3390/rs15092392},\n\tabstract = {Wetlands are a critical component of the landscape for climate mitigation, adaptation, biodiversity, and human health and prosperity. Keeping an eye on wetland vegetation is crucial due to it playing a major role in the planet’s carbon cycle and ecosystem management. By measuring the chlorophyll fluorescence (ChF) emitted by plants, we can get a precise understanding of the current state and photosynthetic activity. In this study, we applied the Extreme Gradient Boost (XGBoost) algorithm to map ChF in the Biebrza Valley, which has a unique ecosystem in Europe for peatlands, as well as highly diversified flora and fauna. Our results revealed the advantages of using a set of classifiers derived from EO Sentinel-2 (S-2) satellite image mosaics to accurately map the spatio-temporal distribution of ChF in a terrestrial landscape. The validation proved that the XGBoost algorithm is quite accurate in estimating ChF with a good determination of 0.71 and least bias of 0.012. The precision of chlorophyll fluorescence measurements is reliant upon determining the optimal S-2 satellite overpass time, which is influenced by the developmental stage of the plants at various points during the growing season. Finally, the model performance results indicated that biophysical factors are characterized by greenness- and leaf-pigment-related spectral indices. However, utilizing vegetation indices based on extended periods of remote sensing data that better capture land phenology features can improve the accuracy of mapping chlorophyll fluorescence.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2023-10-16},\n\tjournal = {Remote Sensing},\n\tauthor = {Bartold, Maciej and Kluczek, Marcin},\n\tmonth = jan,\n\tyear = {2023},\n\tnote = {Number: 9\nPublisher: Multidisciplinary Digital Publishing Institute},\n\tkeywords = {Sentinel-2, biodiversity, chlorophyll fluorescence, machine learning, vegetation monitoring, wetlands},\n\tpages = {2392},\n}\n\n

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\n \n\n \n \n \n \n \n \n Synergy between TROPOMI sun-induced chlorophyll fluorescence and MODIS spectral reflectance for understanding the dynamics of gross primary productivity at Integrated Carbon Observatory System (ICOS) ecosystem flux sites.\n \n \n \n \n\n\n \n Balde, H.; Hmimina, G.; Goulas, Y.; Latouche, G.; and Soudani, K.\n\n\n \n\n\n\n Biogeosciences, 20(7): 1473–1490. April 2023.\n Publisher: Copernicus GmbH\n\n\n\n
\n\n\n\n \n \n $\"Synergy$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{balde_synergy_2023,\n\ttitle = {Synergy between {TROPOMI} sun-induced chlorophyll fluorescence and {MODIS} spectral reflectance for understanding the dynamics of gross primary productivity at {Integrated} {Carbon} {Observatory} {System} ({ICOS}) ecosystem flux sites},\n\tvolume = {20},\n\tissn = {1726-4170},\n\turl = {https://bg.copernicus.org/articles/20/1473/2023/},\n\tdoi = {10.5194/bg-20-1473-2023},\n\tabstract = {An accurate estimation of vegetation gross primary productivity (GPP), which is the amount of carbon taken up by vegetation through photosynthesis for a given time and area, is critical for understanding terrestrial–atmosphere CO2 exchange processes and ecosystem functioning, as well as ecosystem responses and adaptations to climate change. Prior studies, based on ground, airborne, and satellite sun-induced chlorophyll fluorescence (SIF) observations, have recently revealed close relationships with GPP at different spatial and temporal scales and across different plant functional types (PFTs). However, questions remain regarding whether there is a unique relationship between SIF and GPP across different sites and PFTs and how we can improve GPP estimates using solely remotely sensed data. Using concurrent measurements of daily TROPOspheric Monitoring Instrument (TROPOMI) SIF (daily SIFd); daily MODIS Terra and Aqua spectral reflectance; vegetation indices (VIs, notably normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRv), and photochemical reflectance index (PRI)); and daily tower-based GPP across eight major different PFTs, including mixed forests, deciduous broadleaf forests, croplands, evergreen broadleaf forests, evergreen needleleaf forests, grasslands, open shrubland, and wetlands, the strength of the relationships between tower-based GPP and SIFd at 40 Integrated Carbon Observation System (ICOS) flux sites was investigated. The synergy between SIFd and MODIS-based reflectance (R) and VIs to improve GPP estimates using a data-driven modeling approach was also evaluated. The results revealed that the strength of the hyperbolic relationship between GPP and SIFd was strongly site-specific and PFT-dependent. Furthermore, the generalized linear model (GLM), fitted between SIFd, GPP, and site and vegetation type as categorical variables, further supported this site- and PFT-dependent relationship between GPP and SIFd. Using random forest (RF) regression models with GPP as output and the aforementioned variables as predictors (R, SIFd, and VIs), this study also showed that the spectral reflectance bands (RF-R) and SIFd plus spectral reflectance (RF-SIF-R) models explained over 80 \\% of the seasonal and interannual variations in GPP, whereas the SIFd plus VI (RF-SIF-VI) model reproduced only 75 \\% of the tower-based GPP variance. In addition, the relative variable importance of predictors of GPP demonstrated that the spectral reflectance bands in the near-infrared, red, and SIFd appeared as the most influential and dominant factors determining GPP predictions, indicating the importance of canopy structure, biochemical properties, and vegetation functioning on GPP estimates. Overall, this study provides insights into understanding the strength of the relationships between GPP and SIF and the use of spectral reflectance and SIFd to improve estimates of GPP across sites and PFTs.},\n\tlanguage = {English},\n\tnumber = {7},\n\turldate = {2023-10-16},\n\tjournal = {Biogeosciences},\n\tauthor = {Balde, Hamadou and Hmimina, Gabriel and Goulas, Yves and Latouche, Gwendal and Soudani, Kamel},\n\tmonth = apr,\n\tyear = {2023},\n\tnote = {Publisher: Copernicus GmbH},\n\tpages = {1473--1490},\n}\n

\n\n\n

\n An accurate estimation of vegetation gross primary productivity (GPP), which is the amount of carbon taken up by vegetation through photosynthesis for a given time and area, is critical for understanding terrestrial–atmosphere CO2 exchange processes and ecosystem functioning, as well as ecosystem responses and adaptations to climate change. Prior studies, based on ground, airborne, and satellite sun-induced chlorophyll fluorescence (SIF) observations, have recently revealed close relationships with GPP at different spatial and temporal scales and across different plant functional types (PFTs). However, questions remain regarding whether there is a unique relationship between SIF and GPP across different sites and PFTs and how we can improve GPP estimates using solely remotely sensed data. Using concurrent measurements of daily TROPOspheric Monitoring Instrument (TROPOMI) SIF (daily SIFd); daily MODIS Terra and Aqua spectral reflectance; vegetation indices (VIs, notably normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRv), and photochemical reflectance index (PRI)); and daily tower-based GPP across eight major different PFTs, including mixed forests, deciduous broadleaf forests, croplands, evergreen broadleaf forests, evergreen needleleaf forests, grasslands, open shrubland, and wetlands, the strength of the relationships between tower-based GPP and SIFd at 40 Integrated Carbon Observation System (ICOS) flux sites was investigated. The synergy between SIFd and MODIS-based reflectance (R) and VIs to improve GPP estimates using a data-driven modeling approach was also evaluated. The results revealed that the strength of the hyperbolic relationship between GPP and SIFd was strongly site-specific and PFT-dependent. Furthermore, the generalized linear model (GLM), fitted between SIFd, GPP, and site and vegetation type as categorical variables, further supported this site- and PFT-dependent relationship between GPP and SIFd. Using random forest (RF) regression models with GPP as output and the aforementioned variables as predictors (R, SIFd, and VIs), this study also showed that the spectral reflectance bands (RF-R) and SIFd plus spectral reflectance (RF-SIF-R) models explained over 80 % of the seasonal and interannual variations in GPP, whereas the SIFd plus VI (RF-SIF-VI) model reproduced only 75 % of the tower-based GPP variance. In addition, the relative variable importance of predictors of GPP demonstrated that the spectral reflectance bands in the near-infrared, red, and SIFd appeared as the most influential and dominant factors determining GPP predictions, indicating the importance of canopy structure, biochemical properties, and vegetation functioning on GPP estimates. Overall, this study provides insights into understanding the strength of the relationships between GPP and SIF and the use of spectral reflectance and SIFd to improve estimates of GPP across sites and PFTs.\n

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\n \n 2022\n \n \n (6)\n \n \n

