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METEOROLOGICAL DATA --- SOLAR RADIATION --- TEMPERATURE --- SUNLIGHT --- CLIMATE --- DIURNAL VARIATIONS --- INSOLATION --- IRRADIANCE --- TIME --- HEATING LOAD --- HEAT GAIN --- BUILDINGS --- HOUSES --- SOLAR ENERGY --- METEOROLOGICAL DATA --- SOLAR RADIATION --- TEMPERATURE --- SUNLIGHT --- CLIMATE --- DIURNAL VARIATIONS --- INSOLATION --- IRRADIANCE --- TIME --- HEATING LOAD --- HEAT GAIN --- BUILDINGS --- HOUSES --- SOLAR ENERGY

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In recent years, several projects and studies have been launched towards the development and use of new methodologies, in order to assess, monitor, and support clean forms of energy. Accurate estimation of the available energy potential is of primary importance, but is not always easy to achieve. The present Special Issue on ‘Renewable Energy Resource Assessment and Forecasting’ aims to provide a holistic approach to the above issues, by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today’s technical, socio-economic, and decision-making needs. In particular, research papers, reviews, and case studies on the following subjects are presented: wind, wave and solar energy; biofuels; resource assessment of combined renewable energy forms; numerical models for renewable energy forecasting; integrated forecasted systems; energy for buildings; sustainable development; resource analysis tools and statistical models; extreme value analysis and forecasting for renewable energy resources.

Research & information: general --- short-term forecasts --- direct normal irradiance --- concentrating solar power --- system advisor model --- operational strategies --- central solar receiver --- solar irradiance forecasts --- numerical weather prediction model --- different horizontal resolution --- forecast errors --- validation --- ramp rates --- renewable energy forecasting --- solar radiation --- shark algorithm --- particle swarm optimization --- ANFIS --- nowcasting --- Kalman-Bayesian filter --- WRF --- high-resolution --- complex terrain --- wind --- solar irradiation --- photovoltaic solar energy --- deep learning --- prediction --- biofuel --- risk analysis --- sustainable development --- renewable energy --- biomass --- biotechnology --- anthropogenic waste processing --- energy resource assessment --- tidal-stream energy --- thrust force coefficient --- momentum sink --- unbounded flow --- open channel flows --- shock-capturing capability --- global horizontal irradiance (GHI) --- forecasting --- clearness coefficient --- Markov chains --- weather research and forecasting model --- solar resource --- heat supply of industrial processes --- solar collectors --- economic efficiency --- cross border trading --- Granger causality --- electricity trading --- spot prices --- deformable models --- electric energy demand --- functional statistics --- Kalman filtering --- shape-invariant model --- developing countries --- concentrated solar --- thermochemical --- energy --- renewable energy sources --- climate policy --- forecast --- the European Green Deal --- short-term forecasts --- direct normal irradiance --- concentrating solar power --- system advisor model --- operational strategies --- central solar receiver --- solar irradiance forecasts --- numerical weather prediction model --- different horizontal resolution --- forecast errors --- validation --- ramp rates --- renewable energy forecasting --- solar radiation --- shark algorithm --- particle swarm optimization --- ANFIS --- nowcasting --- Kalman-Bayesian filter --- WRF --- high-resolution --- complex terrain --- wind --- solar irradiation --- photovoltaic solar energy --- deep learning --- prediction --- biofuel --- risk analysis --- sustainable development --- renewable energy --- biomass --- biotechnology --- anthropogenic waste processing --- energy resource assessment --- tidal-stream energy --- thrust force coefficient --- momentum sink --- unbounded flow --- open channel flows --- shock-capturing capability --- global horizontal irradiance (GHI) --- forecasting --- clearness coefficient --- Markov chains --- weather research and forecasting model --- solar resource --- heat supply of industrial processes --- solar collectors --- economic efficiency --- cross border trading --- Granger causality --- electricity trading --- spot prices --- deformable models --- electric energy demand --- functional statistics --- Kalman filtering --- shape-invariant model --- developing countries --- concentrated solar --- thermochemical --- energy --- renewable energy sources --- climate policy --- forecast --- the European Green Deal

