Longterm Dynamics of Sea Surface Temperature (SST) in Europe between 1982 and 2018 - Exploring the new TIMELINE AVHRR SST Product

In the TIMELINE project, consistent SST product were developed from AVHRR brightness temperatures over Europe and North Africa for the period 1982-2018. The daily, 10-daily and monthly Level 3 products contain statistics of SST (minimum, maximum, median, mean) for the respective period. Only high quality SST is used, which is ensured by filtering and quality and uncertainty variables. In this study we present results of the first analysis of long-term dynamics of SST at 1 km resolution over Europe and North Africa based on the Level 3 TIMELINE products

Estimating dry biomass and plant nitrogen concentration in pre-Alpine grasslands with low-cost UAS-borne multispectral data – a comparison of sensors, algorithms, and predictor sets

Grasslands are an important part of pre-Alpine and Alpine landscapes. Despite the economic value and the significant role of grasslands in carbon and nitrogen (N) cycling, spatially explicit information on grassland biomass and quality is rarely available. Remotely sensed data from unmanned aircraft systems (UASs) and satellites might be an option to overcome this gap. Our study aims to investigate the potential of low-cost UAS-based multispectral sensors for estimating above-ground biomass (dry matter, DM) and plant N concentration. In our analysis, we compared two different sensors (Parrot Sequoia, SEQ; MicaSense RedEdge-M, REM), three statistical models (linear model; random forests, RFs; gradient-boosting machines, GBMs), and six predictor sets (i.e. different combinations of raw reflectance, vegetation indices, and canopy height). Canopy height information can be derived from UAS sensors but was not available in our study. Therefore, we tested the added value of this structural information with in situ measured bulk canopy height data. A combined field sampling and flight campaign was conducted in April 2018 at different grassland sites in southern Germany to obtain in situ and the corresponding spectral data. The hyper-parameters of the two machine learning (ML) approaches (RF, GBM) were optimized, and all model setups were run with a 6-fold cross-validation. Linear models were characterized by very low statistical performance measures, thus were not suitable to estimate DM and plant N concentration using UAS data. The non-linear ML algorithms showed an acceptable regression performance for all sensor–predictor set combinations with average (avg; cross-validated, cv) R2cv of 0.48, RMSEcv,avg of 53.0 g m2, and rRMSEcv,avg (relative) of 15.9 % for DM and with R2cv, avg of 0.40, RMSEcv,avg of 0.48 wt %, and rRMSEcv, avg of 15.2 % for plant N concentration estimation. The optimal combination of sensors, ML algorithms, and predictor sets notably improved the model performance. The best model performance for the estimation of DM (R2cv=0.67, RMSEcv=41.9 g m2, rRMSEcv=12.6 %) was achieved with an RF model that utilizes all possible predictors and REM sensor data. The best model for plant N concentration was a combination of an RF model with all predictors and SEQ sensor data (R2cv=0.47, RMSEcv=0.45 wt %, rRMSEcv=14.2 %). DM models with the spectral input of REM performed significantly better than those with SEQ data, while for N concentration models, it was the other way round. The choice of predictors was most influential on model performance, while the effect of the chosen ML algorithm was generally lower. The addition of canopy height to the spectral data in the predictor set significantly improved the DM models. In our study, calibrating the ML algorithm improved the model performance substantially, which shows the importance of this step

Long-term dynamics of Land Surface Temperature (LST) over Europe

LST is recognized as one of the Essential Climate Variables by the World Meteorological Organization. It is a key parameter for climate models and a direct indicator of global warming. The Advanced Very High-Resolution Radiometer (AVHRR) is the only sensor that has been providing spatially and temporally continuous daily measurements for 40 years. In the TIMELINE project, consistent LST products were developed from AVHRR over Europe [1,2]. However, the different overpass times and the orbital drift effect hide actual trends and anomalies in LST. In this study TIMELINE LST from NOAA 9, 11, 14, 16, 17, 18 and 19 for the years 1984-2017 over four regions in Western and Central Europe was analyzed. A physical daily temperature cycle (DTC) model was applied to normalize the LST to a consistent observation time and then monthly anomalies and a linear trend was calculated