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\n \n\n \n \n \n \n \n \n FluoCat: A cable-suspended multi-sensor system for the vegetation SIF Cal/Val monitoring and estimation of effective sunlit surface fluorescence \\textbar Elsevier Enhanced Reader.\n \n \n \n \n\n\n \n Moncholi-Estornell, A.; Van Wittenberghe, S.; Cendrero-Mateo, M. P.; Alonso, L.; Jiménez, M.; Urrego, P.; Mac Arthur, A.; and Moreno, J.\n\n\n \n\n\n\n International Journal of Applied Earth Observation and Geoinformation, 116(2023): 103147. September 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"FluoCat:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{moncholi-estornell_fluocat_2022,\n\ttitle = {{FluoCat}: {A} cable-suspended multi-sensor system for the vegetation {SIF} {Cal}/{Val} monitoring and estimation of effective sunlit surface fluorescence {\\textbar} {Elsevier} {Enhanced} {Reader}},\n\tvolume = {116},\n\tshorttitle = {{FluoCat}},\n\turl = {https://reader.elsevier.com/reader/sd/pii/S1569843222003351?token=44F46F0CAC53B6DDDCE7CBBA01EF4AB38D78E0D067B6DFE16EE10168BBE7193F9430D2ADC6C2C7DDEF11C31836BF8D37&originRegion=eu-west-1&originCreation=20221212075540},\n\tdoi = {https://doi.org/10.1016/j.jag.2022.103147},\n\tabstract = {With the upcoming Fluorescence Explorer (FLEX) satellite mission from the European Space Agency, vegetation\nfluorescence (650–780 nm) will become available at 300x300 m resolution. Calibration and validation strategies\nof the fluorescence (F) signal remain however challenging, due to (1) the radiometric subtlety of the signal, (2)\nthe multiple entangled drivers of the signal in space and in time, and (3) the need of a spatially representative\nacquisition, considering the previous two points. To tackle these challenges, the present work introduces the\nFluoCat, a cable-suspended system for the proximal sensing indirect measurement of solar-induced fluorescence,\nmounted across an agricultural field, covering a 60-m transect. On board the FluoCat are mounted: a high-\nspectral resolution Piccolo Doppio dual spectrometer system, a MAIA-S2 multispectral camera and a TeAx\nThermal Capture Fusion camera, which can be triggered simultaneously according to a pre-set protocol.\nIn order to test the system, two protocols were evaluated, a point-wise protocol, stopping at a pre-determined\npoints to acquire the measurements, and the swiping protocol, acquiring measurements while in movement along\nthe transect. Taking as a reference the values obtained with the swiping protocol, which captures the higher\nspatial variability, it was found that to achieve an averaged mean absolute percentage error (MAPE) below 2 \\%\nwithin between the spectral range of 500–800 nm, it is required a minimum of 6 sampling points to characterize\nthe spectral variability of the 40-m melon crop transect.\nFurther, by combining the fluorescence products of the Piccolo system normalized by PAR (NormF687,\nNormF760) and the fractional cover of sunlit vegetation (FVC Sunlit) obtained from the MAIA, we developed a\nmulti-sensor product, i.e., the ‘sunlit green F’ for both retrieved bands. This synergy product improved the\nestimation of the effective surface fluorescence flux, with the leaf fluorescence emission as reference, by reducing\nthe errors from 36 \\% to 18 \\% (band 687 nm); and from 24 \\% to 6 \\% (band 760 nm).},\n\tlanguage = {en},\n\tnumber = {2023},\n\turldate = {2022-12-12},\n\tjournal = {International Journal of Applied Earth Observation and Geoinformation},\n\tauthor = {Moncholi-Estornell, Adrián and Van Wittenberghe, S. and Cendrero-Mateo, Maria Pilar and Alonso, Luis and Jiménez, Marco and Urrego, Patricia and Mac Arthur, Alasdair and Moreno, José},\n\tmonth = sep,\n\tyear = {2022},\n\tdoi = {10.1016/j.jag.2022.103147},\n\tpages = {103147},\n}\n\n

\n\n\n

\n With the upcoming Fluorescence Explorer (FLEX) satellite mission from the European Space Agency, vegetation fluorescence (650–780 nm) will become available at 300x300 m resolution. Calibration and validation strategies of the fluorescence (F) signal remain however challenging, due to (1) the radiometric subtlety of the signal, (2) the multiple entangled drivers of the signal in space and in time, and (3) the need of a spatially representative acquisition, considering the previous two points. To tackle these challenges, the present work introduces the FluoCat, a cable-suspended system for the proximal sensing indirect measurement of solar-induced fluorescence, mounted across an agricultural field, covering a 60-m transect. On board the FluoCat are mounted: a high- spectral resolution Piccolo Doppio dual spectrometer system, a MAIA-S2 multispectral camera and a TeAx Thermal Capture Fusion camera, which can be triggered simultaneously according to a pre-set protocol. In order to test the system, two protocols were evaluated, a point-wise protocol, stopping at a pre-determined points to acquire the measurements, and the swiping protocol, acquiring measurements while in movement along the transect. Taking as a reference the values obtained with the swiping protocol, which captures the higher spatial variability, it was found that to achieve an averaged mean absolute percentage error (MAPE) below 2 % within between the spectral range of 500–800 nm, it is required a minimum of 6 sampling points to characterize the spectral variability of the 40-m melon crop transect. Further, by combining the fluorescence products of the Piccolo system normalized by PAR (NormF687, NormF760) and the fractional cover of sunlit vegetation (FVC Sunlit) obtained from the MAIA, we developed a multi-sensor product, i.e., the ‘sunlit green F’ for both retrieved bands. This synergy product improved the estimation of the effective surface fluorescence flux, with the leaf fluorescence emission as reference, by reducing the errors from 36 % to 18 % (band 687 nm); and from 24 % to 6 % (band 760 nm).\n

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\n \n\n \n \n \n \n \n \n The physiological basis for estimating photosynthesis from Chla fluorescence.\n \n \n \n \n\n\n \n Han, J.; Chang, C. Y.; Gu, L.; Zhang, Y.; Meeker, E. W.; Magney, T. S.; Walker, A. P.; Wen, J.; Kira, O.; McNaull, S.; and Sun, Y.\n\n\n \n\n\n\n New Phytologist, 234(4): 1206–1219. 2022.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/nph.18045\n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{han_physiological_2022,\n\ttitle = {The physiological basis for estimating photosynthesis from {Chla} fluorescence},\n\tvolume = {234},\n\tcopyright = {© 2022 The Authors. New Phytologist © 2022 New Phytologist Foundation. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.},\n\tissn = {1469-8137},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1111/nph.18045},\n\tdoi = {10.1111/nph.18045},\n\tabstract = {Solar-induced Chl fluorescence (SIF) offers the potential to curb large uncertainties in the estimation of photosynthesis across biomes and climates, and at different spatiotemporal scales. However, it remains unclear how SIF should be used to mechanistically estimate photosynthesis. In this study, we built a quantitative framework for the estimation of photosynthesis, based on a mechanistic light reaction model with the Chla fluorescence of Photosystem II (SIFPSII) as an input (MLR-SIF). Utilizing 29 C3 and C4 plant species that are representative of major plant biomes across the globe, we confirmed the validity of this framework at the leaf level. The MLR-SIF model is capable of accurately reproducing photosynthesis for all C3 and C4 species under diverse light, temperature, and CO2 conditions. We further tested the robustness of the MLR-SIF model using Monte Carlo simulations, and found that photosynthesis estimates were much less sensitive to parameter uncertainties relative to the conventional Farquhar, von Caemmerer, Berry (FvCB) model because of the additional independent information contained in SIFPSII. Once inferred from direct observables of SIF, SIFPSII provides ‘parameter savings’ to the MLR-SIF model, compared to the mechanistically equivalent FvCB model, and thus avoids the uncertainties arising as a result of imperfect model parameterization. Our findings set the stage for future efforts to employ SIF mechanistically to improve photosynthesis estimates across a variety of scales, functional groups, and environmental conditions.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-10-16},\n\tjournal = {New Phytologist},\n\tauthor = {Han, Jimei and Chang, Christine Y-Y. and Gu, Lianhong and Zhang, Yongjiang and Meeker, Eliot W. and Magney, Troy S. and Walker, Anthony P. and Wen, Jiaming and Kira, Oz and McNaull, Sarah and Sun, Ying},\n\tyear = {2022},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/nph.18045},\n\tkeywords = {nonphotochemical quenching (NPQ), parameter uncertainty, photosynthesis model, redox state of PSII reaction centers, solar-induced chlorophyll fluorescence (SIF)},\n\tpages = {1206--1219},\n}\n\n

