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The paper presents data that testify in favor of the participation of the cytochrome P450 system in the light emission of higher fungi. Extracts from mycelia of different species of luminous basidiomycetes containing fungal luminescent systems that provide luminescence in vitro were obtained. Applied conditions for the isolation of luminescent systems (sonication, centrifugation at 40000g) indicate the presence of membrane structures in the extracts, in particular, microsomes formed as a result of ultrasonic disintegration of the endoplasmic reticulum (ER). Differential spectral analysis of the extracts revealed the presence of two absorption peaks at 410 nm and 450 nm, which indicates the presence of cytochromes b5 and P450. The luminescence of the extracts is stimulated by reduced pyridine nucleotides, however, the addition of NADPH causes a higher level of luminescence compared with NADH. The addition of hydrogen peroxide significantly (from several times to 1-2 orders of magnitude) increases the luminescence intensity of extracts activated by NAD(P)H. The addition of fluconazole significantly inhibits the light emission of extracts. The data obtained indicates that the cytochrome P450 system associated with ER membranes may participate in the mechanism of light emission of higher fungi with the involvement in the process of electron transport enzyme systems: NADPH-dependent reductase of cytochrome P450 - cytochrome P450 and NADH-dependent reductase of cytochrome b5 - cytochrome b5 - cytochrome P450. In this case, cytochrome P450 may hydroxylate hispidin (precursor of the luminescent reaction substrate) to form luciferin and catalyze its oxidation in the presence of ROS with light emission.

Highlights. The article discusses the features of general anesthesia in patients with cerebrovascular diseases using the relaxant binding agent sugammadex.Aim. To evaluate the effectiveness of sugammadex in endovascular management of cerebrovascular diseases.Methods. The study included 57 patients who underwent endovascular treatment of cerebrovascular disease under general anesthesia. The experimental group included 29 patients who were injected with sugammadex - a selective relaxant binding agent for reversal of neuromuscular block induced by rocuronium. The comparison group included 28 patients who did not undergo decurarization (n = 13), or it was performed with neostigmine (n = 15). Acceleromyography was used to monitor the state of neuromuscular function.Result. Onset of action and duration of action of rocuronium at a dose of 0.9 mg/kg did not differ between the groups. The neuromuscular recovery time was 2.14 [1.67; 2.59] minutes after administration of sugammadex, compared with spontaneous 35.8 [31.5; 40.4] minutes and neostigmine-induced 22.1 [16.8; 27.3] minutes (pConclusion. Using a specific antidote sugammadex to eliminate the effect of rocuronium helps to restore neuromuscular function within 2–3 minutes and assess the neurological status of patients immediately after the endovascular treatment of cerebrovascular disease.

An author of the paper was omitted in the original version (Ted Conroy, University of Waikato, New Zealand). This has been corrected in the pdf and HTML versions of the paper, and the associated metadata.

To explore the influence of income on Low Birth Weight (LBW), taking into account other socio-economic measurements.This retrospective cohort study is based on the Israel National Insurance Institute (NII) database. The study population included 58,454 women who gave birth between 2008 and 2013 to 85,605 infants. Only singleton births at term (gestational age in weeks = 37 and later) were included. Logistic regression models with a Generalized Estimating Equation approach were used in order to assess the independent effect of income and Socio-Economic Regional Index (SERI), maternal age, family status, population group and occupational status on LBW. In addition, sibling analysis was conducted to assess the influence of a change in income on birth weight (BW) among 21,998 women.Lower income was associated with higher odds of LBW (odds ratio (OR) = 1.266; 95% CI:1.115-1.437. Immigrants from Ethiopia, Bedouins from the Negev, the youngest, the oldest, and single mothers had higher odds for LBW newborns. Compared to women whose income quartile had not changed between the most recent and the first births, for women who experienced a deterioration of three and two quartiles in family income, significantly lower birth weight was observed at the time point with lower income: 103 g (p = .049) and 71 g (p = .008), respectively. Improvement in income revealed an almost linear increase in birth weight.In an effort to prevent LBW associated mortality and diseases, interventions should be focused first of all on women from population groups who are disadvantaged.

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.

The advancement of smart textiles has led to significant interest in developing wearable textile sensors (WTS) and offering new modalities to sense vital signs and activity monitoring in daily life settings. For this, textile fabrication methods such as knitting, weaving, embroidery, and braiding offer promising pathways toward unobtrusive and seamless sensing for WTS applications. Specifically, the knitted sensor has a unique intermeshing loop structure which is currently used to monitor repetitive body movements such as breathing (microscale motion) and walking (macroscale motion). However, the practical sensing application of knit structure demands a comprehensive study of knit structures as a sensor. In this work, we present a detailed performance evaluation of six knitted sensors and sensing variation caused by design, sensor size, stretching percentages % (10, 15, 20, 25), cyclic stretching (1000), and external factors such as sweat (salt-fog test). We also present regulated respiration (inhale–exhale) testing data from 15 healthy human participants; the testing protocol includes three respiration rates; slow (10 breaths/min), normal (15 breaths/min), and fast (30 breaths/min). The test carried out with statistical analysis includes the breathing time and breathing rate variability. These testing results offer an empirically derived guideline for future WTS research, present aggregated information to understand the sensor behavior when it experiences a different range of motion, and highlight the constraints of the silver-based conductive yarn when exposed to the real environment.

Non-invasive comparative analysis of the spectral composition of energy absorbed by crop species at leaf and plant levels was carried out using the absorption coefficient retrieved from leaf and plant reflectance as an informative metric. In leaves of three species with contrasting leaf structures and photosynthetic pathways (maize, soybean, and rice), the blue, green, and red fractions of leaf absorption coefficients were 48, 20, and 32%, respectively. The fraction of green light in the total budget of light absorbed at the plant level was higher than at the leaf level approaching the size of the red fraction (24% green vs. 25.5% red) and surpassing it inside the canopy. The plant absorption coefficient in the far-red region (700–750 nm) was significant reaching 7–10% of the absorption coefficient in green or red regions. The spectral composition of the absorbed light in the three species was virtually the same. Fractions of light in absorbed PAR remained almost invariant during growing season over a wide range of plant chlorophyll content. Fractions of absorption coefficient in the green, red, and far-red were in accord with published results of quantum yield for CO2 fixation on an absorbed light basis. The role of green and far-red light in photosynthesis was demonstrated in simple experiments in natural conditions. The results show the potential for using leaf and plant absorption coefficients retrieved from reflectance to quantify photosynthesis in each spectral range.

The world is witnessing a rising number of preterm infants who are at significant risk of medical conditions. These infants require continuous care in Neonatal Intensive Care Units (NICU). Medical parameters are continuously monitored in premature infants in the NICU using a set of wired, sticky electrodes attached to the body. Medical adhesives used on the electrodes can be harmful to the baby, causing skin injuries, discomfort, and irritation. In addition, respiration rate (RR) monitoring in the NICU faces challenges of accuracy and clinical quality because RR is extracted from electrocardiogram (ECG). This research paper presents a design and validation of a smart textile pressure sensor system that addresses the existing challenges of medical monitoring in NICU. We designed two e-textile, piezoresistive pressure sensors made of Velostat for noninvasive RR monitoring; one was hand-stitched on a mattress topper material, and the other was embroidered on a denim fabric using an industrial embroidery machine. We developed a data acquisition system for validation experiments conducted on a high-fidelity, programmable NICU baby mannequin. We designed a signal processing pipeline to convert raw time-series signals into parameters including RR, rise and fall time, and comparison metrics. The results of the experiments showed that the relative accuracies of hand-stitched sensors were 98.68 (top sensor) and 98.07 (bottom sensor), while the accuracies of embroidered sensors were 99.37 (left sensor) and 99.39 (right sensor) for the 60 BrPM test case. The presented prototype system shows promising results and demands more research on textile design, human factors, and human experimentation.

The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions.