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Horticultural crop yield and quality depend on genotype, environmental conditions, and production management. In particular, adverse environmental conditions may greatly affect crop performance, reducing crop yield by 50%–70%. Abiotic stresses such as cold, heat, drought, flooding, salinity, nutrient deficiency, and ultraviolet radiation affect multiple physiological and biochemical mechanisms in plants as they attempt to cope with the stress conditions. However, different crop species can have different sensitivities or tolerances to specific abiotic stresses. Tolerant plants may activate different strategies to adapt to or avoid the negative effect of abiotic stresses. At the physiological level, photosynthetic activity and light-use efficiency of plants may be modulated to enhance tolerance against the stress. At the biochemical level, several antioxidant systems may be activated, and many enzymes may produce stress-related metabolites to help avoid cellular damage, including compounds such as proline, glycine betaine, and amino acids. Within each crop species there is a wide variability of tolerance to abiotic stresses, and some wild relatives may carry useful traits for enhancing the tolerance to abiotic stresses in their progeny through either traditional or biotechnological breeding. The research papers and reviews presented in this book provide an update of the scientific knowledge of crop interactions with abiotic stresses.

heat --- polyphenols --- stomatal conductance --- shelf-life --- transpiration productivity --- transcription --- ornamental plants --- cold --- green areas --- flowering --- agronomic tools --- gas exchange --- ornamental --- prolonged storage --- transpiration --- greenhouse production --- dormancy --- temperature --- irradiance --- chilling requirements --- qPCR --- phenolics --- lodging --- hypoxia --- salinity --- relative humidity --- signal transduction --- chlorophyll fluorescence --- leaf water saturation deficit --- solar radiation --- plant choice --- partial root zone drying --- drought --- electro-conductivity --- growth --- flavonoids --- transpiration efficiency --- cloning --- oxidative stress --- breeding

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Ocean color measured by satellite-mounted optical sensors is an essential climate variable that is routinely used as a central element for assessing the health and productivity of marine ecosystems and the role of oceans in the global carbon cycle. For satellite ocean color to be reliable and used in these and other important environmental applications, the data must be trustworthy and high quality. Pre-flight and on-board calibration of satellite ocean color sensors is conducted; however, once in orbit, the data quality can only be fully assessed via independent calibration and validation activities using surface measurements. These measurements therefore need to be at least as high quality as the satellite data, which necessitates SI traceability and a full uncertainty budget. This is the basis for fiducial reference measurements (FRMs) and the FRM4SOC project, which was an European Space Agency (ESA) initiative to establish and maintain SI-traceable ground-based FRM for satellite ocean color, thus providing a fundamental contribution to the European system for monitoring the Earth (Copernicus). This Special Issue of MDPI Remote Sensing is designed to showcase this essential Earth observation work through the publication of the project’s main achievements and results accompanied by other select relevant articles.