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\n \n\n \n \n \n \n \n \n Global Carbon Budget 2022.\n \n \n \n \n\n\n \n Friedlingstein, P.; O'Sullivan, M.; Jones, M. W.; Andrew, R. M.; Gregor, L.; Hauck, J.; Le Quéré, C.; Luijkx, I. T.; Olsen, A.; Peters, G. P.; Peters, W.; Pongratz, J.; Schwingshackl, C.; Sitch, S.; Canadell, J. G.; Ciais, P.; Jackson, R. B.; Alin, S. R.; Alkama, R.; Arneth, A.; Arora, V. K.; Bates, N. R.; Becker, M.; Bellouin, N.; Bittig, H. C.; Bopp, L.; Chevallier, F.; Chini, L. P.; Cronin, M.; Evans, W.; Falk, S.; Feely, R. A.; Gasser, T.; Gehlen, M.; Gkritzalis, T.; Gloege, L.; Grassi, G.; Gruber, N.; Gürses, Ö.; Harris, I.; Hefner, M.; Houghton, R. A.; Hurtt, G. C.; Iida, Y.; Ilyina, T.; Jain, A. K.; Jersild, A.; Kadono, K.; Kato, E.; Kennedy, D.; Klein Goldewijk, K.; Knauer, J.; Korsbakken, J. I.; Landschützer, P.; Lefèvre, N.; Lindsay, K.; Liu, J.; Liu, Z.; Marland, G.; Mayot, N.; McGrath, M. J.; Metzl, N.; Monacci, N. M.; Munro, D. R.; Nakaoka, S.; Niwa, Y.; O'Brien, K.; Ono, T.; Palmer, P. I.; Pan, N.; Pierrot, D.; Pocock, K.; Poulter, B.; Resplandy, L.; Robertson, E.; Rödenbeck, C.; Rodriguez, C.; Rosan, T. M.; Schwinger, J.; Séférian, R.; Shutler, J. D.; Skjelvan, I.; Steinhoff, T.; Sun, Q.; Sutton, A. J.; Sweeney, C.; Takao, S.; Tanhua, T.; Tans, P. P.; Tian, X.; Tian, H.; Tilbrook, B.; Tsujino, H.; Tubiello, F.; van der Werf, G. R.; Walker, A. P.; Wanninkhof, R.; Whitehead, C.; Willstrand Wranne, A.; Wright, R.; Yuan, W.; Yue, C.; Yue, X.; Zaehle, S.; Zeng, J.; and Zheng, B.\n\n\n \n\n\n\n Earth System Science Data, 14(11): 4811–4900. November 2022.\n Publisher: Copernicus GmbH\n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{friedlingstein_global_2022,\n\ttitle = {Global {Carbon} {Budget} 2022},\n\tvolume = {14},\n\tissn = {1866-3508},\n\turl = {https://essd.copernicus.org/articles/14/4811/2022/},\n\tdoi = {10.5194/essd-14-4811-2022},\n\tabstract = {Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ.\n\n For the year 2021, EFOS increased by 5.1 \\% relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1 (40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9 ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with a BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of +1.0 \\% (0.1 \\% to 1.9 \\%) globally and atmospheric CO2 concentration reaching 417.2 ppm, more than 50 \\% above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).},\n\tlanguage = {English},\n\tnumber = {11},\n\turldate = {2023-10-16},\n\tjournal = {Earth System Science Data},\n\tauthor = {Friedlingstein, Pierre and O'Sullivan, Michael and Jones, Matthew W. and Andrew, Robbie M. and Gregor, Luke and Hauck, Judith and Le Quéré, Corinne and Luijkx, Ingrid T. and Olsen, Are and Peters, Glen P. and Peters, Wouter and Pongratz, Julia and Schwingshackl, Clemens and Sitch, Stephen and Canadell, Josep G. and Ciais, Philippe and Jackson, Robert B. and Alin, Simone R. and Alkama, Ramdane and Arneth, Almut and Arora, Vivek K. and Bates, Nicholas R. and Becker, Meike and Bellouin, Nicolas and Bittig, Henry C. and Bopp, Laurent and Chevallier, Frédéric and Chini, Louise P. and Cronin, Margot and Evans, Wiley and Falk, Stefanie and Feely, Richard A. and Gasser, Thomas and Gehlen, Marion and Gkritzalis, Thanos and Gloege, Lucas and Grassi, Giacomo and Gruber, Nicolas and Gürses, Özgür and Harris, Ian and Hefner, Matthew and Houghton, Richard A. and Hurtt, George C. and Iida, Yosuke and Ilyina, Tatiana and Jain, Atul K. and Jersild, Annika and Kadono, Koji and Kato, Etsushi and Kennedy, Daniel and Klein Goldewijk, Kees and Knauer, Jürgen and Korsbakken, Jan Ivar and Landschützer, Peter and Lefèvre, Nathalie and Lindsay, Keith and Liu, Junjie and Liu, Zhu and Marland, Gregg and Mayot, Nicolas and McGrath, Matthew J. and Metzl, Nicolas and Monacci, Natalie M. and Munro, David R. and Nakaoka, Shin-Ichiro and Niwa, Yosuke and O'Brien, Kevin and Ono, Tsuneo and Palmer, Paul I. and Pan, Naiqing and Pierrot, Denis and Pocock, Katie and Poulter, Benjamin and Resplandy, Laure and Robertson, Eddy and Rödenbeck, Christian and Rodriguez, Carmen and Rosan, Thais M. and Schwinger, Jörg and Séférian, Roland and Shutler, Jamie D. and Skjelvan, Ingunn and Steinhoff, Tobias and Sun, Qing and Sutton, Adrienne J. and Sweeney, Colm and Takao, Shintaro and Tanhua, Toste and Tans, Pieter P. and Tian, Xiangjun and Tian, Hanqin and Tilbrook, Bronte and Tsujino, Hiroyuki and Tubiello, Francesco and van der Werf, Guido R. and Walker, Anthony P. and Wanninkhof, Rik and Whitehead, Chris and Willstrand Wranne, Anna and Wright, Rebecca and Yuan, Wenping and Yue, Chao and Yue, Xu and Zaehle, Sönke and Zeng, Jiye and Zheng, Bo},\n\tmonth = nov,\n\tyear = {2022},\n\tnote = {Publisher: Copernicus GmbH},\n\tpages = {4811--4900},\n}\n\n

\n\n\n

\n Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1 (40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9 ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with a BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).\n

\n\n\n

\n \n\n \n \n \n \n \n \n Leaf chlorophyll contents dominates the seasonal dynamics of SIF/GPP ratio: Evidence from continuous measurements in a maize field.\n \n \n \n \n\n\n \n Chen, R.; Liu, L.; and Liu, X.\n\n\n \n\n\n\n Agricultural and Forest Meteorology, 323: 109070. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Leaf$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{chen_leaf_2022,\n\ttitle = {Leaf chlorophyll contents dominates the seasonal dynamics of {SIF}/{GPP} ratio: {Evidence} from continuous measurements in a maize field},\n\tvolume = {323},\n\tissn = {0168-1923},\n\tshorttitle = {Leaf chlorophyll contents dominates the seasonal dynamics of {SIF}/{GPP} ratio},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0168192322002581},\n\tdoi = {10.1016/j.agrformet.2022.109070},\n\tabstract = {The coupling of solar-induced chlorophyll fluorescence (SIF) and gross primary production (GPP) is the foundation of SIF-based GPP estimations; however, the relationship between them varies in different conditions. Structural changes contribute much to the dynamics of their relationship at the canopy scale, whereas the role of physiological mechanisms is not very clear. Here, based on three-year continuous observations from 2018 to 2020 in a maize field in Northwest China, we obtained the total SIF (tSIF) at the photosystem scale and investigated the seasonal dynamics of its link with GPP. Using the ratio of tSIF to GPP, we eliminated the contribution of canopy structure and discovered an increase in the ratio during the late reproductive and ripening stages. Seasonal variation in the ratio was tracked by the leaf chlorophyll contents (LCC) related to the photosynthetic functional maturity (represented by maximum carboxylation rate, Vcmax). In addition, we also found that there was variation in the regression slope of the relationship between SIF/GPP and LCC at different growth stages. The correlation between tSIF/GPP and LCC was better than that between dSIF/GPP (dSIF, the ratio of directional SIF at canopy scale to GPP) and LCC, which demonstrated that the physiological information is reinforced after the elimination of structural contributions. Overall, the seasonal dynamics of the GPP–tSIF relationship in our study highlighted the necessity of considering the growing stage in SIF-based GPP estimations. Although they are usually covered up by the contribution of the canopy structure, physiological mechanisms still impacted the dynamics of the GPP–SIF relationship.},\n\turldate = {2023-10-16},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Chen, Ruonan and Liu, Liangyun and Liu, Xinjie},\n\tmonth = aug,\n\tyear = {2022},\n\tkeywords = {GPP, Physiological property, SIF, Seasonal variation},\n\tpages = {109070},\n}\n\n

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\n \n\n \n \n \n \n \n \n Global GOSAT, OCO-2, and OCO-3 solar-induced chlorophyll fluorescence datasets.\n \n \n \n \n\n\n \n Doughty, R.; Kurosu, T. P.; Parazoo, N.; Köhler, P.; Wang, Y.; Sun, Y.; and Frankenberg, C.\n\n\n \n\n\n\n Earth System Science Data, 14(4): 1513–1529. April 2022.\n Publisher: Copernicus GmbH\n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{doughty_global_2022,\n\ttitle = {Global {GOSAT}, {OCO}-2, and {OCO}-3 solar-induced chlorophyll fluorescence datasets},\n\tvolume = {14},\n\tissn = {1866-3508},\n\turl = {https://essd.copernicus.org/articles/14/1513/2022/},\n\tdoi = {10.5194/essd-14-1513-2022},\n\tabstract = {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.},\n\tlanguage = {English},\n\tnumber = {4},\n\turldate = {2023-10-16},\n\tjournal = {Earth System Science Data},\n\tauthor = {Doughty, Russell and Kurosu, Thomas P. and Parazoo, Nicholas and Köhler, Philipp and Wang, Yujie and Sun, Ying and Frankenberg, Christian},\n\tmonth = apr,\n\tyear = {2022},\n\tnote = {Publisher: Copernicus GmbH},\n\tpages = {1513--1529},\n}\n\n