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-flled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the frst time in this paper. In addition, 206 of these sites are for the frst time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible., European Union (EU), United States Department of Energy (DOE)

Digital devices are the essential building blocks of any modern electronic system. Fibres containing digital devices could enable fabrics with digital system capabilities for applications in physiological monitoring, human-computer interfaces, and on-body machine-learning. Here, a scalable preform-to-fibre approach is used to produce tens of metres of flexible fibre containing hundreds of interspersed, digital temperature sensors and memory devices with a memory density of ~7.6 × 105 bits per metre. The entire ensemble of devices are individually addressable and independently operated through a single connection at the fibre edge, overcoming the perennial single-fibre single-device limitation and increasing system reliability. The digital fibre, when incorporated within a shirt, collects and stores body temperature data over multiple days, and enables real-time inference of wearer activity with an accuracy of 96% through a trained neural network with 1650 neuronal connections stored within the fibre. The ability to realise digital devices within a fibre strand which can not only measure and store physiological parameters, but also harbour the neural networks required to infer sensory data, presents intriguing opportunities for worn fabrics that sense, memorise, learn, and infer situational context., Implementation of digital electronics into fibres can enable real time monitoring of human physiological functions. Loke et al. show how digital functionalities can be incorporated into thin flexible polymeric fibre strands and applied for on-body machine-learning and intelligent textiles.

Chemoembolization has advantages over systemic chemotherapy as increase in drug concentration, time of exposition and decrease in systemic adverse effects.

Work absenteeism following an injury creates an economic burden on society and the individual. Programs encouraging return to work (RTW) should be implemented for high risk populations. The aim of this study was to identify the predictors for duration until RTW following an injury. The Israeli National Trauma Registry and the National Insurance Institute database (2008-2013) were linked. Logistic-regression models tested the probability not RTW within 1 month, 1 year and 2 years among 67% of the population and the quality of the model was examined among 33% of the population. The study population comprised 45,291 casualties (aged 21-67 and employed prior to injury as salaried workers). The majority of the study population (61%) RTW within 1 month from the injury event. Injury severity, multiple injuries, brain injury, traffic related injuries and fall injuries were significantly associated with work absenteeism. A dose-response relationship was found between income and not RTW: the lower the income the greater was the chance of not RTW. Among casualties with occupational injuries the odds for not RTW within a month, a year and 2 years were respectively, 3.7, 2.4 and 2 times significantly greater in comparison with casualties not injured at work. Underprivileged ethnic groups (Arabs and immigrants from Ethiopia) had a greater chance for long out of work stay following an injury. The outcomes of this study identified casualties at high risk for not RTW and enables health professionals to develop intervention programs focusing on returning to a productive lifestyle.

This paper presents the preliminary results of the Chaetopterus variopedatus bioluminescent system separation into luciferin and luciferase and a brief description of some their properties.

The objective of this study is to develop generic algorithm for estimating fraction of radiation absorbed by photosynthetically active vegetation not requiring parameterization for C3 and C4 crops,...

The goal of this study is to quantify variability of daily light use efficiency (LUE) based on radiation absorbed by photosynthetically active vegetation (LUEgreen) in C3 and C4 crops. Data contained GPP, fAPAR, total and green leaf area index taken over 16 site years of irrigated and rainfed maize and 8 site years of soybean in 2001–2008 including years with quite strong drought events. LUEgreen in irrigated and rainfed sites were statistically indistinguishable showing low sensitivity to water availability. Seasonal changes of LUEgreen remained remarkably small over a wide range of water supply, leaf area index and weather conditions. The magnitude and composition of incident radiation affected the magnitude of the day-to-day LUEgreen change – increases in incident PAR caused statistically significant decreases of LUEgreen. Convergence of LUEgreen to a narrow range in irrigated and rainfed crops brought important implications for understanding mechanisms of plant response to stress and remote estimation of primary production in crops.

Non-invasive comparative analysis of the spectral composition of energy absorbed by crop species at leaf and plant levels was carried out using the absorption coefficient retrieved from leaf and plant reflectance as an informative metric. In leaves of three species with contrasting leaf structures and photosynthetic pathways (maize, soybean, and rice), the blue, green, and red fractions of leaf absorption coefficients were 48, 20, and 32%, respectively. The fraction of green light in the total budget of light absorbed at the plant level was higher than at the leaf level approaching the size of the red fraction (24% green vs. 25.5% red) and surpassing it inside the canopy. The plant absorption coefficient in the far-red region (700-750 nm) was significant reaching 7-10% of the absorption coefficient in green or red regions. The spectral composition of the absorbed light in the three species was virtually the same. Fractions of light in absorbed PAR remained almost invariant during growing season over a wide range of plant chlorophyll content. Fractions of absorption coefficient in the green, red, and far-red were in accord with published results of quantum yield for CO

Sun induced fluorescence at 760 nm (F 760 ) has shown to provide a valid approach to quantify gross primary production (GPP) at various scales, however the relationship between GPP and F760 is influenced by the escape probability of fluorescence (Fesc), a variable which is not still fully understood. Combining radiative transfer modelling approaches, by means of the SCOPE model, and a data driven methodology based on variable selection methods we identify the predictors of Fesc, focusing on the effect of functional and structural traits. We show that Fesc is mainly predicted by structural variables such as fraction of grasses and near infrared reflectance. Building on the analysis of the predictor of Fesc, LUE p and LUE f we present a semi-empirical model formulation based only on optical data that significantly improves the GPP prediction.

