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The frequency and intensity of extreme hot events have increased worldwide, particularly over the past couple of decades. Europe has been affected by unprecedented mega heatwaves, namely the events that struck Western Europe in 2003 and Eastern Europe in 2010. The year 2018 was also reported as an unusually hot year, with record-breaking temperatures in many parts of Europe during spring and summer, associated with severe and unusual wildfires and significant crop losses in central and northern Europe. We show the ability of Land Surface Temperature (LST), retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG) to monitor heat extremes, using the 2018 European event as a showcase. The monitoring approach relies on monthly anomalies performed as departures from the median and the monthly number of hot days (NHD), both computed for satellite LST derived from MSG and MODIS, and for 2 m air temperature (T2m) from ERA5 reanalysis, using as threshold the 90th percentiles. Results show strong monthly LST anomalies during the spring and summer of 2018 extending over central and north Europe. Over a vast region in Central and Northern Europe, LST reached the last 15 years high record. Moreover, those outstanding warm LSTs persisted for more than four months. Results obtained using MODIS LST and ERA5 T2m show similar patterns, which, although slightly less intense, corroborate the exceptionality of the heat extremes observed over central and northern Europe during 2018. The spatial pattern of the number of monthly record high anomalies over the MSG observations period clearly depicts the regions in Northern and Central Europe affected by the complex phenomena that occurred in 2018, which resulted from the combined effect of an extreme heatwave in spring and summer with extensive dry conditions. Therefore, the results highlighted the suitability of MSG LST to evaluate and monitor heat extremes alone or combined with dry and bright conditions and prompts the potential of other climate data records from geostationary satellites to characterize these climate extremes that could become the norm in the near future over central and northern Europe. [ABSTRACT FROM AUTHOR]

The Vegetation Health Index (VHI) has been widely used for monitoring and characterising droughts. This index takes into account ecosystem features in terms of fluctuations between prescribed maxima and minima of NDVI (Vegetation Condition Index, VCI) and of Land Surface Temperature (LST; Thermal Condition Index, TCI), and is estimated as the weighted sum of these two contributions. Since there is no a priori knowledge about vegetation and temperature contributions, VHI is typically taken as the average of both contributions, i.e., a weight of 0.5 is assumed. In this work climatologies of NDVI and LST – spanning the period between 1982 and 2009 – are used to estimate VCI, TCI and VHI on a Mediterranean geographic window, which are then correlated with the multiscalar drought indicator SPEI (Standardized Precipitation-Evapotranspiration Index) with the aim of assessing the effect of drought on each contribution. Results of the correlations between VCI-SPEI and TCI-SPEI show that the relative contributions of VCI and TCI to vegetation health depend on vegetation cover: the effect of drought is more evident in the case of VCI in semiarid climate classes (regions where the limiting factor to vegetation growth is water); while the effect of drought is more obvious in TCI for moistier climate classes (regions where the limiting factor is solar radiation). This leads to the conclusion that by maximising the correlations between VHI and SPEI, over a climatological period, it is possible to evaluate the relative roles of VCI and TCI to VHI for different climate regions. [ABSTRACT FROM AUTHOR]

Dry lands are expected to cover about half of the terrestrial surface in the near future due to climate change. Drought events, which are recurrent over dry lands, are also projected to increase in both frequency and severity. There is a strong need to better monitor droughts over dry regions, and satellite-based indicators such as the Vegetation Health Index (VHI) have been operationally used worldwide in the last two decades. VHI is traditionally defined as the simple average of two components, the Temperature Condition Index (TCI) and the Vegetation Condition Index (VCI) respectively derived from information on the thermal and visible bands. However, the weights of VCI and TCI depend on landcover because of the different contributions of moisture and temperature to the vegetation cycle. By systematically comparing VHI with the Standardised Precipitation-Evapotranspiration Index (SPEI), a multi-scalar drought index, we demonstrate that is possible to disentangle the role played by VCI and TCI on vegetation health. Here we propose a methodology that allows estimating optimal weights for the two components of VHI and we show that VHI is persistently dominated by VCI over dry lands. Results obtained indicate that severe drought episodes over dry lands are better identified when using the proposed methodology. This may be an asset for operational monitoring, paving the way to more efficient social and political responses aiming to mitigate drought impacts. This work is also expected to contribute to the development of optimal sets of VCI and TCI weights that take into consideration expected changes in the land surface based on information from future climate scenarios. • Drought monitoring with VHI is historically estimated as the plain average between the VCI and TCI terms; • It is shown that over global drylands the importance of VCI is larger than TCI; • It is shown that drought events are more likely to be detected when using VHI with larger weights given to VCI. [ABSTRACT FROM AUTHOR]

