The operation of a remote sensing spaceborne mission like EnMAP requires operating a high-precision instrument in Space, the commanding of the instrument according to user-provided parameters, the handling of a large data volume and processing these data on demand according to different processing options. All these aspects pose many challenges from the operational point of view. The EnMAP Ground Segment (GS) provides the infrastructure responsible for the operation of the mission after launch and it acts as the interface between the users and the satellite, overcoming the challenges just mentioned. In this contribution we present the up-to-date status of the EnMAP mission operations when the system has been in operation for two years, the latest updates that have been introduced in the tasking of the instrument and the plans for the future.

The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission designed to characterise and monitor the Earth’s environment and its changes. The EnMAP satellite was launched on April 1st, 2022 and has been in the operational phase since November of that year. This presentation provides an update on the mission status two years after launch, and on the activities of the science segment, including data quality and mission performance, new developments in open source algorithms and educational tools (EnMAP-Box, HYPERedu), current status of EnMAP's background and foreground missions, as well as selected demonstration examples of geo- and bio-EnMAP products. This reflects the scientific achievements for various cutting-edge and environmental applications that are of benefit to society today.

On August 2024, the DLR Earth Sensing Imaging Spectrometer (DESIS) completed six years of operations onboard the International Space Station (ISS). In that time, DESIS has acquired data worldwide for both scientific and commercial users. The continuously growing data archive supports methodical and application developments for the monitoring of the Earth’s surface. We present a short update of the mission status and then provide a deeper view into the DESIS data archive. DESIS is currently operating in nominal conditions, further expanding its multitemporal data archive, which holds great value for a wide range of applications and serves as a database for recent and upcoming hyperspectral Earth-observing missions. It enables long-term analysis of physical phenomena and land use changes by providing high-resolution data spanning an extended temporal range for the monitoring of a site of interest.

The retrieval of sun-induced fluorescence (SIF) from hyper-spectral imagery is an ill-posed problem that has been tackled in different ways. We present a novel retrieval method combining semi-supervised deep learning with an existing spectral fitting method. A validation study with in-situ SIF measurements shows high sensitivity of the deep learning method to SIF changes even though systematic shifts deteriorate its absolute prediction accuracy. A detailed analysis of diurnal SIF dynamics and SIF prediction in topographically variable terrain highlights the benefits of this deep learning approach.

Spaceborne imaging spectroscopy is undergoing a rapid expansion with a new generation of missions in recent years. Following the Hyperion (2000) and HICO (2009) missions, new spaceborne imaging spectroscopy missions have recently started operating: DESIS (2018), PRISMA (2019), HISUI (2019) and more recently EnMAP (2022) and EMIT (2022). These missions face the common challenge of providing accurate spectral and radiometric results over a wide spectral range. This requires accurate instrument calibration and the validation of the results obtained. In this contribution, we provide an overview of the calibration and validation (CalVal) activities in the EnMAP mission, and we present the CalVal results that were obtained as part of the commissioning phase (April - October 2022).

The DLR HyperSpectral Unmixing (DLR HySU) open benchmark dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes on a homogeneous background, complemented by simultaneous ground-based reflectance measurements. The dataset allows assessing dimensionality estimation, endmember extraction with and without pure pixel assumption, and abundance estimation in the frame of spectral unmixing applications, enabling estimations at sub-pixel level. This paper presents the first works in the literature using the dataset, which demonstrate that DLR HySU is filling a gap regarding validation using real imaging spectrometer data with accurately measured targets.

In many remote sensing applications the measured radiance needs to be corrected for atmospheric effects to study surface properties such as reflectance, temperature or emission features. The correction often applies radiative transfer to simulate atmospheric propagation, a time-consuming step usually done offline. In principle, an efficient machine learning (ML) model can accelerate the simulation step. This is the goal pursued here in the context of solar-induced fluorescence (SIF) emitted by vegetation around the O2-A band using the spaceborne DESIS and airborne HyPlant spectrometers. We present an ML simulator of at-sensor radiances trained on synthetic spectra and describe its performance in detail. The simulator is fast and accurate, constituting a promising alternative to a full-fledged, lengthy radiative transfer code for SIF retrieval in the O2-A band with DESIS and HyPlant.

