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Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring.

Despite the vast scientific evidence obtained from the genomic sequencing of COVID-19, controversy regarding its origin has been created in the mass media. This could potentially have a long-term influence on the behavior among individuals, such as failure to comply with proposed social distancing measures, leading to a consequent rise in the morbidity and mortality rates from COVID-19 infection. Several studies have collected information about knowledge, attitudes, and practices regarding COVID-19; however, very little is known about the relationship of the perceptions of the individuals regarding the origin of the virus with the knowledge and perception about social distancing. This study aimed at ascertaining this relationship. For such purpose, a web-based cross-sectional study was conducted among a sample population from five provinces of the Dominican Republic from June to July of 2020. The data collection instrument exploited in the study was a self-designed questionnaire distributed throughout different social media platforms. A purposive sampling strategy was implemented and a total of 1195 respondents completed the questionnaire. The collected data was analyzed using SPSS. Descriptive statistics, stepwise multiple linear regression, and one-way multivariate analysis were implemented to test the hypotheses. The level of education was significantly associated ( P = .017) with individuals’ perception about the origin of COVID-19, whilst only age ( P = .032) and education level ( P < .001) statistically significantly predicted ‘knowledge about social distancing’. Perception of COVID-19 origin was statistically significant associated ( P = < .001) with the measures of the dependent variables (knowledge and perception on social distancing). The present study has established a possible link between the ‘perception of COVID-19 origin’ and ‘the perception and knowledge about social distancing’.

The benefits of garlic to health have been proclaimed for centuries; however, only recently have Allium sativum and its derivatives been proposed as promising candidates for maintaining the homeostasis of the immune system. The complex biochemistry of garlic makes it possible for variations in processing to yield different preparations with differences in final composition and compound proportion. In this review, we assess the most recent experimental results, which indicate that garlic appears to enhance the functioning of the immune system by stimulating certain cell types, such as macrophages, lymphocytes, natural killer (NK) cells, dendritic cells, and eosinophils, by mechanisms including modulation of cytokine secretion, immunoglobulin production, phagocytosis, and macrophage activation. Finally, because immune dysfunction plays an important role in the development and progress of several diseases, we critically examined immunoregulation by garlic extracts and compounds isolated, which can contribute to the treatment and prevention of pathologies such as obesity, metabolic syndrome, cardiovascular disorders, gastric ulcer, and even cancer. We concluded that A. sativum modulates cytokine secretion and that such modulation may provide a mechanism of action for many of their therapeutic effects.

What are the key insights from this review? Vegetation Earth observation is an inherently multiscale retrieval problem, with the effective scale determined by the interplay of observation, retrieval, and aggregation processes. Increasing spatial, spectral, or temporal resolution does not necessarily improve ecological accuracy (resolution–accuracy paradox), due to non-commutativity and scale-dependent error propagation. What are the implications emerging from this review? Vegetation products must be interpreted and evaluated at their effective scale, using aggregation-consistent and cross-scale diagnostics rather than single-resolution agreement. Robust use of current and future hyperspectral missions (e.g., FLEX, CHIME) requires scale-aware and uncertainty-aware modelling frameworks that explicitly account for heterogeneity, nonlinearity, and temporal sampling. Satellite-based vegetation monitoring has evolved from mapping vegetation canopy properties at single points in time toward analysing time-resolved dynamics of vegetation traits and process-related variables. Retrieved traits and solar-induced chlorophyll fluorescence (SIF) are inherently defined by sensor-specific spatial resolution, temporal integration, and spectral response. Modifying these characteristics alters the retrieval problem itself: under nonlinear retrievals and heterogeneous landscapes, aggregation and retrieval are generally non-commutative, and error components scale differently with resolution. Consequently, increasing spatial, spectral, or temporal detail does not guarantee improved ecological accuracy; a phenomenon we term the resolution–accuracy paradox. These interacting processes define the effective scale of vegetation products, which may differ from nominal sensor resolution and governs the interpretation of retrieved vegetation traits. When products with differing resolutions or compositing strategies are combined, scale effects can induce systematic artefacts in spatial patterns and derived dynamic indicators that cannot be resolved through improved per-pixel accuracy alone. This review establishes a scale-aware conceptual framework that treats scale as an explicit diagnostic dimension linking observation characteristics, retrieval formulations, trait definitions, and aggregation operators. We analyse how scale interactions influence spatial patterns, temporal dynamics, disturbance signals, and multiresolution data fusion, and derive diagnostic principles, best-practice guidelines, and research priorities for the scale-consistent interpretation of vegetation trait dynamics and SIF-constrained productivity and stress indicators across spatial and temporal scales. Framed in the context of upcoming hyperspectral missions such as CHIME and FLEX, which increase spectral information content, robust interpretation of vegetation products requires scale-consistent analysis and uncertainty-aware processing. For practitioners, this implies that vegetation products should be interpreted, validated, and compared at their effective scale rather than assuming that a finer spatial, spectral, or temporal resolution necessarily yields more reliable ecological information.

Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SCOPE and the atmospheric RTM 6SV. The retrieval models, named to S3-TOA-GPR-1.0, were directly implemented in Google Earth Engine (GEE) to enable the quantification of the traits from TOA data as acquired from the S3 Ocean and Land Colour Instrument (OLCI) sensor. Following good to high theoretical validation results with normalized root mean square error (NRMSE) ranging from 5% (FAPAR) to 19% (LAI), a three fold evaluation approach over diverse sites and land cover types was pursued: (1) temporal comparison against LAI and FAPAR products obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) for the time window 2016–2020, (2) spatial difference mapping with Copernicus Global Land Service (CGLS) estimates, and (3) direct validation using interpolated in situ data from the VALERI network. For all three approaches, promising results were achieved. Selected sites demonstrated coherent seasonal patterns compared to LAI and FAPAR MODIS products, with differences between spatially averaged temporal patterns of only 6.59%. In respect of the spatial mapping comparison, estimates provided by the S3-TOA-GPR-1.0 models indicated highest consistency with FVC and FAPAR CGLS products. Moreover, the direct validation of our S3-TOA-GPR-1.0 models against VALERI estimates indicated good retrieval performance for LAI, FAPAR and FVC. We conclude that our retrieval workflow of spatiotemporal S3 TOA data processing into GEE opens the path towards global monitoring of fundamental vegetation traits, accessible to the whole research community.

México’s implementation of several labor market reforms aimed to stimulate the flexibilization of labor relationships have brought long-standing concerns among scholars and governmental authorities about a displacement process of permanent by temporary jobs. In this regard, this manuscript aims to respond whether the dynamic interaction between permanent and temporary employment across Mexican states describe a substitution or complementary relationship. By means of implementing a structural panel vector autoregressive model, our estimation results demonstrate that a combination of both types of interaction, substitutive and complementary ones, prevail across manufacturing labor markets at state level. Moreover, a marked heterogeneity among estimated dynamic responses suggests that incentivizing permanent employment would induce stronger substitution effects on its temporary counterpart than, for example, substitution of permanent job positions arising when incentivizing temporary employment.

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> 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.

Aerosol particles from forest fire events in the Amazon can be effectively transported to urban areas in southeastern South America, thus affecting the air quality over those regions. A combination of observational data and a comprehensive air quality modeling system capable of anticipating acute air pollution episodes is therefore required. A new predictive framework for Amazon forest fire smoke dispersion over South America has been developed based on the Weather Research and Forecasting with Chemistry community (WRF-Chem) model. Two experiments of 48-h simulations over South America were performed by using this system at 20-km horizontal resolution, on a daily basis, during August and September of 2018 and 2019. The experiment in 2019 included the very strong 3-week forest fire event, when the São Paulo metropolitan area, located in southeastern South America, was plunged into darkness on August 19. The model results were satisfactorily compared against satellite-based data products and in situ measurements collected from air quality monitoring sites. The system is executed daily immediately after the CPTEC Satellite Division releases the latest active fire locations data and provides 48-h forecasts of regional distributions of chemical species such as CO, PM2.5, and O3. The new modeling system will be used as a benchmark within the framework of the Chemistry of the Atmosphere–Field Experiment in Brazil (CAFE-Brazil) project, which will take place in 2022 over the Amazon.