Research & information: general --- VIIRS --- SNPP --- NOAA-20 --- DINEOF --- ocean color data --- data merging --- gap-filling --- ocean color radiometers --- radiometric calibration --- indoor intercomparison measurement --- agreement between sensors --- measurement uncertainty --- field intercomparison measurement --- Hyperspectral reflectance --- validation --- autonomous measurements --- ground-truth data --- system design --- downwelling irradiance --- satellite validation --- Fiducial Reference Measurements --- water reflectance --- satellite --- calibration --- solar diffusor --- SDSM --- desert trend --- lunar calibration --- RVS --- MODIS --- Aqua --- ocean color --- water-leaving radiance --- atmospheric correction --- Sentinel-3 OLCI --- Copernicus --- ocean colour --- system vicarious calibration --- fiducial reference measurement --- Lampedusa --- MOBY --- MarONet --- radiometry --- research infrastructure --- uncertainty budget --- satellite ocean colour --- fiducial reference measurements (FRM) --- calibration and validation --- SI traceability and uncertainty --- European Space Agency (ESA) --- Committee for Earth Observation Satellites (CEOS) --- fiducial reference measurements --- SI-traceability --- Mediterranean Sea --- BOUSSOLE --- MSEA --- hyper-temporal dataset --- optical radiometry --- coastal environment --- observation geometry --- remote sensing reflectance --- ocean colour radiometers --- TriOS RAMSES --- Seabird HyperSAS --- field intercomparison --- AERONET-OC --- Acqua Alta Oceanographic Tower --- remote sensing --- spectral irradiance comparison --- spectral radiance sources comparison --- VIIRS --- SNPP --- NOAA-20 --- DINEOF --- ocean color data --- data merging --- gap-filling --- ocean color radiometers --- radiometric calibration --- indoor intercomparison measurement --- agreement between sensors --- measurement uncertainty --- field intercomparison measurement --- Hyperspectral reflectance --- validation --- autonomous measurements --- ground-truth data --- system design --- downwelling irradiance --- satellite validation --- Fiducial Reference Measurements --- water reflectance --- satellite --- calibration --- solar diffusor --- SDSM --- desert trend --- lunar calibration --- RVS --- MODIS --- Aqua --- ocean color --- water-leaving radiance --- atmospheric correction --- Sentinel-3 OLCI --- Copernicus --- ocean colour --- system vicarious calibration --- fiducial reference measurement --- Lampedusa --- MOBY --- MarONet --- radiometry --- research infrastructure --- uncertainty budget --- satellite ocean colour --- fiducial reference measurements (FRM) --- calibration and validation --- SI traceability and uncertainty --- European Space Agency (ESA) --- Committee for Earth Observation Satellites (CEOS) --- fiducial reference measurements --- SI-traceability --- Mediterranean Sea --- BOUSSOLE --- MSEA --- hyper-temporal dataset --- optical radiometry --- coastal environment --- observation geometry --- remote sensing reflectance --- ocean colour radiometers --- TriOS RAMSES --- Seabird HyperSAS --- field intercomparison --- AERONET-OC --- Acqua Alta Oceanographic Tower --- remote sensing --- spectral irradiance comparison --- spectral radiance sources comparison

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Nowadays, forecast applications are receiving unprecedent attention thanks to their capability to improve the decision-making processes by providing useful indications. A large number of forecast approaches related to different forecast horizons and to the specific problem that have to be predicted have been proposed in recent scientific literature, from physical models to data-driven statistic and machine learning approaches. In this Special Issue, the most recent and high-quality researches about forecast are collected. A total of nine papers have been selected to represent a wide range of applications, from weather and environmental predictions to economic and management forecasts. Finally, some applications related to the forecasting of the different phases of COVID in Spain and the photovoltaic power production have been presented.

Research & information: general --- Direct Normal Irradiance (DNI) --- IFS/ECMWF --- forecast --- evaluation --- DNI attenuation Index (DAI) --- bias correction --- nowcast --- meteorological radar data --- optical flow --- deep learning --- Bates-Granger weights --- uniform weights --- (REG) ARIMA --- ETS --- Hodrick-Prescott trend --- Google Trends indices --- Himalayan region --- streamflow forecast verification --- persistence --- snow-fed rivers --- intermittent rivers --- costumer relation management --- business to business sales prediction --- machine learning --- predictive modeling --- microsoft azure machine-learning service --- travel time forecasting --- time series --- bus service --- transit systems --- sustainable urban mobility plan --- bus travel time --- learning curve --- forecasting --- production cost --- cost estimating --- semi-empirical model --- logistic map --- COVID-19 --- SARS-CoV-2 --- PV output power estimation --- PV-load decoupling --- behind-the-meter PV --- baseline prediction --- Direct Normal Irradiance (DNI) --- IFS/ECMWF --- forecast --- evaluation --- DNI attenuation Index (DAI) --- bias correction --- nowcast --- meteorological radar data --- optical flow --- deep learning --- Bates-Granger weights --- uniform weights --- (REG) ARIMA --- ETS --- Hodrick-Prescott trend --- Google Trends indices --- Himalayan region --- streamflow forecast verification --- persistence --- snow-fed rivers --- intermittent rivers --- costumer relation management --- business to business sales prediction --- machine learning --- predictive modeling --- microsoft azure machine-learning service --- travel time forecasting --- time series --- bus service --- transit systems --- sustainable urban mobility plan --- bus travel time --- learning curve --- forecasting --- production cost --- cost estimating --- semi-empirical model --- logistic map --- COVID-19 --- SARS-CoV-2 --- PV output power estimation --- PV-load decoupling --- behind-the-meter PV --- baseline prediction