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\n \n\n \n \n \n \n \n \n Asynchrony of Spaceborne Chlorophyll Fluorescence and the Near Infrared Reflectance of Vegetation in Tropical Forests.\n \n \n \n \n\n\n \n Doughty, R.; Guanter, L.; Parazoo, N.; Joiner, J.; Magney, T.; Johnson, J. E.; Köhler, P.; Frankenberg, C.; Xiao, X.; Pierrat, Z.; Wang, Y.; Maguire, A.; Norton, A.; Somkuti, P.; Ma, S.; Qin, Y.; Turner, A. J.; Crowell, S.; and Moore, B.\n\n\n \n\n\n\n , 2022: B41G–01. December 2022.\n Conference Name: AGU Fall Meeting Abstracts ADS Bibcode: 2022AGUFM.B41G..01D\n\n\n\n
\n\n\n\n \n \n $\"Asynchrony$ Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{doughty_asynchrony_2022,\n\ttitle = {Asynchrony of {Spaceborne} {Chlorophyll} {Fluorescence} and the {Near} {Infrared} {Reflectance} of {Vegetation} in {Tropical} {Forests}},\n\tvolume = {2022},\n\turl = {https://ui.adsabs.harvard.edu/abs/2022AGUFM.B41G..01D},\n\tabstract = {Although spaceborne surface reflectance and vegetation indices have been used to model global photosynthesis, field investigations have found photosynthesis and photosynthetic capacity to be asynchronous with vegetation indices in Amazon evergreen broadleaf forest. Similarly, we previously found a lack or weak correlation between solar-induced chlorophyll fluorescence (SIF) and vegetation indices in the tropics. This investigation was complicated by spatio-temporal differences in data acquisition by the two sensors used, TROPOMI and MODIS. Thus, it remains unknown if surface reflectance or vegetation indices are synchronous with SIF in the tropics and to what extent the observations of each are influenced by clouds and sun-sensor geometry. Here, we use coincident TROPOMI soundings of SIF, red and near-infrared reflectance, and radiance in tropical evergreen broadleaf forest to (1) determine whether the near-infrared reflectance and radiance of vegetation (NIRvRef and NIRvRad, respectively) are synchronous with SIF in tropical forests, including under clear-sky and cloudy conditions or when excluding hotspot observations ({\\textless} 0.20° phase angle); (2) explore the effect of including hotspot observations, cloudy observations, and both hotspot and cloudy observations on SIF, NIRvRef, NIRvRad, red reflectance, and NIR reflectance; (3) evaluate the relationship between phase angle and SIF, NIRvRef, NIRvRad, red reflectance, and NIR reflectance; and (4) compare SIF, NIRvRef, and NIRvRad to eddy covariance gross ecosystem productivity and photosynthetic capacity at four tropical forest sites in the Amazon. We find SIF to be asynchronous with NIRvRef and NIRvRad in the Amazon, but synchronous in Africa and Asia-Pacific. SIF is less sensitive to cloud cover and phase angle than NIRvRef and NIRvRad, which are complicated by the disproportionate effects of cloud cover and sun-sensor geometry on red and near-infrared reflectance. Contrary to NIRvRef and NIRvRad, we found SIF to be synchronous with eddy-covariance gross ecosystem productivity and photosynthetic capacity in Amazon evergreen broadleaf forest. Our results advance our understanding of the relationship between SIF, red and near-infrared surface reflectance, NIRvRef, and NIRvRad, and inform future global modeling of photosynthesis.},\n\turldate = {2023-10-16},\n\tauthor = {Doughty, Russell and Guanter, Luis and Parazoo, Nicholas and Joiner, Joanna and Magney, Troy and Johnson, Jennifer E. and Köhler, Philipp and Frankenberg, Christian and Xiao, Xiangming and Pierrat, Zoe and Wang, Yujie and Maguire, Andrew and Norton, Alexander and Somkuti, Peter and Ma, Shuang and Qin, Yuanwei and Turner, Alexander J. and Crowell, Sean and Moore, Berrien},\n\tmonth = dec,\n\tyear = {2022},\n\tnote = {Conference Name: AGU Fall Meeting Abstracts\nADS Bibcode: 2022AGUFM.B41G..01D},\n\tpages = {B41G--01},\n}\n\n

\n\n\n

\n Although spaceborne surface reflectance and vegetation indices have been used to model global photosynthesis, field investigations have found photosynthesis and photosynthetic capacity to be asynchronous with vegetation indices in Amazon evergreen broadleaf forest. Similarly, we previously found a lack or weak correlation between solar-induced chlorophyll fluorescence (SIF) and vegetation indices in the tropics. This investigation was complicated by spatio-temporal differences in data acquisition by the two sensors used, TROPOMI and MODIS. Thus, it remains unknown if surface reflectance or vegetation indices are synchronous with SIF in the tropics and to what extent the observations of each are influenced by clouds and sun-sensor geometry. Here, we use coincident TROPOMI soundings of SIF, red and near-infrared reflectance, and radiance in tropical evergreen broadleaf forest to (1) determine whether the near-infrared reflectance and radiance of vegetation (NIRvRef and NIRvRad, respectively) are synchronous with SIF in tropical forests, including under clear-sky and cloudy conditions or when excluding hotspot observations (\\textless 0.20° phase angle); (2) explore the effect of including hotspot observations, cloudy observations, and both hotspot and cloudy observations on SIF, NIRvRef, NIRvRad, red reflectance, and NIR reflectance; (3) evaluate the relationship between phase angle and SIF, NIRvRef, NIRvRad, red reflectance, and NIR reflectance; and (4) compare SIF, NIRvRef, and NIRvRad to eddy covariance gross ecosystem productivity and photosynthetic capacity at four tropical forest sites in the Amazon. We find SIF to be asynchronous with NIRvRef and NIRvRad in the Amazon, but synchronous in Africa and Asia-Pacific. SIF is less sensitive to cloud cover and phase angle than NIRvRef and NIRvRad, which are complicated by the disproportionate effects of cloud cover and sun-sensor geometry on red and near-infrared reflectance. Contrary to NIRvRef and NIRvRad, we found SIF to be synchronous with eddy-covariance gross ecosystem productivity and photosynthetic capacity in Amazon evergreen broadleaf forest. Our results advance our understanding of the relationship between SIF, red and near-infrared surface reflectance, NIRvRef, and NIRvRad, and inform future global modeling of photosynthesis.\n

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\n \n 2021\n \n \n (7)\n \n \n

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\n \n\n \n \n \n \n \n \n Global High-Resolution Estimates of SIF from Fused SCIAMACHY and GOME-2, 2002-2018.\n \n \n \n \n\n\n \n Wen, J.; Koehler, P.; Duveiller, G.; Parazoo, N. C.; Magney, T.; Hooker, G.; Yu, L.; Chang, C. Y.; and Sun, Y.\n\n\n \n\n\n\n ORNL DAAC. June 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wen_global_2021,\n\ttitle = {Global {High}-{Resolution} {Estimates} of {SIF} from {Fused} {SCIAMACHY} and {GOME}-2, 2002-2018},\n\turl = {https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1864},\n\tdoi = {10.3334/ORNLDAAC/1864},\n\tabstract = {ORNL DAAC: This dataset provides global solar-induced chlorophyll fluorescence (SIF) estimates at a 0.05-degree resolution (approximately 5 km at the equator) for each month from August 2002 through December 2018. SIF data (740 nm) was retrieved from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) and Global Ozone Monitoring Experiment 2 (GOME-2) instruments onboard the MetOp-A satellite and was downscaled to 0.05 degree using Random Forest algorithm and harmonized with cumulative distribution function (CDF) matching technique. The uncertainty of the SIF estimates was also quantified and is provided. Validation of the harmonized product showed that it retained high spatial and temporal consistency with the original SCIAMACHY and GOME-2 SIF retrievals and had good correlations with independent airborne and ground-based SIF measurements. The dataset will inform on the synergy between satellite SIF and photosynthesis and research on drought, yield estimation, and land degradation evaluation.},\n\tlanguage = {en-US},\n\turldate = {2023-10-16},\n\tjournal = {ORNL DAAC},\n\tauthor = {Wen, J. and Koehler, P. and Duveiller, G. and Parazoo, N. C. and Magney, T. and Hooker, G. and Yu, L. and Chang, C. Y. and Sun, Y.},\n\tmonth = jun,\n\tyear = {2021},\n}\n\n

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\n\n\n

\n \n\n \n \n \n \n \n \n Challenges in the atmospheric characterization for the retrieval of spectrally resolved fluorescence and PRI region dynamics from space.\n \n \n \n \n\n\n \n Sabater, N.; Kolmonen, P.; Van Wittenberghe, S.; Arola, A.; and Moreno, J.\n\n\n \n\n\n\n Remote Sensing of Environment, 254: 112226. March 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Challenges$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{sabater_challenges_2021,\n\ttitle = {Challenges in the atmospheric characterization for the retrieval of spectrally resolved fluorescence and {PRI} region dynamics from space},\n\tvolume = {254},\n\tissn = {0034-4257},\n\turl = {https://www.sciencedirect.com/science/article/pii/S003442572030599X},\n\tdoi = {10.1016/j.rse.2020.112226},\n\tabstract = {In the coming years, Earth Observation missions like the FLuorescence EXplorer (FLEX) will acquire the radiance signal from the visible to the near-infrared at a very high spectral resolution, enabling exciting prospects for new insights in satellite-based photosynthetic studies. In this context, the process of de-coupling atmospheric and vegetation-related spectral signatures will become essential to guarantee a reliable estimation of the vegetation photosynthetic activity from space. Dynamic changes related to the vegetation photosynthetic status result in subtle contributions to the top of atmosphere radiance signal, e.g. due to the emission of the solar-induced chlorophyll fluorescence ({\\textasciitilde} 650–800 nm) or due to changes in surface reflectance spectra (500–600 nm) indicating variations in the vegetation photoprotection and light use efficiency. Conversely, atmospheric effects (molecular and aerosol absorption and scattering) dominate the spectral interval of interest for vegetation studies. This article presents a comprehensive overview of the atmospheric radiative effects caused by aerosols, ozone (O3), water vapor (H2O), oxygen (O2), and atmospheric pressure and temperature changes within the visible and near-infrared spectral interval, and paying special attention to the co-occurring vegetation-related spectral changes associated with the fluorescence emission and the activation of the photoprotection mechanisms. Since the largest uncertainties in the atmospheric correction process are associated with the characterization of the aerosol radiative effects, this work largely concentrates on the satellite retrieval-related implications under different aerosol absorbing and scattering scenarios on a global scale. Through a simulation exercise, it is evaluated to what extent aerosol climatology could influence the accuracy of satellite-derived surface apparent reflectance spectra impacting; therefore, any vegetation-related satellite product on a seasonal and global scale.},\n\tlanguage = {en},\n\turldate = {2023-04-05},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Sabater, Neus and Kolmonen, Pekka and Van Wittenberghe, Shari and Arola, Antti and Moreno, José},\n\tmonth = mar,\n\tyear = {2021},\n\tkeywords = {Aerosol climatology, Aerosol optical properties, FLORIS, FLuorescence EXplorer (FLEX) mission, Photochemical reflectance index, Photoprotection, Sentinel-3, Solar-induced chlorophyll fluorescence},\n\tpages = {112226},\n}\n\n