Background Xylan, the second most abundant polysaccharide in plant biomass, requires endoxylanases for its hydrolysis into xylooligosaccharides (XOS). Xylanases have been widely used in industries such as animal feed, bakery, juice production, and paper pulp. Recently, XOS have gained attention for their health benefits, including improved digestion, reduced cholesterol, and antioxidant effects. The cold-adapted GH10 xylanase of Antarctic origin Xyl-L was previously expressed in Escherichia coli , showing promising low-temperature activity. However, Pichia pastoris is currently a preferred host for industrial xylanase production due to its ability to express complex proteins and secrete them into the culture medium. This study explored the expression of Xyl-L in P. pastoris and evaluated its potential for XOS production using common flours as substrates, aiming for applications in the food and nutraceutical industry. Results Comparison between AOX1 ( P A O X 1 ) and GAP ( P GAP ) promoters for recombinant Xyl-L production in P. pastoris showed that the P A O X 1 promoter resulted in higher activity per wet-cell weight. Co-transforming P A O X 1 -Xyl strains with plasmids encoding genes aiding in protein folding ( HAC1 or PDI1 ) did not enhance Xyl-L catalytic activity compared to the parental P A O X 1 strain. Thus, P A O X 1 -Xyl was cultivated in 3 L bioreactors in fed-batch cultures; it is presumed that the enzyme is produced with glycosylations within its structure, given its migration within the SDS-PAGE gels. The produced Xyl-L was purified from the culture supernatant, resulting in peak xylanase activity after 90 h, with specific activity of 5.10 ± 0.21 U/mg, at pH 7.5 and 25 ∘ C, using beechwood xylan. It also showed a Km of 3.5 mg/mL and a kcat of 9.16 s - 1 . Xyl-L maintained over 80% of relative activity between pH 5.6 - 8.6 and 37 - 44 ∘ C, and was activated by CaCl 2 and MgCl 2 , but inhibited by MnCl 2 . Xyl-L was tested using several flours (whole wheat, rye, oatmeal and all-purpose) as substrates, where XOS with a polymerization degree (DP) of 2 were obtained from each substrate, whole wheat flour generated XOS with DP 3, and XOS with DP 2, 3 and 4 were produced when beechwood xylan was used as substrate. Conclusions The xylanase Xyl-L was successfully expressed in P. pastoris and proved to be able to degrade various flour substrates, producing XOS with DP ranging from 2 to 4, indicating its potential applications in the nutraceutical and food industries. Further studies must be performed to optimize its production in bioreactors.

The Granger Causality (GC) statistical test explores the causal relationships between different time series variables. By employing the GC method, the underlying causal links between environmental drivers and global vegetation properties can be untangled, which opens possibilities to forecast the increasing strain on ecosystems by droughts, global warming, and climate change. This study aimed to quantify the spatial distribution of four distinct satellite vegetation products’ (VPs) sensitivities to four environmental land variables (ELVs) at the global scale given the GC method. The GC analysis assessed the spatially explicit response of the VPs: (i) the fraction of absorbed photosynthetically active radiation (FAPAR), (ii) the leaf area index (LAI), (iii) solar-induced fluorescence (SIF), and, finally, (iv) the normalized difference vegetation index (NDVI) to the ELVs. These ELVs can be categorized as water availability assessing root zone soil moisture (SM) and accumulated precipitation (P), as well as, energy availability considering the effect of air temperature (T) and solar shortwave (R) radiation. The results indicate SM and P are key drivers, particularly causing changes in the LAI. SM alone accounts for 43%, while P accounts for 41%, of the explicitly caused areas over arid biomes. SM further significantly influences the LAI at northern latitudes, covering 44% of cold and 50% of polar biome areas. These areas exhibit a predominant response to R, which is a possible trigger for snowmelt, showing more than 40% caused by both cold and polar biomes for all VPs. Finally, T’s causality is evenly distributed amongst all biomes with fractional covers between ∼10 and 20%. By using the GC method, the analysis presents a novel way to monitor the planet’s ecosystem, based on solely two years as input data, with four VPs acquired by the synergy of Sentinel-3 (S3) and 5P (S5P) satellite data streams. The findings indicated unique, biome-specific responses of vegetation to distinct environmental drivers.