Description: Berlin-Urban-Gradient is a ready-to-use imaging spectrometry dataset for multi-scale unmixing and hard classification analyses in urban environments. The dataset comprises two airborne HyMap scenes at 3.6 and 9 m resolution, a simulated spaceborne EnMAP scene at 30 m resolution, an im-age endmember spectral library and detailed land cover reference information. All images are pro-vided as geocoded reflectance products and cover the same subset along Berlin’s urban-rural gra-dient. The variety of land cover and land use patterns captured make the dataset an ideal play-ground for testing the transfer of methods and research approaches at multiple spatial scales.

Description: Berlin-Urban-Gradient is a ready-to-use imaging spectrometry dataset for multi-scale unmixing and hard classification analyses in urban environments. The dataset comprises two airborne HyMap scenes at 3.6 and 9 m resolution, a simulated spaceborne EnMAP scene at 30 m resolution, an image endmember spectral library and detailed land cover reference information. All images are provided as geocoded reflectance products and cover the same subset along Berlin’s urban-rural gradient. The variety of land cover and land use patterns captured make the dataset an ideal playground for testing the transfer of methods and research approaches at multiple spatial scales.

Description: Spaceborne imaging spectrometers are expected to facilitate regional-scale vegetation analyses with multi-season hyperspectral imagery. However, we still lack a better understanding on both whether multi-season hyperspectral approaches are favorable over single-season approaches, as well as on the benefits of hyperspectral compared to multispectral data. Our study investigates the potential of multi-season unmixing of simulated Environmental Mapping and Analysis Program (EnMAP) data for vegetation class fraction mapping across diverse natural and semi-natural ecoregions in California, USA. We utilized spring, summer and fall 2013 simulated EnMAP imagery derived from Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) data covering study sites in the San Francisco Bay Area, Lake Tahoe and Santa Barbara. Regression-based unmixing with synthetic training datasets from spectral libraries was implemented for mapping needleleaf tree, broadleaf tree, shrub, herbaceous and non-vegetation fractions, and independent reference data was used for validation. Multi-season unmixing of simulated EnMAP had average Mean Absolute Errors (MAE) over all classes of 8.7% for the Bay Area, 8.5% for Lake Tahoe and 9.6% for Santa Barbara. However, larger errors in the low and high end of the fraction range remained, particularly in open-canopy woodlands and xeric shrub-dominated regions. Single-season unmixing of simulated EnMAP revealed large seasonal and regional variations within individual vegetation classes. In most cases, the best performing single-season unmixing had similar errors as the multi-season unmixing, i.e., ∆MAEs within ±1.0%. This points to the advantage of the multi-season integration strategy for more robust and generalized mapping independent from season and study site. Relative to EnMAP analyses, multi-season unmixing of multispectral Landsat composites for the same seasons yielded increases in average MAEs of +1.7%, +2.3% and +1.4% for the three study sites. This indicates that the higher spectral resolution of simulated EnMAP provides more relevant discriminative information when comparing contemporary image pairs. Unmixing of seasonal spectral-temporal metrics (STMs) from all available Landsat images for an entire year took advantage of the full temporal detail provided by these ongoing missions. We found Landsat STMs to effectively map vegetation class fractions, with average MAEs of 9.9%, 10.0% and 9.7% for the three study sites. Still, improvements particularly for mapping fractions of the woody vegetation classes through multi-season unmixing of simulated EnMAP point to the benefit of high spectral resolution data, and we assume that a comparable higher temporal resolution of hyperspectral satellites will further positively influence results. Overall, we conclude that multi-season unmixing of spaceborne imaging spectroscopy data holds great potential for advancing vegetation class fraction mapping across natural and semi-natural ecosystems.

Description: Severe droughts caused unprecedented impacts on grasslands in Central Europe in 2018 and 2019. Yet, spatially varying drought impacts on grasslands remain poorly understood as they are driven by complex interactions of environmental conditions and land management. Sentinel-2 time series offer untapped potential for improving grassland monitoring during droughts with the required spatial and temporal detail. In this study, we quantified drought effects in a major Central European grassland region from 2017 to 2020 using a regression-based unmixing framework. The Sentinel-2-based intra-annual time series of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil fractional cover provide easily interpretable quantities relevant for understanding drought effects on grasslands. Fractional cover estimates from Sentinel-2 matched in-situ conditions observed during field visits. The comparison to a multitemporal reference dataset showed the best agreement for PV cover (MAE = 7.2%). Agreement was lower for soil and NPV, but we observed positive relationships between fractional cover from Sentinel-2 and the reference data with MAE = 10.1% and MAE = 15.4% for soil and NPV, respectively. Based on the fractional cover estimates, we derived a Normalized Difference Fraction Index (NDFI) time series contrasting NPV and soil cover relative to PV. In line with meteorological and soil moisture drought indices, and with the Normalized Difference Vegetation Index (NDVI), NDFI time series showed the most severe drought impacts in 2018, followed by less severe, but persisting effects in 2019. Drought-specific metrics from NDFI time series revealed a high spatial variability of onset, duration, impact, and end of drought effects on grasslands. Evaluating drought metrics on different soil types, we found that grasslands on less productive, sandy Cambisols were strongly affected by the drought in 2018 and 2019. In comparison, grasslands on Gleysols and Histosols were less severely impacted suggesting a higher drought resistance of these grasslands. Our study emphasizes that the high temporal and spatial detail of Sentinel-2 time series is mandatory for capturing relevant vegetation dynamics in Central European lowland grasslands under drought.

