There is a critical need for sensitive remote sensing approaches to monitor the parameters governing photosynthesis, at the temporal scales relevant to their natural dynamics. The photochemical ...reflectance index (PRI) and chlorophyll fluorescence (F) offer a strong potential for monitoring photosynthesis at local, regional, and global scales, however the relationships between photosynthesis and solar induced F (SIF) on diurnal and seasonal scales are not fully understood. This study examines how the fine spatial and temporal scale SIF observations relate to leaf level chlorophyll fluorescence metrics (i.e., PSII yield, YII and electron transport rate, ETR), canopy gross primary productivity (GPP), and PRI. The results contribute to enhancing the understanding of how SIF can be used to monitor canopy photosynthesis. This effort captured the seasonal and diurnal variation in GPP, reflectance, F, and SIF in the O2A (SIFA) and O2B (SIFB) atmospheric bands for corn (Zea mays L.) at a study site in Greenbelt, MD. Positive linear relationships of SIF to canopy GPP and to leaf ETR were documented, corroborating published reports. Our findings demonstrate that canopy SIF metrics are able to capture the dynamics in photosynthesis at both leaf and canopy levels, and show that the relationship between GPP and SIF metrics differs depending on the light conditions (i.e., above or below saturation level for photosynthesis). The sum of SIFA and SIFB (SIFA+B), as well as the SIFA+B yield, captured the dynamics in GPP and light use efficiency, suggesting the importance of including SIFB in monitoring photosynthetic function. Further efforts are required to determine if these findings will scale successfully to airborne and satellite levels, and to document the effects of data uncertainties on the scaling.
Highlights • Six IL36RN variants were found in 29 GPP patients, 12 PV cases and 6 controls. • The variants’ frequencies were different between GPP and PV groups. • There were also significant ...differences between GPP alone and GPP with PV groups. • Demonstrated that GPP and PV are distinct subtypes of psoriasis on the etiology. • GPP alone may be regarded as an especial entities of GPP different from GPP with PV.
•Patterns of summer forest productivity were examined in the context of winter snow and summer rain.•The GPP season was longer in years with earlier snowmelt.•Total GPP was higher for a longer GPP ...season in sites with wetter summer climate.•Total GPP was lower for a longer GPP season in sites with drier summer climate.•These patterns were weaker in forests with snow as a higher annual fraction of precipitation.
Seasonal snow cover is important in shaping ecosystem carbon uptake across many regions of the world, however forest responses to projected declines in snowpack remain uncertain. We studied the response of forest gross primary productivity (GPP) during the photosynthetically active season to interannual and spatial variability in snow water equivalent (SWE), timing of snowmelt, and length of the active season. We combined carbon flux and weather data from 14 temperate deciduous and evergreen forests in the US and southeast Canada with SWE and precipitation from the Snow Data Assimilation System to test these hypotheses: 1) earlier snowmelt leads to a longer active season; 2) a longer active season is associated with higher total GPP, and 3) GPP during the active season is dependent on peak SWE and timing of snowmelt the previous winter.
Regression and correlation analyses did not reveal meaningful environmental predictors of interannual variability in GPP, so linear mixed effects models were used to analyze broader scale spatiotemporal patterns. We found that active season length was negatively correlated with total active season GPP in forests with drier summers on average (based on mean annual summer climatic water deficit), but positively correlated in areas with typically wetter summers. The magnitude of these effects decreased at forests with a higher percentage of annual precipitation falling as snow. Our results showed that the capacity for plants to gain more carbon during a longer active season appears to be dependent on soil water status determined by long-term climate, rather than interannual fluctuations in weather. We found no evidence that the magnitude of total snowfall or peak SWE had a legacy effect on subsequent active season GPP. Finally, we highlight that there was large interannual variability both within and between sites that was not well explained by seasonal climate or phenology.