Detection of Grassland Degradation In Azerbaijan By Combining Multi-Decadal NDVI Time Series And Fractional Cover Estimates Based On DESIS Data

Degradation is one of the most pressing global environmental problems and is projected to worsen due to climate change and land use pressure. Grassland ecosystems used for pasturing are especially prone to degradation. In the Caucasus region, livestock farming is an important part of the agricultural sector and subsistence farming is commonplace, hence threats to pastures can significantly impact livelihoods. The grassland areas of Azerbaijan are under heavy anthropogenic pressure, leading to vegetation cover loss and erosion, especially on community pasture land. Degradation is generally assessed in remote sensing by quantifying changes in vegetation indices (VIs). This is a challenging task, as information from long time series is needed to detect trends, and frequent observations are needed to distinguish degradation from phenological variability. For such time series, only multispectral data is available. However, especially in regions where vegetation is sparse, information on the fractions of ground cover such as photoactive vegetation (PV), non-photoactive vegetation (NPV) or soil is important, as soil reflectance affects VIs. Hyperspectral data are particularly valuable in this regard as they have the spectral resolution required to distinguish soil, vital and degraded vegetation. In this study we therefore investigate the potential of combining multispectral time series with hyperspectral data. In a first step, a nationwide land cover map is generated. During two field campaigns in August and October 2018, 296 plots in grassland, cropland and shrubland were visited, for which land cover, coverage and erosion intensity were recorded. In addition, samples of urban areas, soil, water and forests were collected from Google Earth Engine (GEE) imagery. 70 spectral-temporal metrics of the Sentinel-2 imagery of 2018 were used as input features together with the field data in a random forest classifier. Land cover is modeled with an overall accuracy of 83 % (Asam et al. 2019, ESA LPS). A Normalized Difference Vegetation Index (NDVI) time series is used to identify grassland degradation on a national scale. Acquisitions from the Landsat Missions (TM, ETM+, OLI; 1984-2020) are harmonized and each image is masked using fmask on the GEE platform. For each year, median NDVI of the grassland areas are generated and trends are calculated using the Sen's slope and the Mann-Kendall test. For 2019 – 2021, 9 DESIS acquisitions are available with cloud coverage < 25% and recorded with sun angle < 40°, covering parts of the western lowlands and Lesser Caucasus of Azerbaijan. For each scene, fractional cover was calculated using the “fCover” processor. It derives pure material signatures using the Spatial-Spectral Endmember Extraction (Rogge et al. 2012, JSTARS, 5(1)), which are then classified into the classes PV, NPV and soil, using a pre-trained random forest. fCovers are then calculated using a Multiple Endmember Spectral Mixture Analysis (Bachmann et al. 2009, 6th EARSeL-SIG-IS) with each pixel treated as a linear combination of each spectral class. Originally developed for hyperspectral sensors covering the full VNIR-SWIR range, this method was successfully applied also to VNIR-only DESIS data (Marshall et al., 2021, 1st DESIS User Workshop). First results indicate that 5.4 % of Azerbaijan’s grasslands show a significant (p < 0.05) negative NDVI trend, pointing to potential degradation hotspots. PV could be derived from DESIS with a mean absolute error of 8,94 %. Next, areas showing a degradation trend are intersected with PV and NPV fractions and analyzed regarding their statistical relationship. First results show that pixels with a high PV coverage are less degraded. In addition, effects of topography and degradation time scales will be analyzed. Using this approach, a country-wide multi-decadal assessment of vegetation changes can be enhanced by adding canopy structure information from hyperspectral DESIS data

Validated UPLC-MS/MS methods to quantitate free and conjugated Alternaria toxins in commercially available tomato products and fruit and vegetable juices in Belgium