Description: The scope of the Science Plan is to describe the scientific background, applications, and activities of the Environmental Mapping and Analysis Program (EnMAP) imaging spectroscopy mission. Primarily, this document addresses scientists and funding institutions, but it may also be of interest to environmental stakeholders and governmental agencies. It is designed to be a living document that will be updated throughout the entire mission lifetime. Chapter 1 provides a brief overview of the principles and current state of imaging spectroscopy. This is followed by an introduction to the EnMAP mission, including its objectives and impact on international programs as well as major environmental and societal challenges. Chapter 2 describes the EnMAP system together with data products and access, calibration/validation, and synergies with other missions. Chapter 3 gives an overview of the major fields of application such as vegetation and forests, geology and soils, coastal and inland waters, cryosphere, urban areas, atmosphere and hazards. Finally, Chapter 4 outlines the scientific exploitation strategy, which includes the strategy for community building and training, preparatory flight campaigns and software developments. A list of abbreviations is provided in the annex to this document and an extended glossary of terms and abbreviations is available on the EnMAP website.

Description: Berlin-Urban-Gradient is a ready-to-use imaging spectrometry dataset for multi-scale unmixing and hard classification analyses in urban environments. The dataset comprises two airborne HyMap scenes at 3.6 and 9 m resolution, a simulated spaceborne EnMAP scene at 30 m resolution, an im-age endmember spectral library and detailed land cover reference information. All images are pro-vided as geocoded reflectance products and cover the same subset along Berlin’s urban-rural gra-dient. The variety of land cover and land use patterns captured make the dataset an ideal play-ground for testing the transfer of methods and research approaches at multiple spatial scales.

Description: This dataset is composed of simulated EnMAP mosaics for the San Francisco Bay Area, USA. Hyperspectral imagery used for the EnMAP simulation was collected across three time periods (Spring, Summer, and Fall) in 2013 with the AVIRIS-Classic sensor flown as part of the HyspIRI Preparatory Campaign. Flight lines were simulated to EnMAP-like data using the EnMAP end-to end Simulation tool to produce 30 x 30 m imagery with 195 bands (after band removal) ranging from 423 to 2439 nm. Secondary geometric correction was applied using automatically generated tie points, and a class-wise empirical across track brightness correction was implemented to mitigate brightness gradients.

Description: This dataset is composed of three-season simulated EnMAP mosaics for the Lake Tahoe region, USA. HyspIRI Airborne Campaign AVIRIS imagery from spring, summer and fall formed the basis for simulating EnMAP data with 30 m spatial resolution and 195 spectral bands ranging from 420 to 2450 nm. The mosaics are provided as Analysis-Ready-Datasets (tiled surface reflectance products) to be used for regional-scale and multi-season hyperspectral image analysis of California’s diverse ecoregions. The dataset primarily intends to support the development of processing algorithms and to demonstrate spaceborne hyperspectral data capabilities during the pre-launch activities of the forthcoming EnMAP mission. This dataset was processed in line with companion simulated EnMAP mosaics for the San Francisco Bay Area and for the Santa Barbara region.

Description: This dataset is composed of three-season simulated EnMAP mosaics for the Lake Tahoe region, USA. HyspIRI Airborne Campaign AVIRIS imagery from spring, summer and fall formed the basis for simulating EnMAP data with 30 m spatial resolution and 195 spectral bands ranging from 420 to 2450 nm. The mosaics are provided as Analysis-Ready-Datasets (tiled surface reflectance products) to be used for regional-scale and multi-season hyperspectral image analysis of California’s diverse ecoregions. The dataset primarily intends to support the development of processing algorithms and to demonstrate spaceborne hyperspectral data capabilities during the pre-launch activities of the forthcoming EnMAP mission. This dataset was processed in line with companion simulated EnMAP mosaics for the San Francisco Bay Area (Cooper et al. 2020a) and for the Santa Barbara region (Okujeni et al. 2021a).