Water availability, which can be represented by soil water content (SWC), plays a crucial role in plant growth and productivity across the cold and arid Qinghai-Tibetan Plateau. However, the indirect ...effects of SWC are less well understood, and a more comprehensive understanding of its regulating effects may enhance the recognition of its importance, as this factor is pivotal for accurately predicting the future response of alpine ecosystems to climate change. In this study, in situ eddy covariance observation data from typical alpine ecosystems and satellite data covering the Qinghai-Tibetan region were used to comprehensively reveal the effects of SWC on ecosystem productivity. The results indicated that SWC played an important role in regulating the responses of gross primary productivity (GPP) to other environmental factors over both time and space, especially in terms of the responses of GPP to vapor pressure deficit (VPD). The regulating effect can be summarized as follows: there was a specific SWC value (SWC = 0.24 m3 m−3 on the Qinghai-Tibetan Plateau) above which SWC was no longer the primary limiting factor. The responses of GPP to certain environmental factors shifted from negative to positive when the SWC increased above this value. The responses of GPP to VPD exhibited the highest sensitivity to the regulating effects of SWC, with a general response pattern found across different temporal and spatial scales. The findings revealed divergent responses of GPP to environmental factors under different SWC conditions and between arid and humid regions, emphasizing the importance of soil water conditions. These findings suggest that water conditions should be given primary consideration in global change studies.
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•The soil water content (SWC) controlled the alpine ecosystem productivity (GPP).•The responses of GPP to other factors shifted by an SWC threshold of 0.24 m3 m−3.•SWC threshold (0.24) was verified and applicable at multiple spatiotemporal scales.•From arid to humid, the VPD shifted from a negative factor to a positive factor.•SWC would determine the responses of alpine ecosystems to future climate change.
•The GPP/SIF ratio has significant dynamic variations.•SIF-based GPP estimation is mainly determined by SIF.•The dynamic GPP-SIF relationship can improve the accuracy of GPP estimates.
Previous ...studies have indicated that gross primary production (GPP) and solar-induced chlorophyll fluorescence (SIF) have a strong linear relationship, and usually exhibit similar spatial and temporal patterns. However, the responses of GPP and SIF to the environment may be different, which will lead to a variant GPP-SIF relationship. To better investigate the impact of the dynamics in GPP-SIF relationship on GPP estimation, we established two GPP models. An inconstant GPP/SIF ratio model (Dynamic-Ratio model, DR model) was first established using meteorological variables and leaf area index (LAI) based on random forest regression algorithm. The model was then used to estimate GPP (referred to as GPP_DR) with different satellite SIF datasets i.e., downscaled fine resolution SIF from the Orbiting Carbon Observatory-2 (GOSIF) and Global Ozone Monitoring Experiment‐2 SIF (downscaled GOME-2 SIF). The second model (SIF-Climate-LAI model, SCL model) was also based on the random forest algorithm but was directly driven by meteorological variables, LAI and SIF data, and no GPP/SIF ratio was used in the model. As a comparison, the linear relationship between GPP and SIF was also established using eddy covariance tower GPP (GPP_EC) and SIF datasets based on linear regression without considering variations of GPP-SIF relationship (Fixed-Ratio model, FR model). Considering the spatio-temporal variations of GPP-SIF relationship can improve the GPP simulation to a certain extent by mitigating the underestimation of peak GPP values. This improvement was found for both DR and SCL models. Owing to the dynamic variations of GPP/SIF ratio and associated uncertainties, the performance of DR model was not as good as that of SCL model. GPP estimation derived from GOSIF matched better with GPP_EC than that from downscaled GOME-2 SIF for DR, SCL and FR models. Our findings suggested that GPP can be better derived from satellite SIF by considering the variations of GPP-SIF relationship.