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible. Fil: Pastorello, Gilberto. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Trotta, Carlo. University of Tuscia. DiBAf; Italia Fil: Canfora, Eleonora. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: Chu, Housen. Lawrence Berkeley national Laboratory. Climate & Ecosystem Sciences Division; Estados Unidos Fil: Christianson, Danielle. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Cheah, You - Wei. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Poindexter, Cristina. California State University. Department of Civil Engineering; Estados Unidos Fil: Chen, Jiquan. Michigan State University. Department of Geography, Environment, and Spatial Sciences; Estados Unidos Fil: Elbashandy, Abdelrah man. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Humphrey, Marty. University of Virginia. Department of Computer Science; Estados Unidos Fil: Isaac, Peter. TeRn Ecosystrem Processes; Australia Fil: Polidori, Diego. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: Riveca, Alessio. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: van Ingen, Catharine. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Zhang, Leiming. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China Fil: Amiro, Brian. University of Manitoba. Department of Soil Science; Canadá Fil: Ammann, Christof. Agroscope Research Institute. Department of Agroecology and Environment; Suiza Fil: Altaf Arain, M. McMaster University. School of Geography and Earth Sciences; Canadá. Fil: Ardo, Jonas. Lund University. Department of Physical Geography and Ecosystem Science; Suecia Fil: Arkebauer, Timothy. University of Nebraska-Lincoln. Department of Agronomy and Horticulture; Estados Unidos Fil: Arndt, Stefan K. The University of Melbourne. School of Ecosystem and Forest Sciences; Australia Fil: Arriga, Nicola. University of Antwerp. Department of Biology, Research Group PLECO; Bélgica. European Commission. Joint Research Centre; Italia Fil: Aubinet, Marc. University of Liege. TeRRA Teaching and Research Center; Bélgica Fil: Aurela, Mika. Finnish Meteorological Institute; Finlandia Fil: Baldocchi, Dennis. University of California Berkeley. ESPM; Estados Unidos Fil: Barr, Alan. University of Saskatchewan. Global Institute for Water Security; Canadá. Environment and Climate Change Canada. Climate Research Division; Canadá. Fil: Beamesderfer, Eric. McMaster University. School of Geography and Earth Sciences; Canadá. Fil: Belelli Marchesini, Luca. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia. RUDN University. Agrarian-Technological Institute. Department of Landscape Design and Sustainable Ecosystems; Rusia Fil: Bergeron, Onil. Ministère du Développement durable de l’Environnement et de la Lutte contre les Changements Climatiques. Direction du Marché du Carbone; Canadá. Fil: Beringer, Jason. University of Western Australia. School of Agriculture and Environment; Australia. Fil: Bernhofer, Christia. Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: Berveiller, Daniel. Université Paris-Saclay, CNRS, AgroParisTech, Ecologie Systématique et Evolution; Francia Fil: Billesbach, Dave. University of Nebraska-Lincoln. Biological Systems Engineering; Estados Unidos Fil: Black, Tomas Andrew. University of British Columbia. Faculty of Land and Food Systems; Canadá. Fil: Blanken, Peter D. University of Colorado. Department of Geography; Estados Unidos Fil: Bohrer, Gil. Ohio State University. Department of Civil, Environmental & Geodetic Engineering; Estados Unidos Fil: Boike, Julia Alfred. Wegener Institute. Helmholtz Centre for Polar and Marine Research; Alemania. Humboldt-Universität zu Berlin. Geography Department; Alemania Fil: Bolstad, Paul V. University of Minnesota. Forest Resources; Estados Unidos Fil: Bonal, Damien. Université de Lorraine, AgroParisTech, INRAE, UMR Silva; Francia Fil: Bonnefond, Jean - Marc. ISPA, Bordeaux Sciences Agro, INRAE; Francia Fil: Bowling, David R. University of Utah. School of Biological Sciences; Estados Unidos Fil: Bracho, Rosuel. University of Florida.School of Forest Resources and Conservation; Estados Unidos Fil: Brodeur, Jason. McMaster University. McMaster University Library; Estados Unidos Fil: Brummer, Christian. Federal Research Institute of Rural Areas, Forestry and Fisheries. Thünen Institute of Climate-Smart Agriculture; Alemania Fil: Buchmann, Nina. ETH. Department of Environmental Systems Science; Suiza Fil: Burban, Benoit. INRAE UMR ECOFOG; Guyana Francesa Fil: Burns, Sean P. University of Colorado. Department of Geography; Estados Unidos. National Center for Atmospheric Research. Mesoscale and Microscale Meteorology Laboratory; Estados Unidos Fil: Buysse, Pauline. Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS; Francia Fil: Cale, Peter. Australian Landscape Trust; Australia Fil: Cavagna, Mauro. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia Fil: Cellier, Pierre. Université Paris-Saclay. INRAE, AgroParisTech, UMR ECOSYS; Francia Fil: Che, Shiping. Chinese Academy of Sciences. Institute of Botany. State Key Laboratory of Vegetation and Environmental Change; China Fil: Chini, Issac. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia Fil: Christensen, Storben. Aarhus University. Arctic Research Center. Department of Bioscience; Dinamarca Fil: Cleverly, James. University of Technology. School of Life Sciences; Australia. University of Technology. Terrestrial Ecosystem Research Network; Australia. Fil: Collalti, Alessio. University of Tuscia. DiBAf; Italia. National Research Council of Italy. Institute for Agricultural and Forestry Systems in the Mediterranean; Italia Fil: Consalvo, Claudia. University of Tuscia. DiBAf; Italia. National Research Council of Italy. Research Institute on Terrestrial Ecosystems; Italia Fil: Cook, Bruce. NASA Goddard Space Flight Center. Biospheric Sciences Laboratory; Estados Unidos Fil: Cook, David. Argonne National Laboratory. Environmental Science Division; Estados Unidos Fil: Coursolle, Carole. Natural Resources Canada. Canadian Forest Service; Canadá. Université Laval. Faculté de Foresterie, de Géographie et de Géomatique. Centre d’étude de la Forêt; Canadá Fil: Cremonose, Edoardo. Environmental Protection Agency of Aosta Valley. Climate Change Unit; Italia Fil: Curtis, Peter. Ohio State University. Department of Evolution, Ecology, and Organismal Biology; Estados Unidos Fil: D'Andrea, Ettore. National Research Council of Italy. Institute for Agricultural and Forestry Systems in the Mediterranean; Italia Fil: da Rocha, Humberto. Universidade de São Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; Brasil Fil: Dai, Xiaoqin. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China. Fil: Davis, Kenneth. The Pennsylvania State University. Department of Meteorology and Atmospheric Science; Estados Unidos Fil: De Cinti, Bruno. National Research Council of Italy. Institute of Research on Terrestrial Ecosystems; Italia Fil: de Grandcourt, Agnes. UMR Eco&Sols, CIRAD; Francia Fil: De Ligne, Anne. University of Liege. TeRRA Teaching and Research Center; Bélgica Fil: De Oliveira, Raimundo C. Pedology, Embrapa Amazonia Oriental; Brasil. Fil: Delpierre, Nicolas. Université Paris-Saclay. CNRS, AgroParisTech, Ecologie Systématique et Evolution; Francia Fil: Desai, Ankur R. University of Wisconsin-Madison. Atmospheric and Oceanic Sciences; Estados Unidos Fil: Di Bella, Carlos Marcelo. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; Argentina Fil: di Tommasi, Paul. National Research Council of Italy. Institute for Agricultural and Forestry Systems in the Mediterranean; Italia Fil: Dolman, Han. Vrije Universiteit Amsterdam. Department of Earth Sciences; Holanda Fil: Domingo, Francisco. CSIC. Experimental Station of Arid Zones. Desertification and Geoecology Department; España Fil: Dong, Gang. Shanxi University. School of Life Science; China. Fil: Dore, Sabina. HydroFocus; Estados Unidos Fil: Duce, Pierpaolo. National Research Council of Italy. Institute of Bioeconomy; Italia Fil: Dufrêne, Eric. Université Paris-Saclay, CNRS., AgroParisTech. Ecologie Systématique et Evolution; Francia Fil: Dunn, Allison. Worcester State University. Department of Earth, Environment, and Physics; Estados Unidos Fil: Dušek, Jiri. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa Fil: Eamus Derek. University of Technology. School of Life Sciences; Australia. Fil: Eichelmann, Uwe.Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: ElKhidir, Hatim abdalla M. elObeid. Agricultural Research Corporation. Research Station; Sudán Fil: Eugster, Wener. ETH. Department of Environmental Systems Science; Suiza Fil: Ewenz, Cacilia M. Airborne Research Australia. TERN Ecosystem Processes Central Node; Australia Fil: Ewers, Brent. University of Wyoming. Department of Botany. Program in Ecology; Estados Unidos Fil: Famulari, Daniela. National Research Council of Italy. Institute for Agricultural and Forestry Systems in the Mediterranean; Italia Fil: Fares, Silvano. National Research Council of Italy. Institute of BioEconomy; Italia Fil: Feigenwinter, Iris. ETH. Department of Environmental Systems Science; Suiza Fil: Feitz, Andrew. Geoscience Australia; Australia Fil: Fensholt, Rasmus. University of Copenhagen. Department of Geosciences and Natural Resource Management; Dinamarca Fil: Filippa, Gianluca. Environmental Protection Agency of Aosta Valley. Climate Change Unit; Italia Fil: Fischer, Marc. Lawrence Berkeley National Laboratory. Energy Analysis & Environmental Impacts Division; Estados Unidos Fil: Frank, John. USDA Forest Service. Rocky Mountain Research Station; Estados Unidos Fil: Galvagno, Marta. Environmental Protection Agency of Aosta Valley. Climate Change Unit; Italia Fil: Gharun, Mana. ETH. Department of Environmental Systems Science; Suiza Fil: Gianelle, Damiano. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia Fil: Gielen, Bert. University of Antwerp. Department of Biology. Research Group PLECO; Bélgica Fil: Gioli, Beniamino. National Research Council of Italy. Institute of BioEconomy; Italia Fil: Gitelson, Anatoly. University of Nebraska-Lincoln. School of natural Resources; Estados Unidos Fil: Goded, Ignacio. Joint Research Centre, European Commission; Italia Fil: Goeckede, Mathias. Max Planck Institute for Biogeochemistry; Alemania Fil: Goldstein. Allen H. University of California Berkeley. ESPM; Estados Unidos Fil: Gough, Christopher M. Virginia Commonwealth University. Department of Biology; Estados Unidos Fil: Goulden, Michael L. University of California Irvine. Department of Earth System Science; Estados Unidos Fil: Graf, Alexander. Agrosphere. (IBG3), Forschungszentrum Jülich; Alemania Fil: Griebel, Anne. The University of Melbourne. School of Ecosystem and Forest Sciences; Australia Fil: Gruening, Carsten. Joint Research Centre, European Commission; Italia Fil: Grünwald, Thomas. Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: Hammerle, Albin. University of Innsbruck. Department of Ecology; Austria. Fil: Han, Shijie. Henan University. School of Life Sciences. International Joint Research Laboratory for Global Change Ecology; China. Chinese Academy of Sciences. Institute of Applied Ecology; China. Fil: Han, Xingguo. Chinese Academy of Sciences.Institute of Botany. State Key Laboratory of Vegetation and Environmental Change; China Fil: Hansen, Birger Ulf. University of Copenhagen. Department of Geosciences and Natural Resource Management; Dinamarca Fil: Hanson, Chad. Oregon State University. Department of Forest Ecosystems and Society; Estados Unidos Fil: , Juha Hatakka, Juha. Finnish Meteorological Institute; Finlandia Fil: He, Yongtao. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China. University of Chinese Academy of Sciences. College of Resources and Environment; China Fil: Hehn, Markus. Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: Heinesch, Bernard. University of Liege. TeRRA Teaching and Research Center; Bélgica Fil: Hinko-Najera, Nina. The University of Melbourne. School of Ecosystem and Forest Sciences; Australia. Fil: Hörtnagl, Lukas. ETH. Department of Environmental Systems Science; Suiza. Fil: Hutley, Lindsay. Charles Darwin University. Research Institute for the Environment and Livelihoods; Australia Fil: Ibrom, Andreas. Technical University of Denmark. Department of Environmental Engineering; Dinamarca Fil: Ikawa, Hiroki. National Agriculture and Food Research Organization. Institute for Agro-Environmental Sciences; Japón Fil: Jackowicz-Korczynski, Marcin. Lund University. Department of Physical Geography and Ecosystem Science; Suecia. Aarhus University. Arctic Research Center. Department of Bioscience; Dinamarca Fil: Janouš, Dalibor. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa Fil: Jans, Wilma. Wageningen University and Research. Wageningen Environmental Research; Holanda Fil: Jassal, Rachhpal. University of British Columbia. Faculty of Land and Food Systems; Canadá. Fil: Jiang, Shicheng. Ministry of Education, Northeast Normal University. Key Laboratory of Vegetation Ecology; China Fil: Kato, Tomomichi. Hokkaido University. Research Faculty of Agriculture; Japón. Hokkaido University. GICore; Japón Fil: Khomik, Myroslava. Geography and Environmental Management; Canadá. Fil: Klatt, Janina. Karlsruhe Institute of Technology. Institute of Meteorology and Climate Research; Alemania Fil: Knohl, Alexander. University of Goettingen. Bioclimatology; Alelmania. University of Goettingen. Centre of Biodiversity and Sustainable Land Use; Alemania Fil: Knox, Sara. The University of British Columbia. Department of Geography; Canadá Fil: Kobayashi, Hideki. Japan Agency for Marine-Earth Science and Technology. Research Institute for Global change, Institute of Arctic Climate and Environment Research; Japón Fil: Koerber, Georgia. University of Adelaide. Biological Sciences; Australia. Fil: Kolle, Olaf. Max Planck Institute for Biogeochemistry; Alemania Fil: Kosugi, Yoshiko. Kyoto University. Graduate School of Agriculture; Japón Fil: Kotani, Ayumi. Nagoya University. Graduate School of Bioagricultural Sciences; Japón Fil: Kowalski, Andrew. University of Granada. Department of Applied Physics; España Fil: Kruijt, Bart. Wageningen University, Wageningen. Water systems and Global Change group; Holanda Fil: Kurbatova; Julia A. Russian Academy of Sciences. Severtsov institute of Ecology and Evolution; Rusia Fil: Kutsch, Werner L. Integrated Carbon Observation System (ICOS ERIC). Head Office; Finlandia Fil: Kwon, Hyojung. Oregon State University. Department of Forest Ecosystems and Society; Estados Unidos Fil: Launiainen, Samuli. Natural Resources Institute Finland; Finlandia Fil: Laurila, Tuomas. Finnish Meteorological Institute; Finlandia Fil: Law, Bev. Oregon State University. Department of Forest Ecosystems and Society; Estados Unidos Fil: Leuning. Ray. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Li, Yingnian. Chinese Academy of Sciences. Northwest institute of Plateau Biology. Key Laboratory of Adaptation and Evolution of Plateau Biota; China Fil: Liddell, Michael. James Cook University. Centre for Tropical Environmental Sustainability Studies; Australia. Fil: Limousin, Jean-Marc. CEFE, CNRS, Univ Montpellier: Francia Fil: Lion, Marryanna. Forest Research Institute Malaysia. Forestry and Environment Division; Malasia Fil: Lohila, Annalea. University of Helsinki. Institute for Atmosphere and Earth System Research/Physics; Finlandia Fil: López-Ballesteros, Ana. Trinity College Dublin. School of Natural Sciences. Department of Botany; Irlanda Fil: López-Blanco, Efren. Aarhus University. Arctic Research Center. Department of Bioscience; Dinamarca Fil: Loubet, Benjamin. Université Paris-Saclay. INRAE, AgroParisTech, UMR ECOSYS; Francia Fil: Lucas-Moffat, Antje. Centre for Agrometeorological Research. German Meteorological Service; Alemania Fil: Lüers, Johannes. University of Bayreuth. Micrometeorology; Alemania. Bayreuth Center of Ecology and Environmental Research; Alemania Fil: Ma, Siyan. University of California Berkeley. ESPM; Estados Unidos Fil: Macfarlane, Craig. CSIRO Land and Water; Australia Fi: Magliulo, Vincenzo. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Mammarella, Ivan. University of Helsinki . Institute for Atmosphere and Earth System Research/Physics; Finlandia Fil: Manca, Giovanni. Joint Research Centre, European Commission; Italia Fil: Marras, Serena. University of Sassari. Department of Agriculture; Italia Fil: Massman, William. USDA Forest Service. Rocky Mountain Research Station; Estados Unidos Fil: Mastepanov, Mikhail. University of Oulu. Oulanka Research Station; Finlandia Fil: Matamala, Roser. Argonne National Laboratory. Environmental Science Division; Estados Unidos Fil: Matthes, Jaclyn Hatala. Wellesley College. Department of Biological Sciences; Estados Unidos Fil: , Mazzenga, Francesco. National Research Council of Italy. Research Institute on Terrestrial Ecosystems; Italia Fil: McCaughey, Harry. Queen’s University. Department of Geography and Planning; Canadá. Fil: McHugh, Ian. The University of Melbourne. School of Ecosystem and Forest Sciences; Australia. Fil: McMillan, Andrew M.S. Environmental Analytics nZ, Ltd.; Nueva Zelanda Fil: Merbold, Lutz. International Livestock Research Institute. Mazingira Centre; Kenia. Fil: Meyer, Wayne. University of Adelaide. Biological Sciences; Australia. Fil: Meyers, Tilden. NOAA/OAR/Air Resources Laboratory; Estados Unidos Fil: Miller, Scott D. State University of New York at Albany. Atmospheric Sciences Research Center; Estados Unidos Fil: Minerbi, Stefano. Forest Department of South Tyrol; Italia Fil: Monson, Russell K. University of Arizona. Department of Ecology and Evolutionary Biology; Estados Unidos Fil: Montagnani, Leonardo. Forest Department of South Tyrol; Italia. Free University of Bolzano. Faculty of Science and Technology; Italia Fil: Moore, Caitlin E. University of Illinois at Urbana-Champaign. Department of Plant Biology; Estados Unidos Fil: Moors, Eddy. IHE Delft: Holanda. VU Amsterdam. Faculty of Science; Holanda Fil: Moreaux, Virginie. University Grenoble Alpes; Francia Fil: Moureaux, Christine. University of Liege. TeRRA Teaching and Research Center; Bélgica Fil: Munger, J. William. Harvard University. School of engineering and Applied Sciences; Estados Unidos. Harvard University. Department of Earth and Planetary Sciences; Estados Unidos Fil: Nakai, Taro. National Taiwan University. School of forestry and Resource conservation; Taiwan. University of Alaska Fairbanks. International Arctic Research Center; Estados Unidos Fil: Neirynck, Johan. Research Institute for Nature and Forest. Environment and Climate; Bélgica Fil: Nesic, Zoran. University of British Columbia. Faculty of Land and Food Systems; Canadá Fil: Nicolini, Giacomo. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: Noormets, Asko. Texas A&M University. College Station. Department of Ecosystem Science and Management; Estados Unidos Fil: Northwood, Matthew. Charles Darwin University. Research Institute for the Environment and Livelihoods; Australia Fil: Nosetto, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Matemática Aplicada San Luis. Grupo de Estudios Ambientales. Universidad Nacional de San Luis; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentina Fil: Nouvellon, Yann. Université Montpellier-CIRAD-INRA-IRD-Montpellier SupAgro. Eco&Sols; Francia Fil: Novick, Kimberly O’Neill. Indiana University Bloomington. School of Public and Environmental Affairs; Estados Unidos Fil: Oechel, Walter. San Diego State University. Department of Biology. Global Change Research Group; Estados Unidos. University of Exeter. College of Life and Environmental Sciences. Department of Geography; Reino Unido Fil: Olesen, Jørgen Eivind. Aarhus University. Department of Agroecology; Dinamarca. Aarhus University. iCLIMATE; Dinamarca Fil: Ourcival, Jean-Marc. CEFE, CNRS, Université Montpellier; Francia Fil: Papuga, Shirley A. Wayne State University. Department of Geology; Estados Unidos Fil: Parmentier, Frans-Jan. University of Oslo. Department of Geosciences; Noruega Fil: Paul-Limoges, Eugenie. University of Zurich. Department of Geography; Suiza Fil: Pavelka, Marian. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa Fil: Peichl, Matthias. Swedish University of Agricultural Sciences. Department of Forest Ecology and Management; Suecia Fil: Pendall, Elise. Western Sydney University. Hawkesbury Institute for the Environment; Australia Fil: Phillips, Richard P. Indiana University Bloomington. Department of Biology; Estados Unidos Fil: Pilegaard, Kim. Technical University of Denmark. Department of Environmental Engineering; Dinamarca Fil: Pirk, Norbert. CSiRO Land and Water; Australia. Fil: Posse Beaulieu, Gabriela. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina Fil: Powell, Thomas. Lawrence Berkeley National Laboratory. Climate & Ecosystem Sciences Division; Estados Unidos Fil: Prasse, Heiko. Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: Reed, David. Michigan State University. Center for Global Change & Earth Observations: Estados Unidos. Michigan State University. Center for Global Change & Earth Observations; Estados Unidos Fil: Resco de Dios, Víctor. Western Sydney University. Hawkesbury Institute for the Environment; Australia. Southwest University of Science and Technology. School of Life Science and Engineering; China Fil: Restrepo-Coupe, Natalia. University of Arizona. Department of Ecology and Evolutionary Biology; Estados Unidos Fil: Reverter, Borja R. Universidade Federal da Paraiba. Departamento de Química e Física; Brasil Fil: Roland, Marilyn. University of Antwerp. Department of Biology. Research Group PLECO; Bélgica Fil: Sabbatini, Simone. University of Tuscia. DiBAf; Italia. Fil: Sachs, Torsten. GfZ German Research Centre for Geosciences. Remote Sensing and Geoinformatics; Alemania Fil: Saleska, Scott R. University of Arizona. Department of Ecology and Evolutionary Biology; Estados Unidos Fil: Sánchez-Cañete, Enrique P. University of Granada. Department of Applied Physics; España. Andalusian Institute for Earth System Research (CEAMA-IISTA); España Fil: Sanchez-Mejia, Zulia M. Instituto Tecnológico de Sonora. Ciencias del Agua y Medioambiente; México Fil: Schmid, Hans Peter. Karlsruhe Institute of Technology. Institute of Meteorology and Climate Research; Alemania Fil: Schmidt, Marius. Agrosphere (IBG3), Forschungszentrum Jülich; Alemania Fil: Schneider, Karl. University of Cologne. Geographical Institute; Alemania Fil: Schrader, Frederik Thünen. Federal Research Institute of Rural Areas, Forestry and Fisheries. Institute of Climate-Smart Agriculture; Alemania Fil: Schroder, Ivan. Geoscience Australia. Department of industry, Innovation and Science; Australia. Fil: Scott, Russell L. USDA-ARS. Southwest Watershed Research Center; Estados Unidos Fil: Sedlák, Pavel. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa. Institute of Atmospheric Physics of the Czech Academy of Sciences; República Checa Fil: Serrano-Ortíz, Penélope. Andalusian Institute for Earth System Research (CEAMA-IISTA); España. University of Granada. Department of Ecology; España Fil: Shao, Changliang. Chinese Academy of Agricultural Sciences. National Hulunber Grassland Ecosystem Observation and Research Station & Institute of Agricultural Resources and Regional Planning; China. Fil: Shi, Peili. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China. Fil: Shironya, Ivan A.n. Russian Academy of Sciences. Severtsov Institute of Ecology and Evolution; Rusia Fil: Siebicke, Lukas. University of Goettingen. Bioclimatology; Alemania Fil: Šigut, Ladislav. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa Fil: Silberstein, Richard. University of Western Australia. School of Agriculture and Environment; Australia. Edith Cowan University. School of Science; Australia. Fil: Sirca, Costantino. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia. University of Sassari. Department of Agriculture; Italia Fil: Spano, Donatella. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia. University of Sassari. Department of Agriculture; Italia Fil: Steinbrecher, Rainer. Karlsruhe Institute of Technology. Institute of Meteorology and Climate Research; Alemania Fil: Stevens, Robert M. Sentek Pty Ltd.; Australia Fil: Sturtevant, Cove. National ecological Observatory Network Program; Estados Unidos Fil: Suyker, Andy. University of Nebraska-Lincoln. School of Natural Resources; Estados Unidos Fil: Tagesson, Torbem. Lund University. Department of Physical Geography and Ecosystem Science; Suecia. University of Copenhagen. Department of Geosciences and Natural Resource Management; Dinamarca Fil: Takanashi, Satoru. Forestry and Forest Products Research Institute. Kansai Research Center; Japón Fil: Tang, Yanhong. Peking University. College of Urban and Environmental Sciences; China. Fil: Tapper, Nigel. Monash University. School of Earth, Atmosphere and Environment; Australia Fil: Thom, Jonathan. University of Wisconsin-Madison. Space Science and Engineering Center; Estados Unidos Fil: Tiedemann, Frank. University of Goettingen. Bioclimatology; Alemania Fil: Tomassucci, Michele. University of Tuscia. DiBAf; Italia. Terrasystem srl; Italia Fil: Tuovinen, Juha-Pekka. Finnish Meteorological Institute; Finlandia Fil: Urbanski, Shawn. USDA Forest Service. Rocky Mountain Research Station; Estados Unidos Fil: Valentini, Riccardo. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: van der Molen, Michiel. Wageningen University. Meteorology and Air Quality Group; Holanda Fil: van Gorsel, Eva. Australian National University Canberra. Fenner School of Environment and Society; Australia. Fil: van Huissteden, Ko. Vrije Universiteit Amsterdam. Department of Earth Sciences; Holanda Fil: Varlagin, Andrej. Agroscope Research Institute. Department of Agroecology and Environment; Suiza Fil: Verfaillie, Joseph. University of California Berkeley. ESPM; Estados Unidos Fil: Vesala, Timo. University of Helsinki. Institute for Atmosphere and Earth System Research/Physics; Finlandia Fil: Vincke, Caroline. Chinese Academy of Sciences. South China Botanical Garden; China. Fil: Vitale, Domenico. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: Vygodskaya, Natalia. University of Bayreuth. Micrometeorology; Alemania Fil: Walker, Jeffrey P. Monash University. Department of Civil Engineering; Australia Fil: Walter-Shea, Elizabeth. University of Nebraska-Lincoln. School of natural Resources; Estados Unidos Fil: Wang, Huimin. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China Fil: Weber, Robin. University of California Berkeley. ESPM; Estados Unidos Fil: Westermann, Sebastian. Instituto Nacional de Tecnologia Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Fil: Wille, Christian. GfZ German Research centre for Geosciences. Remote Sensing and Geoinformatics; Alemania Fil: Wofsy, Steven. Harvard University. School of engineering and Applied Sciences; Estados Unidos. Harvard University. Department of Earth and Planetary Sciences; Estados Unidos Fil: Wohlfahrt, Georg. University of Innsbruck. Department of Ecology; Austria. Fil: Woodgate, William. CSIRO Land and Water; Australia. Fil: Li, Yuelin. Chinese Academy of Sciences. South China Botanical Garden; China. Fil: Zampedri, Roberto. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia Fil: Zhang, Junhui. Chinese Academy of Sciences. Institute of Applied Ecology; China. Fil: Zhou, Guoyi. Nanjing University of Information Science & Technology. College of Applied Meteorology; China. Fil: Zona, Donatella. San Diego State University. Department of Biology. Global Change Research Group; Estados Unidos. University of Sheffield. Department of Animal and Plant Sciences; Reino Unido Fil: Agarwal, Deb. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Biraud, Sebastien. Lawrence Berkeley National Laboratory. Climate & Ecosystem Sciences Division; Estados Unidos Fil: Torn, Margaret. Lawrence Berkeley National Laboratory. Climate & Ecosystem Sciences Division; Estados Unidos Fil: Papale, Dario. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia

Background Traumatic injury is one of the main reasons for temporary and permanent occupational disability. The objective of this study was to define the role of socio-economic position on post-injury occupational absenteeism. Methods This was a nationwide retrospective cohort study, based on linking The Israeli National Trauma Registry (INTR) and the National Insurance Institute (NII) databases. The study population included 44,740 injured workers (residents of Israel, aged 21–67, hospitalized between 2008 and 2013 and employed prior to injury as salaried workers). Logistic-regression models tested the probability of not returning to work (RTW). Results The majority of the study population (61%) RTW within 1 month following the injury event. Income prior to injury was significantly associated with longer out of work stay, explaining 9% variance. A significant interaction (p value

The absorption of Photosynthetically Active Radiation (PAR) by different foliar pigments defines the amount of energy available for photosynthesis and also the need for photoprotection. Both characteristics reveal essential information about productivity, development, and stress acclimation of plants. Here we present an approach for the estimation of the efficiency by three foliar pigment groups (chlorophylls, carotenoids, and anthocyanins) at capturing light, via the absorption coefficient derived from leaf reflectance spectra. The absorption coefficient (and hence light capture efficiency) of the pigment is quantitatively related to the ratio of light absorbed by each pigment group over the total amount of light absorbed by the leaf. The proposed approach allows discerning the contribution of pigment groups to the overall light absorption, despite the strong interference by other pigments with overlapping absorption spectra. For photosynthetic pigments, like chlorophylls, this is indicative of the energy captured for photosynthesis and hence of potential plant productivity. For photoprotective pigments, like anthocyanins or secondary carotenoids, it gives information about the spectral ranges where their optical screening works best and their screening capacity. In addition, the approach allows the selection of optimal spectral bands where different pigments operate. Such information improves our understanding of the phenological, physiological and photosynthetic dynamics of plants over space and through time, useful for developing better monitoring and management strategies.

