Description: The EnPT Python package is an automated pre-processing pipeline for the new EnMAP hyperspectral satellite data. It provides free and open-source features to transform EnMAP Level-1B data to Level-2A. The package has been developed at the German Research Centre for Geosciences Potsdam (GFZ) as an alternative to the processing chain of the EnMAP Ground Segment.

Description: The Environmental Mapping and Analysis Program (EnMAP) is a spaceborne German hyperspectral satellite mission that aims at monitoring and characterizing the Earth’s environment on a global scale. After several years delay due to a major design issue to meet the mission requirements, the mission is now back on track and planned for launch in 2021. This paper presents an update of the mission status with recent activities and developments from the space and the ground segment. Furthermore, a draft plan for the independent validation of EnMAP radiance and reflectance products was developed and will be introduced, along with highlights of the science preparatory activities in 2019 including airborne campaigns, algorithm consolidations, and HYPERedu education initiative.

Description: SICOR is a sensor independent atmospheric correction of optical Earth Observation (EO) data from both multispectral and hyperspectral instruments. Currently, SICOR can be applied to Sentinel-2 and EnMAP data but the implementation of additional space- and airborne sensors is under development.

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: Industrial emissions play a major role in the global methane budget. The Permian basin is thought to be responsible for almost half of the methane emissions from all U.S. oil- and gas-producing regions, but little is known about individual contributors, a prerequisite for mitigation. We use a new class of satellite measurements acquired during several days in 2019 and 2020 to perform the first regional-scale and high-resolution survey of methane sources in the Permian. We find an unexpectedly large number of extreme point sources (37 plumes with emission rates 〉500 kg hour−1), which account for a range between 31 and 53% of the estimated emissions in the sampled area. Our analysis reveals that new facilities are major emitters in the area, often due to inefficient flaring operations (20% of detections). These results put current practices into question and are relevant to guide emission reduction efforts.

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: The dataset is composed of Hyspex (VNIR/SWIR) hyperspectral imagery acquired during airplane overflights on October 1, 2015 within the Demmin Research Area. The acquisition conditions were cloud free. The dataset includes two mosaics generated based on 9 HySpex flight lines. The dataset also includes Level 2A EnMAP-like imagery simulated using the end-to-end Simulation tool (EeteS). Additionally, a soil database focusing on the soil organic carbon content (SOC) with geographic coordinates, SOC content, texture and spectral information is included.

Description: The dataset is composed of Hyspex (VNIR/SWIR) hyperspectral imagery acquired during airplane overflights on 01. Oktober, 2015 within the Demmin Research Area. The acquisition conditions were cloud free. The dataset includes two mosaics generated based on 9 HySpex flight lines. The dataset also includes Level 2A EnMAP-like imagery simulated using the end-to-end Simulation tool (EeteS). Additionally a soil database focussed on the soil organic carbon content (SOC) with geographic coordinates, texture and spectral information is included.

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).