Since November 2022, the PACO [1] atmospheric correction program has operated routinely as the L2A processor in the ground segment for the hyperspectral missions DESIS [2] and EnMAP [3]. Both missions monitor the Earth’s environment similar to other operational hyperspectral missions like PRISMA [4] and EMIT [5]. The Ground Segment L2A processor for the DESIS and EnMAP missions corrects the at-sensor received terrestrial reflection of the incident solar radiation for the effects of atmospheric constituents generating Bottom-Of-Atmosphere (BOA) ground reflectance spectral image cube, along with pixel-classification masks, Aerosol Optical Thickness (AOT) at 550 nm and Water Vapor (WV) maps. In this contribution we summarize the lessons learned on the validation of the atmospheric correction and the related uncertainty of the hyperspectral L2A products, applying the same validation criteria, for both DESIS and EnMAP.

The German Hyperspectral Satellite Mission EnMAP (Environmental Mapping and Analysis Program; www.EnMAP.org) is an imaging spectroscopy remote sensing mission with the primary objective to measure, derive and analyze quantitative diagnostic parameters describing key processes on the Earth’s surface (see, e.g., [1]). In this article, an overview of the EnMAP processing chain is given with a focus on the geometric processing of EnMAP data. Furthermore, the geometric performance after one year of acquisitions is assessed and presented.

The DLR Earth Sensing Spectrometer (DESIS) on board the International Space Station (ISS) has been providing high quality hyperspectral data to the scientific community and commercial users since the start of operations in September 2018. After almost 4 years in orbit, the DESIS instrument continues to operate correctly and to deliver hyperspectral data products for a wide variety of applications. In order to support this successful activity, the calibration team regularly analyzes the instrument data and provides updates using vicarious calibration. We present here the latest results from the DES IS vicarious calibration and our plans for future improvements.

The DLR Earth Sensing Imaging Spectrometer (DESIS) [1] is a spaceborne instrument installed and operated on the International Space Station (ISS). The German Aerospace Center (DLR) has developed the instrument and the software for data processing [2], while the US company Teledyne Brown Engineering (TBE) provided the Multi-User System for Earth Sensing (MUSES) platform, where DESIS is installed, and the infrastructure for operations and data tasking [3]. The main parameters of the DESIS instrument are summarized in Table 1. DESIS is equipped with an on-board calibration unit and a rotating pointing mirror (POI). The POI can change the line of sight ±15° in the forward/backward direction (independently of the MUSES orientation), allowing BRDF measurements of the same area on ground within an overflight.

The DLR Earth Sensing Spectrometer (DESIS) on board the International Space Station (ISS) is an imaging spectrometer for remote sensing developed by the German Aerospace Center (DLR) and operated by Teledyne Brown Engineering (TBE). In order to maintain the quality of the data during the operational phase, the calibration team monitors the calibration parameters and updates them when a significant deviation is found. The update of calibration parameters is based on vicarious calibration using Earth scenes over uniform areas and RadCalNet calibration sites. We present here a description of the calibration techniques used for the DESIS instrument with special emphasis on the vicarious calibration.

The DLR Earth Sensing Imaging Spectrometer (DESIS) is a space-based instrument installed and operated on the International Space Station (ISS) [1]. This space mission is the achievement of the collaboration between the German Aerospace Center (DLR) and the US company Teledyne Brown Engineering (TBE). DLR has developed the instrument and the software for data processing [2], while TBE provides the Multi-User System for Earth Sensing (MUSES) platform, where DESIS is installed, and the infrastructure for operation and data tasking [3].