The vegetative development of grapevines is orchestrated by very specific meteorological conditions. In the wine industry vineyards demand diligent monitoring, since quality and productivity are the backbone of the economic potential. Regional climate indicators and meteorological information are essential to winemakers to assure proper vineyard management. Satellite data are very useful in this process since they imply low costs and are easily accessible. This work proposes a statistical modelling approach based on parameters obtained exclusively from satellite data to simulate annual wine production. The study has been developed for the Douro Demarcated Region (DDR) due to its relevance in the winemaking industry. It is the oldest demarcated and controlled winemaking region of the world and listed as one of UNESCO's World Heritage regions. Monthly variables associated with Land Surface Temperatures (LST) and Fraction of Absorbed Photosynthetic Active Radiation (FAPAR), which is representative of vegetation canopy health, were analysed for a 15-year period (2004 to 2018), to assess their relation to wine production. Results showed that high wine production years are associated with higher than normal FAPAR values during approximately the entire growing season and higher than normal values of surface temperature from April to August. A robust linear model was obtained using the most significant predictors, that includes FAPAR in December and maximum and mean LST values in March and July, respectively. The model explains 90% of the total variance of wine production and presents a correlation coefficient of 0.90 (after cross validation). The retained predictors' anomalies for the investigated vegetative year (October to July) from 2017/2018 satellite data indicate that the ensuing wine production for the DDR is likely to be below normal, i.e., to be lower than what is considered a high-production year. This work highlights that is possible to estimate wine production at regional scale based solely on low-resolution remotely sensed observations that are easily accessible, free and available for numerous grapevines regions worldwide, providing a useful and easy tool to estimate wine production and agricultural monitoring. [ABSTRACT FROM AUTHOR]

Extreme hot events are increasing in frequency and intensity worldwide, whit special emphasis on the high number of events which occurred during the last decade. Europe was particularly affected by unprecedented mega heat waves during the last decades, namely the events that have struck Western Europe in 2003 and Eastern Europe in 2010. Despite their intense impacts, heatwaves are usually of short duration, lasting in the order of a few days to a few weeks. Therefore, monitoring and assessment strategies of extreme hot events is imperative to further investigate their link with atmospheric conditions, especially within the framework of future warming scenarios and risk assessment.The year of 2018 was reported as an unusually hot year, with record breaking temperatures in many parts of Europe during spring and summer, which were associated to severe and unusual wildfires and significant crop losses in central and northern Europe.In this study we analyse the temperature extremes over Europe and their vegetation impacts during 2018 using Land Surface Temperature (LST) and Fraction Vegetation Cover (FVC) retrieved from SEVIRI/Meteosat available through LSA-SAF (http://lsa-saf.eumetsat.int). LST monthly means and anomalies were computed, considering the diurnal cycle of reprocessed Land Surface Temperature (LST), for the period between 2004-2015 with the LST CDR and for 2016 to present with the operational LST dataset. Monthly means and anomalies were also computed using FVC. ECMWF ERA-Interim skin temperature was also used for the period from 1980 to 2018 and monthly means and anomalies were computed for the extended period and for a sub-period simultaneous with LST dataset.The results exhibit strong spatial patterns of monthly LST anomalies during spring and summer of 2018 over central and north Europe. Over a particularly large region, breaking LST records with respect to last 15 years, persist for more than 4 months. ECMWF ERA-Interim skin temperatures corroborate the exceptional heat extremes observed over central and northern Europe during 2018, highlighting the ability and usefulness of the LST-CDR for climate variability and extremes assessment.Acknowledgements: This study was performed within the framework of the LSA-SAF, co-funded by EUMETSAT. This work was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under project IMPECAF (PTDC/CTA-CLI/28902/2017). Ana Russo thanks FCT for grants SFRH/BPD/99757/2014. [ABSTRACT FROM AUTHOR]

The Vegetation Health Index (VHI) is widely used for monitoring drought using satellite data. VHI depends on vegetation state and thermal stress, respectively assessed via (i) the Vegetation Condition Index (VCI) that usually relies on information from the visible and near infra-red parts of the spectrum (in the form of Normalized Difference Vegetation Index, NDVI); and (ii) the Thermal Condition Index (TCI), based on top of atmosphere thermal infrared (TIR) brightness temperature or on TIR-derived Land Surface Temperature (LST). VHI is then estimated as a weighted average of VCI and TCI. However, the optimum weights of the two components are usually not known and VHI is usually estimated attributing a weight of 0.5 to both. Using a previously developed methodology for the Euro-Mediterranean region, we show that the multi-scalar drought index (SPEI) may be used to obtain optimal weights for VCI and TCI over the area covered by Meteosat satellites that includes Africa, Europe, and part of South America. The procedure is applied using clear-sky Meteosat Climate Data Records (CDRs) and all-sky LST derived by combining satellite and reanalysis data. Results obtained present a coherent spatial distribution of VCI and TCI weights when estimated using clear- and all-sky LST. This study paves the way for the development of a future VHI near-real time operational product for drought monitoring based on information from Meteosat satellites. [ABSTRACT FROM AUTHOR]