Semi-arid forests represent some of the most sensitive ecosystems to climate change. Identifying adjustments to extreme conditions can indicate their resilience, and that of forests undergoing increasing aridity trends. We used eddy covariance and close-range sensing measurements over four years in a semi-arid pine forest to identify canopy-scale adjustments to the short active season and long seasonal drought. Peaks in light use efficiency (LUE), leaf chlorophyll content (LCC), and increasing absorbed photosynthetic active radiation (APAR; based on canopy absorption coefficient in the green range), all converged to support an early peak (March) in gross primary productivity (GPP), exploiting the narrow optimum between PARin, temperature and the rapidly decreasing soil moisture in spring. In contrast, during the long dry period (>200 days), while PARin increased, LCC and LUE decreased, offering physiological photoprotection as GPP sharply declined under the stressful conditions. The strong negative correlation between ρNIR and PARin indicated canopy biophysical adjustments that enhance light absorption under low radiation and eliminate photodamage under excessive radiation. The results provide clear indications of canopy-scale adjustments underlying the high productivity of the forest and its resistance to the harsh conditions, which may soon apply to forests in currently milder climatic regions.

Background Despite relatively high rates of Postpartum Depression (PPD), little is known about the granting of social security benefits to women who are disabled as a result of PPD or of other postpartum mood and anxiety disorders (PMAD). This study aims to identify populations at risk for underutilization of social security benefits due to PMAD among Israeli women, with a focus on ethnic minorities. Methods This retrospective cohort study is based on the National Insurance Institute (NII) database. The study population included a simple 10% random sample of 79,391 female Israeli citizens who gave birth during 2008–2016 (these women delivered a total of 143,871 infants during the study period), and who had not been eligible for NII mental health disability benefits before 2008. The dependent variable was receipt of Benefit Entitlement (BE) due to mental illness within 2 years following childbirth. Maternal age at delivery, population group, Socio-Economic Status (SES), family status, employment status of the mother and her spouse, and infant mortality were the independent variables. Left truncation COX proportional hazard model with time-dependent variables was used, and birth number served as a time discrete variable. Results Bedouin and Arab women had significantly lower likelihood of BE (2.6 times lower and twice lower) compared with other ethnic groups (HR = 0.38; 95% CI: 0.26–0.56; HR = 0.47; 95% CI: 0.37–0.60 respectively). The probability of divorced or widowed women for BE was significantly higher compared to those living with a spouse (HR = 3.64; 95% CI: 2.49–5.33). Lack of employment was associated with higher likelihood of BE (HR = 1.54; 95% CI: 1.30–1.82). Income had a dose-response relationship with BE in multivariable analysis: lower income was associated with the nearly four-fold greater probability compared to the highest income quartile (HR = 3.83; 95% CI: 2.89–5.07). Conclusions Despite the exceptionally high prevalence of PMAD among ethnic minorities, Bedouins and Arabs had lowest likelihood of Benefit Entitlement. In addition to developing programs for early identification of postpartum emotional disorders among unprivileged ethnic groups, awareness regarding entitlement to a mental health disability allowance among ethnic minorities should be improved.

Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in‐water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n = 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions.

This is the first study to obtain a high-purity luciferase from the fungus Neonothopanus nambi biomass that is suitable for subsequent sequencing.

Established reflectance-based approaches for estimation of foliar pigment contents assume close relationship between leaf absorbance and reflectance. Complex organization and high pigment content of leaves may lead to violation of the essential assumptions under Kubleka-Munk theory relating reflectance and absorbance. We compared relationships of absorbance and reciprocal reflectance vs. pigment content in leaves collected across species, developmental stages and physiological states. As a result, limitations of reflectance-based spectroscopy for pigment quantification were revealed. We deduced in situ absorbance of foliar chlorophylls, carotenoids, and flavonoids (including red-colored anthocyanins) and introduced a concept of specific spectral response of the optical properties to each pigment group. Quantitative criteria of spectral range selection for the absorbance- and reflectance-based techniques yielding effect of each pigment on the background of other pigment absorption were suggested and validated. We argue that absorbance- and reflectance-based approaches to pigment estimation complement each other and can be used synergistically in advanced models for accurate estimating foliar pigments. The study provides a deeper insight into interception of light by photosynthetic and photoprotective pigments as function of physiological condition and developmental stage, which is important for plant biology as well as knowledge-driven selection of spectral bands for noninvasive pigment estimation models.

Surface reflectance data acquired in red and near-infrared spectra by remote sensing sensors are traditionally applied to construct various vegetation indices (VIs), which are related to vegetation biophysical parameters. Most VIs use pre-defined weights (usually equal to 1) for the red and NIR reflectance values, therefore constraining particular weights for red and NIR during the VI design phase, and potentially limiting capabilities of the VI to explain an independent variable. In this paper, we propose an approach to estimate biophysical variables, such as Leaf Area Index (LAI), Canopy Chlorophyll Content (CCC) and Fraction of Photosynthetically Active Radiation (FPAR) absorbed by green vegetation, represented as linear combinations of the red and NIR reflectances with weights determined empirically from observations and radiative transfer model (PROSAIL) simulations. The proof of concept is first tested on available close-range observations over maize and soybean crops in Nebraska, USA. The empirical results compare well with those from PROSAIL model simulations. The proposed LAI model is then used with data from Landsat 8, Sentinel-2 and Planet/Dove, and the results are validated with in situ LAI measurements in Ukraine. We show that the weights on red and NIR reflectances are vegetation-specific and stable in time. The approach is further tested on crops and forests in the conterminous USA and on a global scale using MODIS LAI and FPAR products as proxies for “ground observations”. These LAI and FPAR, however, are not independently measured but derived from the corresponding remotely sensed reflectances, which precludes recommending a final set of the weights/coefficients for the users, and, thus, should be considered mostly for demonstrating the concept. The results for crop types, other than maize and soybean, and for all forests are conceptual and need to be tested with real ground data. It was, however, encouraging to see that the derived maps of coefficients/weights exhibit regular patterns over the globe compatible with those of vegetation classes and crop types. Tedious and thorough work on compiling available in situ measurements on various crops and forests needs to be accomplished prior to large-scale applications, and the method needs to be further tested and proven that it works at a large scale. The proposed parameterization may be attractive for global studies of various sub-classes of vegetation, once the parameter coefficients are established, validated, tabulated and their stability verified. Ultimately, this approach may provide quantification of vegetation traits for the past decades and be a useful asset for climate models that include satellite-derived land cover classifications and vegetation variables for simulating surface fluxes. This is a conceptual paper, with a proof-of-concept supported by observations over two crops, for which we had close-range observations. It is not a technical note, which would provide users with a recommended set of coefficients for global applications. Our intent was to develop a paradigm, which could ultimately be useful in global models.