\n\n\n

\n In the coming years, Earth Observation missions like the FLuorescence EXplorer (FLEX) will acquire the radiance signal from the visible to the near-infrared at a very high spectral resolution, enabling exciting prospects for new insights in satellite-based photosynthetic studies. In this context, the process of de-coupling atmospheric and vegetation-related spectral signatures will become essential to guarantee a reliable estimation of the vegetation photosynthetic activity from space. Dynamic changes related to the vegetation photosynthetic status result in subtle contributions to the top of atmosphere radiance signal, e.g. due to the emission of the solar-induced chlorophyll fluorescence (~ 650–800 nm) or due to changes in surface reflectance spectra (500–600 nm) indicating variations in the vegetation photoprotection and light use efficiency. Conversely, atmospheric effects (molecular and aerosol absorption and scattering) dominate the spectral interval of interest for vegetation studies. This article presents a comprehensive overview of the atmospheric radiative effects caused by aerosols, ozone (O3), water vapor (H2O), oxygen (O2), and atmospheric pressure and temperature changes within the visible and near-infrared spectral interval, and paying special attention to the co-occurring vegetation-related spectral changes associated with the fluorescence emission and the activation of the photoprotection mechanisms. Since the largest uncertainties in the atmospheric correction process are associated with the characterization of the aerosol radiative effects, this work largely concentrates on the satellite retrieval-related implications under different aerosol absorbing and scattering scenarios on a global scale. Through a simulation exercise, it is evaluated to what extent aerosol climatology could influence the accuracy of satellite-derived surface apparent reflectance spectra impacting; therefore, any vegetation-related satellite product on a seasonal and global scale.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Unraveling the physical and physiological basis for the solar- induced chlorophyll fluorescence and photosynthesis relationship using continuous leaf and canopy measurements of a corn crop.\n \n \n \n \n\n\n \n Yang, P.; van der Tol, C.; Campbell, P. K. E.; and Middleton, E. M.\n\n\n \n\n\n\n Biogeosciences, 18(2): 441–465. January 2021.\n Publisher: Copernicus GmbH\n\n\n\n
\n\n\n\n \n \n $\"Unraveling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{yang_unraveling_2021,\n\ttitle = {Unraveling the physical and physiological basis for the solar- induced chlorophyll fluorescence and photosynthesis relationship using continuous leaf and canopy measurements of a corn crop},\n\tvolume = {18},\n\tissn = {1726-4170},\n\turl = {https://bg.copernicus.org/articles/18/441/2021/},\n\tdoi = {10.5194/bg-18-441-2021},\n\tabstract = {Estimates of the gross terrestrial carbon uptake exhibit large uncertainties. Sun-induced chlorophyll fluorescence (SIF) has an apparent near-linear relationship with gross primary production (GPP). This relationship will potentially facilitate the monitoring of photosynthesis from space. However, the exact mechanistic connection between SIF and GPP is still not clear. To explore the physical and physiological basis for their relationship, we used a unique data set comprising continuous field measurements of leaf and canopy fluorescence and photosynthesis of corn over a growing season. We found that, at canopy scale, the positive relationship between SIF and GPP was dominated by absorbed photosynthetically active radiation (APAR), which was equally affected by variations in incoming radiation and changes in canopy structure. After statistically controlling these underlying physical effects, the remaining correlation between far-red SIF and GPP due solely to the functional link between fluorescence and photosynthesis at the photochemical level was much weaker (ρ=0.30). Active leaf level fluorescence measurements revealed a moderate positive correlation between the efficiencies of fluorescence emission and photochemistry for sunlit leaves in well-illuminated conditions but a weak negative correlation in the low-light condition, which was negligible for shaded leaves. Differentiating sunlit and shaded leaves in the light use efficiency (LUE) models for SIF and GPP facilitates a better understanding of the SIF–GPP relationship at different environmental and canopy conditions. Leaf level fluorescence measurements also demonstrated that the sustained thermal dissipation efficiency dominated the seasonal energy partitioning, while the reversible heat dissipation dominated the diurnal leaf energy partitioning. These diurnal and seasonal variations in heat dissipation underlie, and are thus responsible for, the observed remote-sensing-based link between far-red SIF and GPP.},\n\tlanguage = {English},\n\tnumber = {2},\n\turldate = {2023-10-16},\n\tjournal = {Biogeosciences},\n\tauthor = {Yang, Peiqi and van der Tol, Christiaan and Campbell, Petya K. E. and Middleton, Elizabeth M.},\n\tmonth = jan,\n\tyear = {2021},\n\tnote = {Publisher: Copernicus GmbH},\n\tpages = {441--465},\n}\n\n