Terrestrial gross primary productivity (GPP) is the key element in the carbon cycle process. Accurate GPP estimation hinges on the maximum carboxylation rate (Vcmax,025). The high uncertainty in ...deriving ecosystem-level Vcmax,025 has long hampered efforts toward the performance of the GPP model. Recently studies suggest the strong relationship between spectral reflectance and Vcmax,025. We proposed the multispectral surface reflectance-driven Vcmax,025 simulator using the fully connected deep neural network and built the hybrid modelling framework for GPP estimation by integrating the data-driven Vcmax,025 simulator in the process-based model. The performance of hybrid GPP model was evaluated at 95 flux sites. The result shows that the multispectral surface reflectance-driven Vcmax,025 simulator acquires the satisfactory estimation, with correlation coefficient (R), root mean square error (RMSE) and median absolute percentage error (MdAPE) ranging from 0.34 to 0.80, 14 to 43 μmol m−2 s−1 and 21 % to 66 % across different land cover types, respectively. The hybrid framework generates good GPP estimates with R, RMSE and MdAPE varying from 0.76 to 0.89, 1.79 to 6.16 μmol m−2 s−1 and 27 % to 90 %, respectively. Compared with EVI-driven method, the multispectral surface reflectance significantly improves the Vcmax,025 and GPP estimates, with MdAPE declining by 0.6 %–18 % and 1 % to 21 %, respectively. The Shapley value analysis reveals that red (620–670 nm), near-infrared (841–876 nm) and shortwave infrared (1628–1652 nm and 2105–2155 nm) are the key bands for Vcmax,025 estimation. This study highlights the potential of multispectral surface reflectance for quantifying ecosystem-level Vcmax,025. The new hybrid framework fully extracts the information of all available spectral bands using deep learning to reduce parameter uncertainty while maintains the description of photosynthetic process to ensure its physical reasonability. It can serve as a powerful tool for accurate global GPP estimation.
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•The multispectral-based Vcmax,025 simulator is constructed using the deep neural network.•The hybrid GPP model is built by integrating data-driven subprocess into BEPS model.•Red, near-infrared and shortwave infrared bands contribute much to Vcmax,025 estimates.•The hybrid model achieved a 21 % reduction in MdAPE for GPP estimates.
Background
Generalized pustular psoriasis (GPP) is a rare and often refractory disease, and GPP flares can be life threatening. The rarity of GPP prevents the development and evaluation of effective ...GPP‐specific treatments and obstructs disease understanding.
Objectives
To evaluate the frequency and severity of GPP flares and the clinical background and treatment history of patients with GPP in Japan.
Methods
This retrospective, longitudinal, Japanese chart review study included patients diagnosed with GPP (according to Japanese Dermatological Association JDA diagnostic criteria), with ≥6 months of continuous observation within 10 years of protocol approval at study sites. Primary outcomes: the frequency and severity of GPP flares during follow‐up. Secondary outcomes: patient characteristics (at time of initial GPP diagnosis) and GPP treatment during follow‐up.
Results
Overall, 205 Japanese patients were included; 106/205 (51.7%) were male, 146/205 (71.2%) were aged <65 years. Few patients had a family history of GPP (7/155, 4.5%) or psoriasis‐related diseases (6/120, 5.0%). At baseline, 36.1% (74/205), 30.7% (63/205) and 33.2% (68/205) of patients had mild, moderate and severe GPP, respectively; GPP flares were reported by 177/205 patients (86.3%), which were mostly moderate in severity (52/205; 25.4%) or severe (99/205; 48.3%). During follow‐up, GPP flares were reported by 70/205 patients (34.1%): 47/205 (22.9%) had 1 flare and 23/205 (11.2%) had ≥2. Among the 106 flare events reported during follow‐up, 1 was mild, 55 were moderate and 50 were severe. The median time to first GPP flare was 7.7 years and the overall incidence of GPP flares during follow‐up was 11.5/100 person‐years. During follow‐up, topical treatment (195/205; 95.1%), systemic therapy other than corticosteroids (177/205; 86.3%) and biologics (163/205; 79.5%) were most frequently used.
Conclusions
During follow‐up, over one‐third of Japanese patients with GPP experienced mostly moderate‐to‐severe GPP flares, despite available treatments. There remains an unmet need for effective GPP treatment options.
