Philipp Reiners

Monitoring and analyzing vegetation trends and their climatic drivers are useful to understand the possible impacts of climate change on vegetation. In this study, we analyzed normalized difference vegetation index (NDVI) trends at 1 km² resolution for spring (March–May), summer (June–August), and autumn (September–November) for individual biogeographical regions and land cover types in Europe for the 30-year period 1989–2018. In addition, we determined the influence of precipitation, temperature, solar radiation, and vapor pressure deficit (VPD) on the NDVI and identified their influence in areas showing significant (p0.005 NDVI year⁻¹), strongest NDVI decrease (<−0.002 NDVI year⁻¹) for grassland (summer), and rain-fed cropland (autumn) in the Steppic region. In spring, significant positive causal links between the NDVI and temperature are dominant in large areas of Europe while VPD influences the NDVI in north-eastern (positive link) and southern Europe (negative link). In summer, precipitation shows positive causal links to NDVI in parts of the Anatolian and Mediterranean regions, while VPD influences NDVI regionally (negative link). In autumn, precipitation (positive link) and VPD (negative link) are the dominant controlling factors on the NDVI for central-eastern Europe and parts of the Iberian Peninsula. Across Europe, significant positive NDVI trends in spring can be mainly explained by increasing temperatures. Negative spring NDVI trends can be linked to decreasing VPD in some regions. In the Mediterranean, Steppic, and Anatolian regions, a significant NDVI decrease in summer and autumn is mainly linked to decreasing precipitation. The results of this study assist in understanding and quantifying ongoing vegetation change in Europe and reveal the influence climate variables have on the vegetation activity for individual land cover types within different regions.

Long time series of satellite-derived land surface temperature (LST) data are an indispensable resource to study climate and global change. The Advanced Very High-Resolution Radiometer (AVHRR) provides thermal infrared measurements, which can be translated into LST, for 40 years with high temporal resolution. However, the time series suffers from discontinuities caused by the orbit drift of the carrying National Oceanic and Atmospheric Administration (NOAA) satellites. In this study a new orbit drift correction (ODC) method is proposed, which uses geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI) LST to create an AVHRR daily maximum LST Time Series over Europe. The orbit drift corrected AVHRR LST is validated in three ways: First observations from the same day but different NOAA platforms are intercompared, second the orbit drift corrected time series is compared to the Climate Change Initiative (CCI) LST product by the European Space Agency (ESA) and third the orbit drift corrected time series is compared to air temperature (T2m) measurements. The cross-platform comparison and the comparison to CCI LST shows that the ODC method successfully minimized the discontinuities between the NOAA platforms and also spurious trends within the individual platforms. The mean absolute difference (MAD) for the cross-platform comparison is improved from 4.13 K to 1.86 K, while the MAD for the comparison with CCI LST is improved from 2.39 K to 2.04 K. The comparison with T2m shows both a high correlation of the anomalies and a high accordance of the trends and climatological patterns. Furthermore, it is shown, that the ODC works consistently for all land cover types, however results are better in Central than in Southern Europe. This is explained by the higher daily LST amplitude and the consequently higher impacts of the orbit drift on the LST in lower latitudes. The resulting long-term LST dataset with its unique temporal coverage and resolution enables numerous studies regarding climate and global change including land atmosphere interactions and phenomena like heat waves and droughts.

Coastal areas are among the most productive areas in the world, ecologically as well as economically. Sea Surface Temperature (SST) has evolved as the major essential climate variable (ECV) and ocean variable (EOV) to monitor land–ocean interactions and oceanic warming trends. SST monitoring can be achieved by means of remote sensing. The current relatively coarse spatial resolution of established SST products limits their potential in small-scale, coastal zones. This study presents the first analysis of the TIMELINE 1 km SST product from AVHRR in four key European regions: The Northern and Baltic Sea, the Adriatic Sea, the Aegean Sea, and the Balearic Sea. The analysis of monthly anomaly trends showed high positive SST trends in all study areas, exceeding the global average SST warming. Seasonal variations reveal peak warming during the spring, early summer, and early autumn, suggesting a potential seasonal shift. The spatial analysis of the monthly anomaly trends revealed significantly higher trends at near-coast areas, which were especially distinct in the Mediterranean study areas. The clearest pattern was visible in the Adriatic Sea in March and May, where the SST trends at the coast were twice as high as that observed at a 40 km distance to the coast. To validate our findings, we compared the TIMELINE monthly anomaly time series with monthly anomalies derived from the Level 4 CCI SST anomaly product. The comparison showed an overall good accordance with correlation coefficients of R > 0.82 for the Mediterranean study areas and R = 0.77 for the North and Baltic Seas. This study highlights the potential of AVHRR Local Area Coverage (LAC) data with 1 km spatial resolution for mapping long-term SST trends in areas with high spatial SST variability, such as coastal regions.