Ultraperformance liquid chromatography tandem mass spectrometry and Quick, Easy, Cheap, Effective, Rugged, and Safe based analytical methodologies to quantitate both free (alternariol (1), alternariol monomethyl ether (2), tenuazonic acid (3), tentoxin (4), altenuene (5), altertoxin-I (6)) and conjugated (sulfates and glucosides of 1 and 2) Alternaria toxins in fruit and vegetable juices and tomato products were developed and validated. Acceptable limits of quantitation (0.7-5.7 mu g/kg), repeatability (RSDr < 15.7%), reproducibility (RSDR < 17.9%), and apparent recovery (87.0-110.6%) were obtained for all analytes in all matrices investigated. 129 commercial foodstuffs were analyzed, and 3 was detected in 100% of tomato product samples (<LOQ to 333 mu g/kg), while 1, 2, 4, and 5 were also frequently detected (21-86%, <LOQ to 62 mu g/kg). Moreover, low levels (<LOQ to 9.9 mu g/kg) of modified Alternaria toxins (sulfates of 1 and 2) were repeatedly detected. A deterministic dietary exposure assessment revealed the possible risk for human health related to the presence of 1 and 2 in tomato based foodstuffs, whereas 3 is unlikely to be of human health concern

Estimating grassland biomass and livestock carrying capacity using Sentinel data to strengthen grazing management on local to national scales in Armenia

Livestock farming is an important part of the Armenian agricultural development strategy. The agricultural sector employs more than one third of Armenia’s labor force and accounts for 13% of GDP, hence threats to livestock and pastures can significantly impact livelihoods. For sustaining and developing that sector, fodder provision from grasslands is a key factor. Grasslands constitute 39% of the total territory of Armenia and 57% of the agricultural lands. Apart from resources for livestock, they provide important areas for biodiversity and ecosystem services. The condition of natural pastures and grasslands, however, is being deteriorated due to anthropogenic pressure and unsustainable management practices, leading to overgrazing and erosion. These risks are potentially further aggravated through climatic changes such as more frequent droughts, heat waves, and lack of snow cover. Hence, the setup of an integrated management approach for local decision-making becomes important, emphasizing the need of robust and up-to-date spatial data. In the context of the “GrassAM” project conducted by DLR and GIZ, we aimed at mapping grassland extent, grasslands types, grassland above ground biomass (AGB) and livestock carrying capacities at 10 m spatial resolution in the entire country of Armenia in the year 2020. In order to create a grassland mask for Armenia, a land use and land cover (LULC) classification was carried out using in situ data together with Sentinel-1, Sentinel-2, and digital elevation (DEM) data in a random forest classification approach implemented on Google Earth Engine. 400 sample points of 7 classes (“pasture”, “meadow”, “other grasslands”, “annual arable land”, “perennial arable land”, “bushland”, “bare soil”) were collected by the partner organization ICARE during summer 2020, distributed over all districts and ecological zones. To complement the classification, additional points were sampled on screen for the class “water”. Urban and forest areas were masked using DLR’s World Settlement Footprint 2015 [1] at 10 m resolution as well as the Hansen Global Forest Change maps [2] at 30 m resolution, respectively. The resulting classification achieved an overall accuracy of 80%, while the grassland area was slightly overestimated with 79% user’s accuracy and 92% producer’s accuracy. Of the 400 in situ sites, 147 pasture and meadow points also included wet and dry AGB samples. These measurements have been collected in one 30 x 30 cm plot per field (mowing at 2 cm height), which was assumed to be representative for the surrounding 30 x 30 m. The fresh plant mass was placed in a paper container, labeled and weighed with precision of 0.1 grams. Samples were then dried at room temperature for 48 - 72 hours and weighed again. The measured green AGB ranges from 1.733 – 27.800 kg/ha, with a mean of 12.367 kg/ha, and dry AGB ranges from 1.011 – 14.300 kg/ha, with a mean of 5.416 kg/ha. The AGB measurements were split 60/40 in training and validation data. To create a spatially balanced training data, the selection of training samples was based on spatial allocation of points in hexagon tessellation (1 point per grid cell; 2 points if there are more than four samples are available per cell). Biomass was modeled in a next step using a random forest regression model. The training samples have been used to test a set of 730 different geospatial features (monthly statistics and bi-weekly interpolated features of B2 - B12 Sentinel-2 bands and of eight vegetation indices, elevation, slope, monthly mean temperature at 2 m, monthly precipitation sums) as predictors using a Sequential Forward Feature Selection. Six features (Sentienl-2 mid-June and mid-July NDVI, Band 12 median, May precipitation, June temperature, elevation) were selected and achieved a R-square of 0.66 with an RMSE of 4.013 kg/ha for green AGB. The country-wide biomass maps are the basis to model grassland carrying capacity, i.e. the maximum number of cattle equivalent animals that can be sustained in a given grassland area in a season. AGB was multiplied with a proper use factor of 0.65 as it was suggested by [3] to estimate the available fodder. This amount is divided by the daily requirement of fodder per animal unit (equivalent of 400 kg live weight of cows) multiplied by pasture season length. For both quantities, landscape-zone specific assumptions have been made, resulting in an optimal stocking density of 1- 3 animals per hectare. Test for improving biomass and carrying capacity models as well as the input data sets are still ongoing. The resulting maps, that characterize the allowable grazing pressure on a country-wide scale, could be used to improve grassland management and to increase the resilience of grassland ecosystems to future climate conditions. [1] Marconcini, M., Metz-Marconcini, A., Üreyen, S., Palacios-Lopez, D., Hanke, W., Bachofer, F., Zeidler, J., Esch, T., Gorelick, N., Kakarla, A., & Strano, E. (2020). Outlining Where Humans Live –The World Settlements Footprint 2015. Scientific Data7(242). doi.org/10.1038/s41597-020-00580-5. [2] Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. [3] de Leeuw, J. Rizayeva, A., Namazov, E., Bayramov, E., Marshall, M. T., Etzold, J., Neudert,R. (2019): Application of the MODIS MOD 17 Net Primary Production product in grassland carrying capacity assessment, International Journal of Applied Earth Observation and Geoinformation 78, 66-76, https://doi.org/10.1016/j.jag.2018.09.014