\n\n\n

\n Estimates of the gross terrestrial carbon uptake exhibit large uncertainties. Sun-induced chlorophyll fluorescence (SIF) has an apparent near-linear relationship with gross primary production (GPP). This relationship will potentially facilitate the monitoring of photosynthesis from space. However, the exact mechanistic connection between SIF and GPP is still not clear. To explore the physical and physiological basis for their relationship, we used a unique data set comprising continuous field measurements of leaf and canopy fluorescence and photosynthesis of corn over a growing season. We found that, at canopy scale, the positive relationship between SIF and GPP was dominated by absorbed photosynthetically active radiation (APAR), which was equally affected by variations in incoming radiation and changes in canopy structure. After statistically controlling these underlying physical effects, the remaining correlation between far-red SIF and GPP due solely to the functional link between fluorescence and photosynthesis at the photochemical level was much weaker (ρ=0.30). Active leaf level fluorescence measurements revealed a moderate positive correlation between the efficiencies of fluorescence emission and photochemistry for sunlit leaves in well-illuminated conditions but a weak negative correlation in the low-light condition, which was negligible for shaded leaves. Differentiating sunlit and shaded leaves in the light use efficiency (LUE) models for SIF and GPP facilitates a better understanding of the SIF–GPP relationship at different environmental and canopy conditions. Leaf level fluorescence measurements also demonstrated that the sustained thermal dissipation efficiency dominated the seasonal energy partitioning, while the reversible heat dissipation dominated the diurnal leaf energy partitioning. These diurnal and seasonal variations in heat dissipation underlie, and are thus responsible for, the observed remote-sensing-based link between far-red SIF and GPP.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Partitioning Net Ecosystem Exchange (NEE) of CO2 Using Solar-Induced Chlorophyll Fluorescence (SIF).\n \n \n \n \n\n\n \n Kira, O.; Y-Y. Chang, C.; Gu, L.; Wen, J.; Hong, Z.; and Sun, Y.\n\n\n \n\n\n\n Geophysical Research Letters, 48(4): e2020GL091247. 2021.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020GL091247\n\n\n\n
\n\n\n\n \n \n $\"Partitioning$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{kira_partitioning_2021,\n\ttitle = {Partitioning {Net} {Ecosystem} {Exchange} ({NEE}) of {CO2} {Using} {Solar}-{Induced} {Chlorophyll} {Fluorescence} ({SIF})},\n\tvolume = {48},\n\tcopyright = {© 2021. American Geophysical Union. All Rights Reserved.},\n\tissn = {1944-8007},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2020GL091247},\n\tdoi = {10.1029/2020GL091247},\n\tabstract = {Accurate partitioning of net ecosystem exchange (NEE) of CO2 to gross primary production (GPP) and ecosystem respiration (Reco) is crucial for understanding carbon cycle dynamics under changing climate. However, it remains as a long-standing problem in global ecology due to lack of independent constraining information for the two offsetting component fluxes. solar-induced chlorophyll fluorescence (SIF), a mechanistic proxy for photosynthesis, holds great promise to improve NEE partitioning by constraining GPP. We developed a parsimonious SIF-based approach for NEE partitioning and examined its performance using synthetic simulations and field measurements. This approach outperforms conventional approaches in reproducing simulated GPP and Reco, especially under high vapor pressure deficit. For field measurements, it results in lower daytime GPP and Reco than conventional approaches. This study made the first attempt to demonstrate SIF's potential for improving NEE partitioning accuracy and sets the stage for future efforts to examine its robustness and scalability under real-world environmental conditions.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-10-16},\n\tjournal = {Geophysical Research Letters},\n\tauthor = {Kira, O. and Y-Y. Chang, C. and Gu, L. and Wen, J. and Hong, Z. and Sun, Y.},\n\tyear = {2021},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020GL091247},\n\tkeywords = {GPP, NEE partitioning, Reco, SIF},\n\tpages = {e2020GL091247},\n}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n The TROPOSIF global sun-induced fluorescence dataset from the Sentinel-5P TROPOMI mission.\n \n \n \n \n\n\n \n Guanter, L.; Bacour, C.; Schneider, A.; Aben, I.; van Kempen, T. A.; Maignan, F.; Retscher, C.; Köhler, P.; Frankenberg, C.; Joiner, J.; and Zhang, Y.\n\n\n \n\n\n\n Earth System Science Data, 13(11): 5423–5440. November 2021.\n Publisher: Copernicus GmbH\n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{guanter_troposif_2021,\n\ttitle = {The {TROPOSIF} global sun-induced fluorescence dataset from the {Sentinel}-{5P} {TROPOMI} mission},\n\tvolume = {13},\n\tissn = {1866-3508},\n\turl = {https://essd.copernicus.org/articles/13/5423/2021/},\n\tdoi = {10.5194/essd-13-5423-2021},\n\tabstract = {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 spatiotemporal 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).},\n\tlanguage = {English},\n\tnumber = {11},\n\turldate = {2023-10-16},\n\tjournal = {Earth System Science Data},\n\tauthor = {Guanter, Luis and Bacour, Cédric and Schneider, Andreas and Aben, Ilse and van Kempen, Tim A. and Maignan, Fabienne and Retscher, Christian and Köhler, Philipp and Frankenberg, Christian and Joiner, Joanna and Zhang, Yongguang},\n\tmonth = nov,\n\tyear = {2021},\n\tnote = {Publisher: Copernicus GmbH},\n\tpages = {5423--5440},\n}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n L2 Solar-Induced Fluorescence (SIF) from SCIAMACHY, 2003-2012.\n \n \n \n \n\n\n \n Joiner, J.; Yoshida, Y.; Koehler, P.; Frankenberg, C.; and Parazoo, N. C.\n\n\n \n\n\n\n ORNL DAAC. August 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"L2$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{joiner_l2_2021,\n\ttitle = {L2 {Solar}-{Induced} {Fluorescence} ({SIF}) from {SCIAMACHY}, 2003-2012},\n\turl = {https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1871},\n\tdoi = {10.3334/ORNLDAAC/1871},\n\tabstract = {ORNL DAAC: This dataset provides Level 2 (L2) Solar-Induced Fluorescence (SIF) of chlorophyll estimates derived from the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) instrument on the European Space Agency's (ESA's) Environmental satellite (Envisat) with {\\textasciitilde}0.5 nm spectral resolution and wavelengths between 734 and 758 nm. SCIAMACHY covers global land between approximately 70 and -57 degrees latitude on an orbital basis at a resolution of approximately 30 km x 240 km. Data are provided for the period from 2003-01-01 to 2012-04-08. Each file contains daily raw and bias-adjusted solar-induced fluorescence along with quality control information and ancillary data.},\n\tlanguage = {en-US},\n\turldate = {2023-10-16},\n\tjournal = {ORNL DAAC},\n\tauthor = {Joiner, J. and Yoshida, Y. and Koehler, P. and Frankenberg, C. and Parazoo, N. C.},\n\tmonth = aug,\n\tyear = {2021},\n}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Practical approaches for normalizing directional solar-induced fluorescence to a standard viewing geometry.\n \n \n \n \n\n\n \n Hao, D.; Zeng, Y.; Qiu, H.; Biriukova, K.; Celesti, M.; Migliavacca, M.; Rossini, M.; Asrar, G. R.; and Chen, M.\n\n\n \n\n\n\n Remote Sensing of Environment, 255: 112171. March 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Practical$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{hao_practical_2021,\n\ttitle = {Practical approaches for normalizing directional solar-induced fluorescence to a standard viewing geometry},\n\tvolume = {255},\n\tissn = {0034-4257},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0034425720305447},\n\tdoi = {10.1016/j.rse.2020.112171},\n\tabstract = {Recent advances in remote sensing of solar-induced chlorophyll fluorescence (SIF) have improved the capabilities of monitoring large-scale Gross Primary Productivity (GPP). However, SIF observations are subject to directional effects which can lead to considerable uncertainties in various applications. Practical approaches for normalizing directional SIF observations to nadir viewing, to minimize the directional effects, have not been well studied. Here we developed two practical and physically-solid approaches for removing the directional effects of anisotropic SIF observations: one is based on near-infrared or red reflectance of vegetation (NIRv and Redv), and the other is based on the kernel-driven model with multi-angular SIF measurements. The first approach uses surface reflectance while the second approach directly leverages multi-angular SIF measurements. The performance of the two approaches was evaluated using a dataset of multi-angular measurements of SIF and reflectance collected with a high-resolution field spectrometer over different plant canopies. Results show that the relative mean absolute errors between the normalized nadir SIF and the observed SIF at nadir decrease by 3–6\\% (far-red) and 6–8\\% (red) for the first approach, and by 7–13\\% and 6–11\\% for the second approach, compared to the original data, respectively. The effectiveness and simplicity of our proposed approaches provide great potential to generate long-term and consistent SIF data records with minimized directional effects.},\n\turldate = {2023-10-16},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Hao, Dalei and Zeng, Yelu and Qiu, Han and Biriukova, Khelvi and Celesti, Marco and Migliavacca, Mirco and Rossini, Micol and Asrar, Ghassem R. and Chen, Min},\n\tmonth = mar,\n\tyear = {2021},\n\tkeywords = {Angular normalization, Directional effects, Kernel-driven model, Near-infrared reflectance of vegetation, Red reflectance of vegetation, Solar-induced chlorophyll fluorescence},\n\tpages = {112171},\n}\n\n

\n\n\n\n\n\n

\n\n

\n \n 2020\n \n \n (1)\n \n \n

\n \n \n

\n \n\n \n \n \n \n \n \n Solar-Induced Fluorescence Does Not Track Photosynthetic Carbon Assimilation Following Induced Stomatal Closure.\n \n \n \n \n\n\n \n Marrs, J. K.; Reblin, J. S.; Logan, B. A.; Allen, D. W.; Reinmann, A. B.; Bombard, D. M.; Tabachnik, D.; and Hutyra, L. R.\n\n\n \n\n\n\n Geophysical Research Letters, 47(15): e2020GL087956. 2020.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020GL087956\n\n\n\n
\n\n\n\n \n \n $\"Solar-Induced$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{marrs_solar-induced_2020,\n\ttitle = {Solar-{Induced} {Fluorescence} {Does} {Not} {Track} {Photosynthetic} {Carbon} {Assimilation} {Following} {Induced} {Stomatal} {Closure}},\n\tvolume = {47},\n\tcopyright = {©2020. American Geophysical Union. All Rights Reserved.},\n\tissn = {1944-8007},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2020GL087956},\n\tdoi = {10.1029/2020GL087956},\n\tabstract = {Since 2006, six satellites measuring solar-induced chlorophyll fluorescence (SIF) have been launched to better constrain terrestrial gross primary productivity (GPP). The promise of the SIF signal as a proxy for photosynthesis with a strong relationship to GPP has been widely cited in carbon cycling studies. However, chlorophyll fluorescence originates from dynamic energy partitioning at the leaf level and does not exhibit a uniformly linear relationship with photosynthesis at finer scales. We induced stomatal closure in deciduous woody tree branches and measured SIF at a proximal scale, alongside leaf-level gas exchange, pulse amplitude modulated (PAM) fluorescence, and leaf pigment content. We found no change in SIF or steady-state PAM fluorescence, despite clear reductions in stomatal conductance, carbon assimilation, and light-use efficiency in treated leaves. These findings suggest that equating SIF and photosynthesis is an oversimplification that may undermine the utility of SIF as a biophysical parameter in GPP models.},\n\tlanguage = {en},\n\tnumber = {15},\n\turldate = {2023-10-16},\n\tjournal = {Geophysical Research Letters},\n\tauthor = {Marrs, J. K. and Reblin, J. S. and Logan, B. A. and Allen, D. W. and Reinmann, A. B. and Bombard, D. M. and Tabachnik, D. and Hutyra, L. R.},\n\tyear = {2020},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020GL087956},\n\tkeywords = {carbon cycle, ecophysiology, photosynthesis, remote sensing, solar-induced fluorescence},\n\tpages = {e2020GL087956},\n}\n\n