Remote sensing multi-decadal time-series provide important information for analysing long-term environmental change. The Advanced Very High Resolution Radiometer (AVHRR) has been providing data since the early 1980s. Normalised Difference Vegetation Index (NDVI) time-series derived thereof can be used for monitoring vegetation conditions. This study presents the novel TIMELINE NDVI product, which provides a consistent set of daily, 10-day, and monthly NDVI composites at a 1 km spatial resolution based on AVHRR data for Europe and North Africa, currently spanning the period from 1981 to 2018. After investigating temporal and spatial data availability within the TIMELINE monthly NDVI composite product, seasonal NDVI trends have been derived thereof for the period 1989–2018 to assess long-term vegetation change in Europe and northern Africa. The trend analysis reveals distinct patterns with varying NDVI trends for spring, summer and autumn for different regions in Europe. Integrating the entire growing season, the result shows positive NDVI trends for large areas within Europe that confirm and reinforce previous research. The analyses show that the TIMELINE NDVI product allows long-term vegetation dynamics to be monitored at 1 km resolution on a pan-European scale and the detection of specific regional and seasonal patterns.

Satellite-derived Land Surface Temperature (LST) dynamics have been increasingly used to study various geophysical processes. This review provides an extensive overview of the applications of LST in the context of global change. By filtering a selection of relevant keywords, a total of 164 articles from 14 international journals published during the last two decades were analyzed based on study location, research topic, applied sensor, spatio-temporal resolution and scale and employed analysis methods. It was revealed that China and the USA were the most studied countries and those that had the most first author affiliations. The most prominent research topic was the Surface Urban Heat Island (SUHI), while the research topics related to climate change were underrepresented. MODIS was by far the most used sensor system, followed by Landsat. A relatively small number of studies analyzed LST dynamics on a global or continental scale. The extensive use of MODIS highly determined the study periods: A majority of the studies started around the year 2000 and thus had a study period shorter than 25 years. The following suggestions were made to increase the utilization of LST time series in climate research: The prolongation of the time series by, e.g., using AVHRR LST, the better representation of LST under clouds, the comparison of LST to traditional climate change measures, such as air temperature and reanalysis variables, and the extension of the validation to heterogenous sites.

Remote sensing image composites are crucial for a wide range of remote sensing applications, such as multi-decadal time series analysis. The Advanced Very High Resolution Radiometer (AVHRR) instrument has provided daily data since the early 1980s at a spatial resolution of 1 km, allowing analyses of climate change-related environmental processes. For monitoring vegetation conditions, the Normalized Difference Vegetation Index (NDVI) is the most widely used metric. However, to actually enable such analyses, a consistent NDVI time series over the AVHRR time-span needs to be created. In this context, the aim of this study is to thoroughly assess the effect of different compositing procedures on AVHRR NDVI composites, as no standard procedure has been established. Thirteen different compositing methods have been implemented; daily, decadal, and monthly composites over Europe and Northern Africa have been calculated for the year 2007, and the resulting data sets have been thoroughly evaluated according to six criteria. The median approach was selected as the best-performing compositing algorithm considering all the investigated aspects. However, the combination of the NDVI value and viewing and illumination angles as the criteria for the best-pixel selection proved to be a promising approach, too. The generated NDVI time series, currently ranging from 1981–2018, shows a consistent behavior and close agreement to the standard MODIS NDVI product. The conducted analyses demonstrate the strong influence of compositing procedures on the resulting AVHRR NDVI composites.

Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.

Land Surface Temperature (LST) is an important parameter for tracing the impact of changing climatic conditions on our environment. Describing the interface between long- and shortwave radiation fluxes, as well as between turbulent heat fluxes and the ground heat flux, LST plays a crucial role in the global heat balance. Satellite-derived LST is an indispensable tool for monitoring these changes consistently over large areas and for long time periods. Data from the AVHRR (Advanced Very High-Resolution Radiometer) sensors have been available since the early 1980s. In the TIMELINE project, LST is derived for the entire operating period of AVHRR sensors over Europe at a 1 km spatial resolution. In this study, we present the validation results for the TIMELINE AVHRR daytime LST. The validation approach consists of an assessment of the temporal consistency of the AVHRR LST time series, an inter-comparison between AVHRR LST and in situ LST, and a comparison of the AVHRR LST product with concurrent MODIS (Moderate Resolution Imaging Spectroradiometer) LST. The results indicate the successful derivation of stable LST time series from multi-decadal AVHRR data. The validation results were investigated regarding different LST, TCWV and VA, as well as land cover classes. The comparisons between the TIMELINE LST product and the reference datasets show seasonal and land cover-related patterns. The LST level was found to be the most determinative factor of the error. On average, an absolute deviation of the AVHRR LST by 1.83 K from in situ LST, as well as a difference of 2.34 K from the MODIS product, was observed.