Are European Cities Getting Warmer? Investigating the Urban Heat Island Phenomenon in Europe from 1981-2018 through the Use of NOAA-AVHRR Data

The Urban Heat Island (UHI) effect is one of the most prominent signs of human impact on the Earth system. This phenomenon has significantly altered the energy exchange between land surfaces and the atmosphere which has led to several negative impacts on the quality of city life in terms of air and water quality, energy consumption, vegetation growth and human health. European cities are particularly vulnerable to UHI because of their high degree of urbanization, as illustrated by the 2003 heatwave which claimed thousands of lives. However, the existing body of research mostly concentrates on local-scale and/or short-term analyses, which leaves long-term effects at continental scale poorly understood. Within the TIMELINE project of the Earth Observation Center (EOC) of the German Aerospace Center (DLR), a consistent AVHRR Land Surface Temperature (LST) product has been generated, which is employed in this study to determine the intensity of the surface UHI (SUHI) over Europe for the period 1981−2018. Specifically, the objective is to analyze the long-term SUHI trends and corresponding changes over European cities, as well as to gain insights on their relationships with different variables, like climate, land use and land cover (change), vegetation indices and day-night temperature differences. First results point towards a remarkable increase in both SUHI spatial extent and intensity across the entire continent

Long-term Dynamics of Land Surface Temperature over Europe: Towards a Daytime normalized AVHRR Land Surface Temperature Product

In this study a statistical orbit drift correction method was applied to TIMELINE AVHRR LST from NOAA 7, 9, 11, 14, 16, 18 and 19 afternoon overpasses at 12 sites across Europe. The corrected LST anomalies were validated against Ta anomalies from nearby meteorological stations. The results showed an improvement of the correlation (R) between the LST and Ta anomalies at most sites. A few sites showed a decrease of R, which can be explained by complex land cover (urban) or missing daytime effects of LST (at forest sites). After the orbit drift correction, the long-term trends of the LST anomalies were much closer to the Ta trends. Furthermore, climatological features visible in the Ta time series (like e.g. a warm period in the late 1980s) are more distinct in the LST time series after the correction. However, similar studies reached higher correlations between LST and Ta anomalies. This can be explained by a more uniform generation of the LST and Ta anomalies. Further improvements and validation are necessary to obtain a reliable and continent-wide orbit drift correction for AVHRR. It is also planned to extend the analysis to further orbit drift correction methods and also to other validation data, e.g. Landsat LST