Grasslands cover one third of the earth’s terrestrial surface and are mainly used for livestock production. The usage type, use intensity and condition of grasslands are often unclear. Remote sensing enables the analysis of grassland production and management on large spatial scales and with high temporal resolution. Despite growing numbers of studies in the field, remote sensing applications in grassland biomes are underrepresented in literature and less streamlined compared to other vegetation types. By reviewing articles within research on satellite-based remote sensing of grassland production traits and management, we describe and evaluate methods and results and reveal spatial and temporal patterns of existing work. In addition, we highlight research gaps and suggest research opportunities. The focus is on managed grasslands and pastures and special emphasize is given to the assessment of studies on grazing intensity and mowing detection based on earth observation data. Grazing and mowing highly influence the production and ecology of grassland and are major grassland management types. In total, 253 research articles were reviewed. The majority of these studies focused on grassland production traits and only 80 articles were about grassland management and use intensity. While the remote sensing-based analysis of grassland production heavily relied on empirical relationships between ground-truth and satellite data or radiation transfer models, the used methods to detect and investigate grassland management differed. In addition, this review identified that studies on grassland production traits with satellite data often lacked including spatial management information into the analyses. Studies focusing on grassland management and use intensity mostly investigated rather small study areas with homogeneous intensity levels among the grassland parcels. Combining grassland production estimations with management information, while accounting for the variability among grasslands, is recommended to facilitate the development of large-scale continuous monitoring and remote sensing grassland products, which have been rare thus far.

Central Europe experienced several droughts in the recent past, such as in the year 2018, which was characterized by extremely low rainfall rates and high temperatures, resulting in substantial agricultural yield losses. Time series of satellite earth observation data enable the characterization of past drought events over large temporal and spatial scales. Within this study, Moderate Resolution Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) 250 m time series were investigated for the vegetation periods of 2000 to 2018. The spatial and temporal development of vegetation in 2018 was compared to other dry and hot years in Europe, like the drought year 2003. Temporal and spatial inter-and intra-annual patterns of EVI anomalies were analyzed for all of Germany and for its cropland, forest, and grassland areas individually. While vegetation development in spring 2018 was above average, the summer months of 2018 showed negative anomalies in a similar magnitude as in 2003, which was particularly apparent within grassland and cropland areas in Germany. In contrast, the year 2003 showed negative anomalies during the entire growing season. The spatial pattern of vegetation status in 2018 showed high regional variation, with north-eastern Germany mainly affected in June, north-western parts in July, and western Germany in August. The temporal pattern of satellite-derived EVI deviances within the study period 2000-2018 were in good agreement with crop yield statistics for Germany. The study shows that the EVI deviation of the summer months of 2018 were among the most extreme in the study period compared to other years. The spatial pattern and temporal development of vegetation condition between the drought years differ.will increase [2,3]. With 2 • C of global warming, one out of every two summer months in Europe could show higher temperatures than ever observed under current climate conditions [4]. Several recent studies show that, by the end of this century, drought events will become more frequent all over Europe [5][6][7].The projected increase of climate extremes is likely to have severe consequences on environmental, economic, and infrastructure systems as well as on human health in Europe. In the aftermath of the summer of 2003 heat wave, the deaths of thousands of vulnerable elderly people in Europe were reported [8]. Different ecological systems show varying degrees of resilience to an increase in droughts. Long-term consequences are possible shifts in species composition, ecosystem degradation, and loss of ecosystem services [9]. For example, ecosystems such as forests and bogs will be affected by an increased risk of wildfires. Furthermore, the carbon sequestration potential is strongly reduced under drought conditions, with a projected overall decrease in vegetation carbon uptake over Europe [10], further reinforcing the drivers of climate change. The agricultural sector is harmed as crop yields [11] and above-ground biomass of grasslands [12] will be reduced. An increased information need on...

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.

Grasslands cover one-third of the agricultural area in Germany and play an important economic role by providing fodder for livestock. In addition, they fulfill important ecosystem services, such as carbon storage, water purification, and the provision of habitats. These ecosystem services usually depend on the grassland management. In central Europe, grasslands are grazed and/or mown, whereby the management type and intensity vary in space and time. Spatial information on the mowing timing and frequency on larger scales are usually not available but would be required in order to assess the ecosystem services, species composition, and grassland yields. Time series of high-resolution satellite remote sensing data can be used to analyze the temporal and spatial dynamics of grasslands. Within this study, we aim to overcome the drawbacks identified by previous studies, such as optical data availability and the lack of comprehensive reference data, by testing the time series of various Sentinel-2 (S2) and Sentinal-1 (S1) parameters and combinations of them in order to detect mowing events in Germany in 2019. We developed a threshold-based algorithm by using information from a comprehensive reference dataset of heterogeneously managed grassland parcels in Germany, obtained by RGB cameras. The developed approach using the enhanced vegetation index (EVI) derived from S2 led to a successful mowing event detection in Germany (60.3% of mowing events detected, F1-Score = 0.64). However, events shortly before, during, or shortly after cloud gaps were missed and in regions with lower S2 orbit coverage fewer mowing events were detected. Therefore, S1-based backscatter, InSAR, and PolSAR features were investigated during S2 data gaps. From these, the PolSAR entropy detected mowing events most reliably. For a focus region, we tested an integrated approach by combining S2 and S1 parameters. This approach detected additional mowing events, but also led to many false positive events, resulting in a reduction in the F1-Score (from 0.65 of S2 to 0.61 of S2 + S1 for the focus region). According to our analysis, a majority of grasslands in Germany are only mown zero to two times (around 84%) and are probably additionally used for grazing. A small proportion is mown more often than four times (3%). Regions with a generally higher grassland mowing frequency are located in southern, south-eastern, and northern Germany.

<p>With amplified climate warming, climate extremes over Europe become more frequent. Since the 2000’s, many years have been characterized by extreme events such as droughts and heat waves. For example, in Central Europe, extreme droughts and heat waves took place in the years 2003 and 2018. In comparison, Cyprus experienced strong droughts during 2003 and 2016-2018. Such extreme climate events can have severe impacts on agricultural yields, the productivity of natural vegetation, and on water resources. In this regard, long-term Earth observation (EO) time series are essential to quantitatively assess and analyse changes on the land surface, including vegetation condition. In this study, a joint analysis of geoscientific time series over the last two decades, including EO-based MODIS vegetation indices and meteorological variables is performed to assess drought events and analyse trends as well as potential drivers of vegetation dynamics in Cyprus. The analysis of drought events and vegetation trends is based on the full archive of MODIS imagery at 250 m spatial resolution covering the period 2000-2022. In detail, climate-related effects on vegetation were analysed by means of the deviations of MODIS 16-day vegetation index composites from their long-term mean. Next, trends of the MODIS vegetation index were calculated to evaluate spatial patterns of vegetation change over the investigated period. These analyses were additionally performed for geographically stratified regions, including diverse vegetation classes such as cropland and grassland. Furthermore, the application of a causal discovery algorithm reveals linkages within a multivariate feature space, in particular between vegetation greenness and climatic drivers. Preliminary analyses showed that drought patterns differ with respect to seasons and the investigated vegetation class. For example, the strong drought year 2008 is clearly reflected in the results, whereas forest areas appear to be least affected by the drought during the spring months. Moreover, considering the significant trends over the last two decades, an increase in vegetation greenness could be observed.</p>