Satellite‐derived sun‐induced chlorophyll fluorescence (SIF) has been increasingly used for estimating gross primary production (GPP). However, the relationship between SIF and GPP has not been well ...defined, impeding the translation of satellite observed SIF to GPP. Previous studies have generally assumed a linear relationship between SIF and GPP at daily and longer time scales, but support for this assumption is lacking. Here, we used the GPP/SIF ratio to investigate seasonal variations in the relationship between SIF and GPP over the Northern Hemisphere (NH). Based on multiple SIF products and MODIS and FLUXCOM GPP data, we found strong seasonal hump‐shaped patterns for the GPP/SIF ratio over northern latitudes, with higher values in the summer than in the spring or autumn. This hump‐shaped GPP/SIF seasonal variation was confirmed by examining different SIF products and was evident for most vegetation types except evergreen broadleaf forests. The seasonal amplitude of the GPP/SIF ratio decreased from the boreal/arctic region to drylands and the tropics. For most of the NH, the lowest GPP/SIF values occurred in October or September, while the maximum GPP/SIF values were evident in June and July. The most pronounced seasonal amplitude of GPP/SIF occurred in intermediate temperature and precipitation ranges. GPP/SIF was positively related to temperature in the early and late parts of the growing season, but not during the peak growing months. These shifting relationships between temperature and GPP/SIF across different months appeared to play a key role in the seasonal dynamics of GPP/SIF. Several mechanisms may explain the patterns we observed, and future research encompassing a broad range of climate and vegetation settings is needed to improve our understanding of the spatial and temporal relationships between SIF and GPP. Nonetheless, the strong seasonal variation in GPP/SIF we identified highlights the importance of incorporating this behavior into SIF‐based GPP estimations.
Satellite‐derived sun‐induced chlorophyll fluorescence (SIF) is widely used to approximate gross primary production (GPP); however, it is unclear how the relationship between these two variables may change across different seasons. Here, we found strong seasonal hump‐shaped patterns for the GPP/SIF ratio over northern latitudes, highlighting the importance of incorporating this behavior into SIF‐based GPP estimations
•Five plant- and climate-factors are decisive in simulating SIF in the SCOPE.•SCOPE-SIF captured GPP better in Earth's tropical zone than other SIF datasets.•SCOPE-SIF illustrated spatially ...complements with GOSIF in Oceania and Europe.•SCOPE model could help uncover proper SIF factors in SIF reconstruction.
Solar-induced chlorophyll fluorescence (SIF) has been regarded as proxy data of vegetation photosynthesis; thus, it is assimilated into the terrestrial carbon cycle modeling. The Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model is one of the most utilized models of SIF simulation. However, the currently incomplete understanding of SCOPE SIF factors and the lack of exploring how SCOPE works under different vegetation types would deteriorate further carbon cycle research. Herein, this study disentangled decisive SIF factors in the SCOPE model; then, a sample SIF dataset (SynSIF), with spatial resolutions of both 0.02∘ and 0.05∘, was simulated through SCOPE model using factors above. Then this study validated how far SCOPE simulating SIF could capture GPP, compared with other SIF datasets. The results showed that: (1) There are five decisive SIF factors in SCOPE model, including plant status (leaf chlorophyll content and leaf area index) and meteorological parameters (incoming shortwave radiation, air temperature, and atmospheric vapor pressure). (2) The linear relationship of SynSIF-GPP outachieved other SIF datasets across all six vegetation types in southern South America, Asia, and Africa, improving R2 averagely by 0.33, 0.28, and 0.15, respectively. (3) SynSIF in Oceania and Europe, revealing GPP better in shrublands (with SynSIF-GPP R2 increasing by 0.15 and 0.16, respectively) and grasslands (with SynSIF-GPP coefficients increasing by 0.14 and 0.06, respectively), illustrated spatially complementary characteristics with GOSIF across varying vegetation types. Thus, we anticipate that this study could provide more complete information for SCOPE simulating SIF in different biome research when estimating the terrestrial carbon cycle.
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