The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions. © 2021, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.

Absorption of radiation in the photosynthetically active radiation (PAR) region is significantly influenced by plant biochemistry, structural properties, and photosynthetic pathway. To understand and quantify the effects of these traits on absorbed PAR it is necessary to develop practical and reliable tools that are sensitive to these traits. Using a semi-analytical modeling framework for deriving the absorption coefficient of plant canopies from reflectance spectra, we quantify the effects of functional, structural and biochemical traits of vegetation on the relationship between the absorption coefficient in the PAR region (αpar) with canopy characteristics such as the fraction of PAR absorbed by photosynthetically active vegetation (fAPARgreen) and chlorophyll (Chl) content. The reflectance dataset used in the study included simulated data obtained from a canopy reflectance model (PROSAIL) and empirical data on three diverse crop species with different leaf structures, canopy architectures and photosynthetic pathways (rice, maize and soybean) acquired at proximal (i.e., using field spectroradiometers) and remote (i.e., Landsat TM and ETM+) distances. Results show the usefulness of αpar derived from reflectance data for assessing not only the photosynthetic status of vegetation, but also the effects of different functional, structural and biochemical traits on plant performance. Furthermore, these assessments can be made using data acquired by satellite sensor systems such as the Landsat series, which are available since the 1980s, thus facilitating the analysis of the photosynthetic status of terrestrial ecosystems throughout the world with a high temporal depth.

Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in-water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n = 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions.

Foliar pigment contents and composition are main factors governing absorbed photosynthetically active radiation, photosynthetic activity and physiological status of vegetation. In this study the performance of non-destructive techniques based on leaf reflectance were tested for estimating chlorophyll (Chl) and anthocyanin (AnC) contents in species with widely variable leaf structure, pigment content and composition. Only three spectral bands (green, red edge and NIR) are required for nondestructive Chl and AnC estimation with NRMSE below 4.5% and 6.1%, respectively. The algorithms developed are generic not requiring re-parameterization for each species allowing for accurate non-destructive Chl and AnC estimation using simple hand-held field/lab instrumentation. They also have potential in interpretation of airborne and satellite data.

Secreted reporter proteins provide monitoring of intracellular events in real time without cell destruction. To create human melanoma cell lines that enables noninvasive bioluminescent monitoring of metabolic activity, a comparison of the efficiency of isoforms and mutant variants of luciferase from the Metridia longa as secreted reporter proteins in the cells of human melanoma lines Mel IL was conducted. The MLM3 deletion mutant had the highest activity in the medium of two studied isoforms and two deletion mutants of secreted M. longa luciferase during the Mel IL melanoma cell transfection. It was established that optimization of the gene structure of the selected MLM3 variant for expression in human cells increases the level of bioluminescent activity in the Mel IL cells by almost an order of magnitude. A stable Mel IL melanoma cell line with constitutive expression of the humanized hMLM3 reporter gene was obtained and characterized. The linear range of identification of living cells by the hMLM3 reporter activity was more than three orders of magnitude with a sensitivity of detection of 10 cells.

International audience; Leaf pigments provide valuable information about plant physiology. High resolution monitoring of their dynamics will give access to better understanding of processes occurring at different scales, and will be particularly important for ecologists, farmers, and decision makers to assess the influence of climate change on plant functions, and the adaptation of forest, crop, and other plant canopies. In this article, we present a new version of the widely-used PROSPECT model, hereafter named PROSPECT-D for dynamic, which adds anthocyanins to chlorophylls and carotenoids, the two plant pigments in the current version. We describe the evolution and improvements of PROSPECT-D compared to the previous versions, and perform a validation on various experimental datasets. Our results show that PROSPECT-D outperforms all the previous versions. Model prediction uncertainty is decreased and photosynthetic pigments are better retrieved. This is particularly the case for leaf carotenoids, the estimation of which is particularly challenging. PROSPECT-D is also able to simulate realistic leaf optical properties with minimal error in the visible domain, and similar performances to other versions in the near infrared and shortwave infrared domains.; Les pigments foliaires fournissent des informations précieuses sur la physiologie des plantes. Le suivi fin de leur dynamique pour permettre de mieux comprendre les processus qui se produisent à différentes échelles et sera particulièrement important pour les écologues, les agriculteurs et les décideurs pour évaluer l'influence du changement climatique sur les fonctions des plantes et l'adaptation des forêts, des cultures, et de la végétation en général. Dans cet article, nous présentons une nouvelle version du modèle PROSPECT largement utilisé, appelé PROSPECT-D, pour Dynamique, qui ajoute les anthocyanes aux chlorophylles et aux caroténoïdes, les deux pigments végétaux pris en compte jusqu'à la version présente. Nous décrivons l'évolution et les améliorations de PROSPECT-D par rapport aux versions précédentes, et réalisons une validation sur différents jeux de données expérimentales. Nos résultats montrent que PROSPECT-D surpasse toutes les versions précédentes. L'incertitude de prédiction du modèle est diminuée et les pigments photosynthétiques sont mieux récupérés. C'est particulièrement le cas des caroténoïdes foliaires, dont l'estimation est particulièrement difficile. PROSPECT-D est également capable de simuler des propriétés optiques réalistes de la feuille avec une erreur minimale dans le domaine visible et des performances similaires aux précédentes versions dans le domaine infrarouge.

The goal of this study was to explore the relationships between stand-level photochemical reflectance index (PRI) and canopy structure/pigment pools, as well as light use efficiency (LUE) of photosynthetically active vegetation focusing on seasonal or ontogenetic time frames. PRI was originally designed as a means of assessing the xanthophyll cycle and LUE over short (e.g. diurnal) time frames, and few studies have explored the drivers of PRI over longer, seasonal time frames, particularly in crops having different photosynthetic pathways or canopy structures. Consequently, our purpose was to understand and quantify the drivers of PRI responses over seasonal time scales for two crops, maize (C4) and soybean (C3), contrasting in photosynthetic pathway, leaf structure and canopy architecture. In both crops, PRI was very closely related to green LAI (R2 > 0.90) and stand chlorophyll (Chl) content (R2 > 0.93). The slopes of the relationships in different phenological stages, vegetative and reproductive, were substantially different (3-fold smaller in the vegetative stage). The main cause of this disparity was the high PRI value of soil/residue background. While PRI was a sensitive indicator of the changes in stand green LAI and stand Chl content over the full growing season, it was not sensitive to LUE; LUE explained below 12% of PRI variation in maize and 19% in soybean. Unlike leaf-level PRI, stand-level PRI was not clearly related to the Car/Chl ratio, presumably because the large changes in canopy structure (affecting stand Chl and green LAI) had a dominant influence on PRI over this time frame. The strong relationship between PRI and stand Chl content as well as between PRI and Chl-related vegetation index over a growing cycle allowed us to subtract the stand Chl content effect from measured PRI to reveal the component of PRI most likely related to periods of stress. However, for accurate subtraction of the Chl effect from long-term PRI records, thoughtful study of uncertainties related to “natural” variation of PRI-stand Chl relationships, and stand Chl content estimation for different varieties of the same species and for different species is required. The findings of a strong link between stand-level PRI and stand green LAI and Chl content and the lack of a clear relationship between PRI and LUE over seasonal and ontogenetic time spans suggest the need for a more careful evaluation of the relationship between PRI and either LUE or photosynthetic activity. In particular, studies that contrast short-term (e.g. diurnal) vs. long-term (e.g. seasonal) pigment, PRI, and photosynthetic responses in contrasting vegetation types are needed to clarify the different mechanisms involved at different temporal and spatial scales. These findings have important implications for attempts to monitor photosynthetic phenology from remote sensing, many of which have relied on PRI as an indicator of photosynthetic activity.