Imaging spectrometry provides densely sampled and finely structured spectral information for each image pixel over large areas, enabling the characterization of materials on the Earth's surface by measuring and analyzing quantitative parameters allowing the user to identify and characterize Earth surface materials such as minerals in rocks and soils, vegetation types and stress indicators, and water constituents. The recently launched DLR Earth Sensing Imaging Spectrometer (DESIS) installed on the International Space Station (ISS) closes the long-term gap of sparsely available spaceborne imaging spectrometry data and will be part of the upcoming fleet of such new instruments in orbit. DESIS measures in the spectral range from 400 and 1000 nm with a spectral sampling distance of 2.55 nm and a Full Width Half Maximum (FWHM) of about 3.5 nm. The various DESIS data products available for users are described with the focus on specific processing steps. A summary of the data quality results are given. The product validation studies show that top-of-atmosphere radiance, geometrically corrected, and bottom-of-atmosphere reflectance products meet the mission requirements.

DESIS (DLR Earth Sensing Imaging Spectrometer) is a space-based hyperspectral sensor currently installed and operated in the International Space Station (ISS). The instrument is the result of the collaboration between the German Aerospace Center (DLR) and Teledyne Brown Engineering (TBE). DLR has developed the instrument and the software for data processing, while TBE provides the Multi-User System for Earth Sensing (MUSES), where DESIS is installed, and the infrastructure for operation.

When working with hyperspectral data, it is very important, that the data is properly pre-processed in terms of systematic radiometric and spectral correction, geometric correction as well as atmospheric correction. Airborne and spaceborne sensors show some similarities regarding the processing, but also some differences. In this paper, these similarities and differences are discussed on the example of the HySpex processing chain in the generic processing environment Catena and the EnMAP processor that is currently developed at DLR. The paper presents the different sensors and their properties and gives an overview of the different workflows and the used algorithms.

This article describes the workflow of the classification algorithm which ranked at 2ndplace in the 2018 GRSS Data Fusion Contest. The objective of the contest was to provide a classification map with 20 classes on a complex urban scenario. The available multi-modal data were acquired from hyperspectral, LiDAR and very high-resolution RGB sensors flown on the same platform over the city of Houston, TX, USA. The classification was obtained by merging deep convolutional and shallow fully-connected neural networks on a simplified set of classes, complemented by a series of specific detectors and ad hoc classifiers.

EnMAP (Environmental Mapping and Analysis Program, www.enmap.org) is a German, Earth observing, imaging spectroscopy, spaceborne mission planned for launch in 2020. This work reflects the status of the EnMAP Ground Segment, currently procuring its facilities and elements for later testing and integrating them. The Ground Segment's Design Model is discussed as well as its constituents are introduced. It further discusses the recent changes to be respected by the design covering the topics, how low quality data is handled within the Ground Segment, how the files aboard the satellite are deleted to ensure a maximum data security, and how the moon could serve as further calibration source during the EnMAP mission.

The German Aerospace Center (DLR) and Teledyne Brown Engineering (TBE), located in Huntsville, Alabama, USA, cooperate to develop and operate the new space-based hyperspectral sensor DLR Earth Sensing Imaging Spectrometer (DESIS). While TBE provides the Multi-User platform MUSES and infrastructure for operation of the DESIS instrument on the ISS, DLR is responsible for providing the instrument and the processing software as well as instrument in-flight calibration and product quality operations. MUSES has been already launched and installed on the International Space Station ISS in early 2017 and DESIS will follow mid of 2018. We present here an overview of the DESIS instrument, the on-ground data processing, the in-flight calibration and product quality investigations.

The hyperspectral instrument DLR Earth Sensing Imaging Spectrometer (DESIS) will be developed and integrated in the Multi-User-System for Earth Sensing (MUSES) platform installed on the International Space Station (ISS). The DESIS instrument will be launched to the ISS mid of 2017 and installed in one of the four slots of the MUSES platform. The MUSES / DESIS system will be commanded and operated by the publically traded company Teledyne Brown Engineering (TBE), which initiated the program. TBE provides the MUSES platform and the German Aerospace Center (DLR) develops DESIS and establishes a Ground Segment for processing, archiving, delivering and calibrating the data used for scientific and humanitarian applications. Harmonized products will be generated by the Ground Segment established at Teledyne. This article describes the processing ground segment and the foreseen data validation activities. Finally comments regarding the data policy and foreseen scientific uses are given.