\n\n\n\n\n\n

\n\n

\n \n 2019\n \n \n (4)\n \n \n

\n \n \n

\n \n\n \n \n \n \n \n \n Canopy chlorophyll fluorescence applied to stress detection using an easy-to-build micro-lidar.\n \n \n \n \n\n\n \n Moya, I.; Loayza, H.; López, M. L.; Quiroz, R.; Ounis, A.; and Goulas, Y.\n\n\n \n\n\n\n Photosynthesis Research, 142(1): 1–15. October 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Canopy$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{moya_canopy_2019,\n\ttitle = {Canopy chlorophyll fluorescence applied to stress detection using an easy-to-build micro-lidar},\n\tvolume = {142},\n\tissn = {1573-5079},\n\turl = {https://doi.org/10.1007/s11120-019-00642-9},\n\tdoi = {10.1007/s11120-019-00642-9},\n\tabstract = {LEDFLEX is a micro-lidar dedicated to the measurement of vegetation fluorescence. The light source consists of 4 blue Light-Emitting Diodes (LED) to illuminate part of the canopy in order to average the spatial variability of small crops. The fluorescence emitted in response to a 5-μs width pulse is separated from the ambient light through a synchronized detection. Both the reflectance and the fluorescence of the target are acquired simultaneously in exactly the same field of view, as well as the photosynthetic active radiation and air temperature. The footprint is about 1 m2 at a distance of 8 m. By increasing the number of LEDs longer ranges can be reached. The micro-lidar has been successfully applied under full sunlight conditions to establish the signature of water stress on pea (Pisum Sativum) canopy. Under well-watered conditions the diurnal cycle presents an M shape with a minimum (Fmin) at noon which is Fmin {\\textgreater} Fo. After several days withholding watering, Fs decreases and Fmin {\\textless} Fo. The same patterns were observed on mint (Menta Spicata) and sweet potatoes (Ipomoea batatas) canopies. Active fluorescence measurements with LEDFLEX produced robust fluorescence yield data as a result of the constancy of the excitation intensity and its geometry fixity. Passive methods based on Sun-Induced chlorophyll Fluorescence (SIF) that uses high-resolution spectrometers generate only flux data and are dependent on both the 3D structure of vegetation and variable irradiance conditions along the day. Parallel measurements with LEDFLEX should greatly improve the interpretation of SIF changes.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2019-10-22},\n\tjournal = {Photosynthesis Research},\n\tauthor = {Moya, Ismael and Loayza, Hildo and López, Maria Llanos and Quiroz, Roberto and Ounis, Abderrahmane and Goulas, Yves},\n\tmonth = oct,\n\tyear = {2019},\n\tkeywords = {LEDFLEX, LIF, SIF, Stress detection, µ-lidar},\n\tpages = {1--15},\n}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Maximum fluorescence and electron transport kinetics determined by light-induced fluorescence transients (LIFT) for photosynthesis phenotyping.\n \n \n \n \n\n\n \n Keller, B.; Vass, I.; Matsubara, S.; Paul, K.; Jedmowski, C.; Pieruschka, R.; Nedbal, L.; Rascher, U.; and Muller, O.\n\n\n \n\n\n\n Photosynthesis Research, 140(2): 221–233. May 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Maximum$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{keller_maximum_2019,\n\ttitle = {Maximum fluorescence and electron transport kinetics determined by light-induced fluorescence transients ({LIFT}) for photosynthesis phenotyping},\n\tvolume = {140},\n\tissn = {1573-5079},\n\turl = {https://doi.org/10.1007/s11120-018-0594-9},\n\tdoi = {10.1007/s11120-018-0594-9},\n\tabstract = {Photosynthetic phenotyping requires quick characterization of dynamic traits when measuring large plant numbers in a fluctuating environment. Here, we evaluated the light-induced fluorescence transient (LIFT) method for its capacity to yield rapidly fluorometric parameters from 0.6 m distance. The close approximation of LIFT to conventional chlorophyll fluorescence (ChlF) parameters is shown under controlled conditions in spinach leaves and isolated thylakoids when electron transport was impaired by anoxic conditions or chemical inhibitors. The ChlF rise from minimum fluorescence (Fo) to maximum fluorescence induced by fast repetition rate (Fm−FRR) flashes was dominated by reduction of the primary electron acceptor in photosystem II (QA). The subsequent reoxidation of QA− was quantified using the relaxation of ChlF in 0.65 ms (Fr1) and 120 ms (Fr2) phases. Reoxidation efficiency of QA− (Fr1/Fv, where Fv = Fm−FRR − Fo) decreased when electron transport was impaired, while quantum efficiency of photosystem II (Fv/Fm) showed often no significant effect. ChlF relaxations of the LIFT were similar to an independent other method. Under increasing light intensities, Fr2′/Fq′ (where Fr2′ and Fq′ represent Fr2 and Fv in the light-adapted state, respectively) was hardly affected, whereas the operating efficiency of photosystem II (Fq′/Fm′) decreased due to non-photochemical quenching. Fm−FRR was significantly lower than the ChlF maximum induced by multiple turnover (Fm−MT) flashes. However, the resulting Fv/Fm and Fq′/Fm′ from both flashes were highly correlated. The LIFT method complements Fv/Fm with information about efficiency of electron transport. Measurements in situ and from a distance facilitate application in high-throughput and automated phenotyping.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-10-16},\n\tjournal = {Photosynthesis Research},\n\tauthor = {Keller, Beat and Vass, Imre and Matsubara, Shizue and Paul, Kenny and Jedmowski, Christoph and Pieruschka, Roland and Nedbal, Ladislav and Rascher, Uwe and Muller, Onno},\n\tmonth = may,\n\tyear = {2019},\n\tkeywords = {Electron transport kinetics, Fast repetition rate, Fluorescence transient, Photosynthesis},\n\tpages = {221--233},\n}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n L2 Daily Solar-Induced Fluorescence (SIF) from ERS-2 GOME, 1995-2003.\n \n \n \n \n\n\n \n Joiner, J.; Yoshida, Y.; Koehler, P.; Frankenberg, C.; and Parazoo, N. C.\n\n\n \n\n\n\n ORNL DAAC. December 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"L2$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{joiner_l2_2019,\n\ttitle = {L2 {Daily} {Solar}-{Induced} {Fluorescence} ({SIF}) from {ERS}-2 {GOME}, 1995-2003},\n\turl = {https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1758},\n\tdoi = {10.3334/ORNLDAAC/1758},\n\tabstract = {ORNL DAAC: This dataset provides Level 2 Solar-Induced Fluorescence (SIF) of Chlorophyll estimates derived from the Global Ozone Monitoring Experiment (GOME) instrument on the European Space Agency's (ESA's) European Remote-Sensing 2 (ERS-2) satellite. Each file contains daily raw and bias-adjusted solar-induced fluorescence on an orbital basis (land pixels only), at a resolution of 40 km x 320 km, along with quality control information and ancillary data. Data is provided for the period from 19950701 to 20030622. The GOME SIF product is inherently noisy due to low signal levels and has undergone only a limited amount of validation.},\n\tlanguage = {en-US},\n\turldate = {2023-10-16},\n\tjournal = {ORNL DAAC},\n\tauthor = {Joiner, J. and Yoshida, Y. and Koehler, P. and Frankenberg, C. and Parazoo, N. C.},\n\tmonth = dec,\n\tyear = {2019},\n}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n TROPOMI reveals dry-season increase of solar-induced chlorophyll fluorescence in the Amazon forest.\n \n \n \n \n\n\n \n Doughty, R.; Köhler, P.; Frankenberg, C.; Magney, T. S.; Xiao, X.; Qin, Y.; Wu, X.; and Moore, B.\n\n\n \n\n\n\n Proceedings of the National Academy of Sciences, 116(44): 22393–22398. 2019.\n _eprint: https://www.pnas.org/doi/pdf/10.1073/pnas.1908157116\n\n\n\n
\n\n\n\n \n \n $\"TROPOMI$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{doughty_tropomi_2019,\n\ttitle = {{TROPOMI} reveals dry-season increase of solar-induced chlorophyll fluorescence in the {Amazon} forest},\n\tvolume = {116},\n\turl = {https://www.pnas.org/doi/abs/10.1073/pnas.1908157116},\n\tdoi = {10.1073/pnas.1908157116},\n\tabstract = {Photosynthesis of the Amazon rainforest plays an important role in the regional and global carbon cycles, but, despite considerable in situ and space-based observations, it has been intensely debated whether there is a dry-season increase in greenness and photosynthesis of the moist tropical Amazonian forests. Solar-induced chlorophyll fluorescence (SIF), which is emitted by chlorophyll, has a strong positive linear relationship with photosynthesis at the canopy scale. Recent advancements have allowed us to observe SIF globally with Earth observation satellites. Here we show that forest SIF did not decrease in the early dry season and increased substantially in the late dry season and early part of wet season, using SIF data from the Tropospheric Monitoring Instrument (TROPOMI), which has unprecedented spatial resolution and near-daily global coverage. Using in situ CO2 eddy flux data, we also show that cloud cover rarely affects photosynthesis at TROPOMI’s midday overpass, a time when the forest canopy is most often light-saturated. The observed dry-season increases of forest SIF are not strongly affected by sun-sensor geometry, which was attributed as creating a pseudo dry-season green-up in the surface reflectance data. Our results provide strong evidence that greenness, SIF, and photosynthesis of the tropical Amazonian forest increase during the dry season.},\n\tnumber = {44},\n\tjournal = {Proceedings of the National Academy of Sciences},\n\tauthor = {Doughty, Russell and Köhler, Philipp and Frankenberg, Christian and Magney, Troy S. and Xiao, Xiangming and Qin, Yuanwei and Wu, Xiaocui and Moore, Berrien},\n\tyear = {2019},\n\tnote = {\\_eprint: https://www.pnas.org/doi/pdf/10.1073/pnas.1908157116},\n\tpages = {22393--22398},\n}\n\n

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\n \n 2018\n \n \n (2)\n \n \n

\n \n \n

\n \n\n \n \n \n \n \n \n Measuring the dynamic photosynthome.\n \n \n \n \n\n\n \n Murchie, E. H; Kefauver, S.; Araus, J. L.; Muller, O.; Rascher, U.; Flood, P. J; and Lawson, T.\n\n\n \n\n\n\n Annals of Botany, 122(2): 207–220. August 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Measuring$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{murchie_measuring_2018,\n\ttitle = {Measuring the dynamic photosynthome},\n\tvolume = {122},\n\tissn = {0305-7364},\n\turl = {https://doi.org/10.1093/aob/mcy087},\n\tdoi = {10.1093/aob/mcy087},\n\tabstract = {Photosynthesis underpins plant productivity and yet is notoriously sensitive to small changes in environmental conditions, meaning that quantitation in nature across different time scales is not straightforward. The ‘dynamic’ changes in photosynthesis (i.e. the kinetics of the various reactions of photosynthesis in response to environmental shifts) are now known to be important in driving crop yield.It is known that photosynthesis does not respond in a timely manner, and even a small temporal ‘mismatch’ between a change in the environment and the appropriate response of photosynthesis toward optimality can result in a fall in productivity. Yet the most commonly measured parameters are still made at steady state or a temporary steady state (including those for crop breeding purposes), meaning that new photosynthetic traits remain undiscovered.There is a great need to understand photosynthesis dynamics from a mechanistic and biological viewpoint especially when applied to the field of ‘phenomics’ which typically uses large genetically diverse populations of plants. Despite huge advances in measurement technology in recent years, it is still unclear whether we possess the capability of capturing and describing the physiologically relevant dynamic features of field photosynthesis in sufficient detail. Such traits are highly complex, hence we dub this the ‘photosynthome’. This review sets out the state of play and describes some approaches that could be made to address this challenge with reference to the relevant biological processes involved.},\n\tnumber = {2},\n\turldate = {2023-10-16},\n\tjournal = {Annals of Botany},\n\tauthor = {Murchie, Erik H and Kefauver, Shawn and Araus, Jose Luis and Muller, Onno and Rascher, Uwe and Flood, Pádraic J and Lawson, Tracy},\n\tmonth = aug,\n\tyear = {2018},\n\tpages = {207--220},\n}\n\n