Given the challenge of consistent physical activity among aging adults, it is important to find ways to increase physical activity levels in this population. Participants in the Senior Olympic Games may extend their sport participation through the use of mental skills. This study examined the relationship between mental skills use by Senior Olympic Games participants and their grit, or passion and perseverance, toward a long-term goal. The participants in the Arizona Senior Olympic Games (n = 304) completed an online survey of mental skills use (Athletic Coping Skills Inventory) and grit (Grit Scale-Short). Based on the ongoing validity and reliability issues of the Grit Scale-Short, two regression models were examined, with consistency of interests (passion) and perseverance of effort (perseverance) as dependent variables. After controlling for age and sex, mental skills accounted for 15.2% of the variance in consistency of interests and 13.1% of the variability in perseverance of effort. The results are discussed in light of the findings.

This paper presents the preliminary results of the separation of the Chaetopterus variopedatus bioluminescent system into luciferin and luciferase and a brief description of some of their properties.

The paper arises from the Workshop on “Exploring the Earth’s Ecosystems on a Global Scale: Requirements, Capabilities and Directions in Spaceborne Imaging Spectroscopy”, held at the International Space Science Institute (ISSI), Bern—Switzerland, on 21–25 November 2016. Agriculture faces the challenge of providing food, fibre and energy from limited land resources to satisfy the changing needs of a growing world population. Global megatrends, e.g., climate change, influence environmental production factors; production and consumption thus must be continuously adjusted to maintain the producer–consumer-equilibrium in the global food system. While, in some parts of the world, smallholder farming still is the dominant form of agricultural production, the use of digital information for the highly efficient cultivation of large areas has become part of agricultural practice in developed countries. Thereby, the use of satellite data to support site-specific management is a major trend. Although the most prominent use of satellite technology in farming still is navigation, Earth Observation is increasingly applied. Some operational services have been established, which provide farmers with decision-supporting spatial information. These services have mostly been boosted by the increased availability of multispectral imagery from NASA and ESA, such as the Landsat or Copernicus programs, respectively. Using multispectral data has arrived in the agricultural commodity chain. Compared to multispectral data, spectrally continuous narrow-band sampling, often referred to as hyperspectral sensing, can potentially provide additional information and/or increased sampling accuracy. However, due to the lack of hyperspectral satellite systems with high spatial resolution, these advantages mostly are not yet used in practical farming. This paper summarizes where hyperspectral data provide additional value and information in an agricultural context. It lists the variables of interest and highlights the contribution of hyperspectral sensing for information-driven agriculture, preparing the application of future operational spaceborne hyperspectral missions.

For successful application of nondestructive approaches for assessment of such key plant pigments as chlorophylls, carotenoids, anthocyanins, and flavonoids, in situ quantification of the effect of each pigment on absorbance and reflectance is required. We introduce a concept of specific absorbance and reflectance responses to each pigment's content, revealing the quantitative effect of each pigment on the background of absorption of other pigments and leaf scattering. We present models for accurate estimation of chlorophyll, carotenoid, anthocyanin, and flavonoid contents and argue that absorbance- and reflectance-based approaches complement each other and can be used synergistically in advanced models for precision estimation of foliar pigments. The findings summarized in the chapter enable a better understanding of light interaction with leaf augmenting for plant physiologists studying stress and developmental effects in plants.

Significance We present identification of the luciferase and enzymes of the biosynthesis of a eukaryotic luciferin from fungi. Fungi possess a simple bioluminescent system, with luciferin being only two enzymatic steps from well-known metabolic pathways. The expression of genes from the fungal bioluminescent pathway is not toxic to eukaryotic cells, and the luciferase can be easily co-opted to bioimaging applications. With the fungal system being a genetically encodable bioluminescent system from eukaryotes, it is now possible to create artificially bioluminescent eukaryotes by expression of three genes. The fungal bioluminescent system represents an example of molecular evolution of a complex ecological trait and with molecular details reported in the paper, will allow additional research into ecological significance of fungal bioluminescence., Bioluminescence is found across the entire tree of life, conferring a spectacular set of visually oriented functions from attracting mates to scaring off predators. Half a dozen different luciferins, molecules that emit light when enzymatically oxidized, are known. However, just one biochemical pathway for luciferin biosynthesis has been described in full, which is found only in bacteria. Here, we report identification of the fungal luciferase and three other key enzymes that together form the biosynthetic cycle of the fungal luciferin from caffeic acid, a simple and widespread metabolite. Introduction of the identified genes into the genome of the yeast Pichia pastoris along with caffeic acid biosynthesis genes resulted in a strain that is autoluminescent in standard media. We analyzed evolution of the enzymes of the luciferin biosynthesis cycle and found that fungal bioluminescence emerged through a series of events that included two independent gene duplications. The retention of the duplicated enzymes of the luciferin pathway in nonluminescent fungi shows that the gene duplication was followed by functional sequence divergence of enzymes of at least one gene in the biosynthetic pathway and suggests that the evolution of fungal bioluminescence proceeded through several closely related stepping stone nonluminescent biochemical reactions with adaptive roles. The availability of a complete eukaryotic luciferin biosynthesis pathway provides several applications in biomedicine and bioengineering.

Climate change can impose large offsets between the seasonal cycle of photosynthesis and that in solar radiation and temperature which drive it. Ecophysiological adjustments to such offsets in forests growing under hot and dry conditions are critical for maintaining carbon uptake and survival. Here, we investigate the adjustments that underlie the unusually short and intense early spring productive season, under suboptimal radiation and temperature conditions in a semi-arid pine forest. We used eddy covariance flux, meteorological, and close-range sensing measurements, together with leaf chlorophyll content over four years in a semi-arid pine forest to identify the canopy-scale ecophysiological adjustments to the short active season, and long seasonal drought. The results reveal a range of processes that intricately converge to support the early spring peak (March) in photosynthetic activity, including peaks in light use efficiency, leaf chlorophyll content, increase in the absorption of solar radiation, and high leaf scattering properties (indicating optimizing leaf orientation). These canopy-scale adjustments exploit the tradeoffs between the yet increasing temperature and solar radiation, but the concurrently rapidly diminishing soil moisture. In contrast, during the long dry stressful period with rapidly declining photosynthesis under high and potentially damaging solar radiation, physiological photoprotection was conferred by strongly relaxing the early spring adjustments. The results provide evidence for canopy-scale ecophysiological adjustments, detectable by spectral measurements, that support the survival and productivity of a pine forest under the hot and dry conditions, which may apply to large areas in the Mediterranean and other regions in the next few decades due to the current warming and drying trends.

a b s t r a c t Different models driven by remotely sensed vegetation indexes (VIs) and incident photosynthetically active radiation (PAR) were developed to estimate gross primary production (GPP) in a subalpine grass- land equipped with an eddy covariance flux tower. Hyperspectral reflectance was collected using an automatic system designed for high temporal frequency acquisitions for three consecutive years, includ- ing one (2011) characterized by a strong reduction of the carbon sequestration rate during the vegetative season. Models based on remotely sensed and meteorological data were used to estimate GPP, and a cross-validation approach was used to compare the predictive capabilities of different model formula- tions. Vegetation indexes designed to be more sensitive to chlorophyll content explained most of the variability in GPP in the ecosystem investigated, characterized by a strong seasonal dynamic. Model performances improved when including also PARpotential defined as the maximal value of incident PAR under clear sky conditions in model formulations. Best performing models are based entirely on remotely sensed data. This finding could contribute to the development of methods for quantifying the temporal variation of GPP also on a broader scale using current and future satellite sensors.

Canopy chlorophyll content (CCC) is an essential ecophysiological variable for photosynthetic functioning. Remote sensing of CCC is vital for a wide range of ecological and agricultural applications. The objectives of this study were to explore simple and robust algorithms for spectral assessment of CCC. Hyperspectral datasets for six vegetation types (rice, wheat, corn, soybean, sugar beet and natural grass) acquired in four locations (Japan, France, Italy and USA) were analysed. To explore the best predictive model, spectral index approaches using the entire wavebands and multivariable regression approaches were employed. The comprehensive analysis elucidated the accuracy, linearity, sensitivity and applicability of various spectral models. Multivariable regression models using many wavebands proved inferior in applicability to different datasets. A simple model using the ratio spectral index (RSI; R815, R704) with the reflectance at 815 and 704 nm showed the highest accuracy and applicability. Simulation analysis using a physically based reflectance model suggested the biophysical soundness of the results. The model would work as a robust algorithm for canopy-chlorophyll-metre and/or remote sensing of CCC in ecosystem and regional scales. The predictive-ability maps using hyperspectral data allow not only evaluation of the relative significance of wavebands in various sensors but also selection of the optimal wavelengths and effective bandwidths.