Land Surface Temperature (LST) is a fundamental state variable for land surface processes, long available from satellite observations in the thermal infrared. Here we make an assessment of LST products derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on-board the geostationary satellite Meteosat Second Generation (MSG), and from the Advanced Very High Resolution Radiometer (AVHRR) on-board Metop polar-orbiting satellites, as provided by the Satellite Application Facility on Land Surface Analysis (LSA-SAF). The validation of LSA-SAF LST products uses ground observations taking into account the representativeness of each station at pixel scale. The in situ - satellite comparisons reveal overall accuracies of 0.13 °C for SEVIRI LST and −0.32 °C for AVHRR-based LST. Better matches are usually found for night-time observations, highlighting the influence of LST spatial and temporal variability as well as of sensor view angle on satellite daytime estimates. The averaged difference between AVHRR and SEVIRI LST products remains mostly within ±1 °C. The impact of different viewing and illumination angles is particularly pronounced for daytime matchups, leading to higher standard deviation of differences than in the case of night-time. Over heterogeneous landscapes, this primarily results from illumination-shadowing effects in daytime observations present in all LST estimates derived from infrared observations. These differences are smaller for night-time observations, or over homogeneous surfaces in general: in these cases, the dependency of LST differences on viewing geometry is smoother and likely explained by the impact of view angle on surface emissivity. Overall the LSA-SAF SEVIRI and AVHRR LST datasets are consistent and meet high accuracy standards. Their production is ensured by the LSA-SAF throughout the life-cycle of the sensors' satellite platforms, making them good candidates for long term applications and studies. [ABSTRACT FROM AUTHOR]

Regional droughts over South America are known to be strongly associated with ENSO. The warm phase of ENSO, El Niño, is related with the drying of the northern part of the continent (including the Amazon forest) and the cold phase, La Niña, with the drying of some regions in the southern part of the continent (south Brazil, Uruguay, eastern Argentina and central Chile). These drought episodes have drastic impacts on society, affecting water resources, crops and energy supplies, among others. With the aim of mitigating drought impacts, a better monitoring and understanding of the phenomenon, coupled with new policies, must be pursued. Satellite-based information is being increasingly used to study and monitor drought events. Indeed, several satellite-derived drought and vegetation indices have been developed in the last decades, the Vegetation Condition Index (VCI) and Thermal Condition Index (TCI) ranking among the most used ones. The former uses the Normalised Difference Vegetation Index (NDVI) derived from the red and near-infrared spectral regions and measures the condition of the vegetation due to the lack or galore of moisture whereas the latter relies on Land Surface Temperature (LST), as obtained from information in thermal infrared window and measures the condition of the soil due to lower or higher than normal temperature. Here, for the period 1982-2009, both indices are first correlated with the Standardised Precipitation-Evapotranspiration Index (SPEI), a multi-scalar drought index. Results show that the importance of VCI and TCI to drought (as measured with SPEI) in the South American region is not the same, with VCI appearing as more important in drier regions and TCI dominating the relation with drought over moister ones (e.g., the Amazon forest). Results further confirm that the use of the Vegetation Health Index (VHI) where VCI and TCI are combined assuming equal weights is not an optimal choice over certain areas. Then, VCI and TCI are correlated with El Niño 3.4 time-series. TCI shows a strong negative correlation with El Niño over the northern part of South America, indicating that the occurrence of such an event degrades vegetation health mainly due to abnormally high temperatures (moisture over the Amazon forest is not a limiting factor), whereas VCI tends to dominate over the southern part of the continent. Results obtained are a first step towards obtaining an optimal combination of VCI and TCI over the different biomes and pave the way to a better understanding of the impact of ENSO on drought events in South America. This work was developed within the framework of the Brazilian Fire-Land-Atmosphere System (BrFLAS) Project financed by the Portuguese and Brazilian science foundations, FCT and FAPESP (project references FAPESP/1389/2014 and 2014/20042-2); and EUMETSAT in the framework of the Satellite Application Facility on Land Surface Analysis (LSA SAF). RL was funded by the Serrapilheira Institute (grant number Serra-1708-15159); and by FCT – project UID/GEO/50019/2019 – Instituto Dom Luiz. [ABSTRACT FROM AUTHOR]