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\n \n\n \n \n \n \n \n \n The mark of vegetation change on Earth’s surface energy balance.\n \n \n \n \n\n\n \n Duveiller, G.; Hooker, J.; and Cescatti, A.\n\n\n \n\n\n\n Nature Communications, 9(1): 679. February 2018.\n Number: 1 Publisher: Nature Publishing Group\n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

@article{duveiller_mark_2018,\n\ttitle = {The mark of vegetation change on {Earth}’s surface energy balance},\n\tvolume = {9},\n\tcopyright = {2018 The Author(s)},\n\tissn = {2041-1723},\n\turl = {https://www.nature.com/articles/s41467-017-02810-8},\n\tdoi = {10.1038/s41467-017-02810-8},\n\tabstract = {Changing vegetation cover alters the radiative and non-radiative properties of the surface. The result of competing biophysical processes on Earth’s surface energy balance varies spatially and seasonally, and can lead to warming or cooling depending on the specific vegetation change and background climate. Here we provide the first data-driven assessment of the potential effect on the full surface energy balance of multiple vegetation transitions at global scale. For this purpose we developed a novel methodology that is optimized to disentangle the effect of mixed vegetation cover on the surface climate. We show that perturbations in the surface energy balance generated by vegetation change from 2000 to 2015 have led to an average increase of 0.23 ± 0.03 °C in local surface temperature where those vegetation changes occurred. Vegetation transitions behind this warming effect mainly relate to agricultural expansion in the tropics, where surface brightening and consequent reduction of net radiation does not counter-balance the increase in temperature associated with reduction in transpiration. This assessment will help the evaluation of land-based climate change mitigation plans.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-10-16},\n\tjournal = {Nature Communications},\n\tauthor = {Duveiller, Gregory and Hooker, Josh and Cescatti, Alessandro},\n\tmonth = feb,\n\tyear = {2018},\n\tnote = {Number: 1\nPublisher: Nature Publishing Group},\n\tkeywords = {Climate-change mitigation, Ecosystem ecology, Forestry},\n\tpages = {679},\n}\n\n

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\n \n 2017\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence.\n \n \n \n \n\n\n \n Sun, Y.; Frankenberg, C.; Wood, J. D.; Schimel, D. S.; Jung, M.; Guanter, L.; Drewry, D. T.; Verma, M.; Porcar-Castell, A.; Griffis, T. J.; Gu, L.; Magney, T. S.; Köhler, P.; Evans, B.; and Yuen, K.\n\n\n \n\n\n\n Science, 358(6360): eaam5747. 2017.\n _eprint: https://www.science.org/doi/pdf/10.1126/science.aam5747\n\n\n\n
\n\n\n\n \n \n $\"OCO-2$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{sun_oco-2_2017,\n\ttitle = {{OCO}-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence},\n\tvolume = {358},\n\turl = {https://www.science.org/doi/abs/10.1126/science.aam5747},\n\tdoi = {10.1126/science.aam5747},\n\tabstract = {Quantifying gross primary production (GPP) remains a major challenge in global carbon cycle research. Spaceborne monitoring of solar-induced chlorophyll fluorescence (SIF), an integrative photosynthetic signal of molecular origin, can assist in terrestrial GPP monitoring. However, the extent to which SIF tracks spatiotemporal variations in GPP remains unresolved. Orbiting Carbon Observatory-2 (OCO-2)’s SIF data acquisition and fine spatial resolution permit direct validation against ground and airborne observations. Empirical orthogonal function analysis shows consistent spatiotemporal correspondence between OCO-2 SIF and GPP globally. A linear SIF-GPP relationship is also obtained at eddy-flux sites covering diverse biomes, setting the stage for future investigations of the robustness of such a relationship across more biomes. Our findings support the central importance of high-quality satellite SIF for studying terrestrial carbon cycle dynamics.},\n\tnumber = {6360},\n\tjournal = {Science},\n\tauthor = {Sun, Y. and Frankenberg, C. and Wood, J. D. and Schimel, D. S. and Jung, M. and Guanter, L. and Drewry, D. T. and Verma, M. and Porcar-Castell, A. and Griffis, T. J. and Gu, L. and Magney, T. S. and Köhler, P. and Evans, B. and Yuen, K.},\n\tyear = {2017},\n\tnote = {\\_eprint: https://www.science.org/doi/pdf/10.1126/science.aam5747},\n\tpages = {eaam5747},\n}\n\n

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\n \n 2014\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n Persistent growth of CO2 emissions and implications for reaching climate targets.\n \n \n \n \n\n\n \n Friedlingstein, P.; Andrew, R. M.; Rogelj, J.; Peters, G. P.; Canadell, J. G.; Knutti, R.; Luderer, G.; Raupach, M. R.; Schaeffer, M.; van Vuuren, D. P.; and Le Quéré, C.\n\n\n \n\n\n\n Nature Geoscience, 7(10): 709–715. October 2014.\n Number: 10 Publisher: Nature Publishing Group\n\n\n\n
\n\n\n\n \n \n $\"Persistent$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n\n\n\n

@article{friedlingstein_persistent_2014,\n\ttitle = {Persistent growth of {CO2} emissions and implications for reaching climate targets},\n\tvolume = {7},\n\tcopyright = {2014 Springer Nature Limited},\n\tissn = {1752-0908},\n\turl = {https://www.nature.com/articles/ngeo2248},\n\tdoi = {10.1038/ngeo2248},\n\tabstract = {In order to limit climate warming, CO2 emissions must remain below fixed quota. An evaluation of past emissions suggests that at 2014 emissions rates, the total quota will probably be exhausted within the next 30 years.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2023-10-16},\n\tjournal = {Nature Geoscience},\n\tauthor = {Friedlingstein, P. and Andrew, R. M. and Rogelj, J. and Peters, G. P. and Canadell, J. G. and Knutti, R. and Luderer, G. and Raupach, M. R. and Schaeffer, M. and van Vuuren, D. P. and Le Quéré, C.},\n\tmonth = oct,\n\tyear = {2014},\n\tnote = {Number: 10\nPublisher: Nature Publishing Group},\n\tkeywords = {Climate and Earth system modelling, Climate-change mitigation},\n\tpages = {709--715},\n}\n\n

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\n \n 2013\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n Chlorophyll fluorescence analysis: a guide to good practice and understanding some new applications.\n \n \n \n \n\n\n \n Murchie, E.; and Lawson, T.\n\n\n \n\n\n\n Journal of Experimental Botany, 64(13): 3983–3998. October 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Chlorophyll$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{murchie_chlorophyll_2013,\n\ttitle = {Chlorophyll fluorescence analysis: a guide to good practice and understanding some new applications},\n\tvolume = {64},\n\tissn = {0022-0957},\n\tshorttitle = {Chlorophyll fluorescence analysis},\n\turl = {https://doi.org/10.1093/jxb/ert208},\n\tdoi = {10.1093/jxb/ert208},\n\tabstract = {Chlorophyll fluorescence is a non-invasive measurement of photosystem II (PSII) activity and is a commonly used technique in plant physiology. The sensitivity of PSII activity to abiotic and biotic factors has made this a key technique not only for understanding the photosynthetic mechanisms but also as a broader indicator of how plants respond to environmental change. This, along with low cost and ease of collecting data, has resulted in the appearance of a large array of instrument types for measurement and calculated parameters which can be bewildering for the new user. Moreover, its accessibility can lead to misuse and misinterpretation when the underlying photosynthetic processes are not fully appreciated. This review is timely because it sits at a point of renewed interest in chlorophyll fluorescence where fast measurements of photosynthetic performance are now required for crop improvement purposes. Here we help the researcher make choices in terms of protocols using the equipment and expertise available, especially for field measurements. We start with a basic overview of the principles of fluorescence analysis and provide advice on best practice for taking pulse amplitude-modulated measurements. We also discuss a number of emerging techniques for contemporary crop and ecology research, where we see continual development and application of analytical techniques to meet the new challenges that have arisen in recent years. We end the review by briefly discussing the emerging area of monitoring fluorescence, chlorophyll fluorescence imaging, field phenotyping, and remote sensing of crops for yield and biomass enhancement.},\n\tnumber = {13},\n\turldate = {2023-10-16},\n\tjournal = {Journal of Experimental Botany},\n\tauthor = {Murchie, E.H. and Lawson, T.},\n\tmonth = oct,\n\tyear = {2013},\n\tpages = {3983--3998},\n}\n\n

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