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.

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.

Over the years, space-based remote sensing of nighttime light has mostly utilized panchromatic or multispectral sensors. The hyperspectral mission EnMAP, primarily intended for daytime observations, can also produce hyperspectral data of nighttime lighting. EnMAP data from the Las Vegas Strip was analyzed by detecting locations of certain lighting types using matched filtering and detection of sharp emission spikes at known wavelengths. Additionally, images from different nights were compared to determine how changes in observation geometry affect the observed spectra. The results indicate that corrections for geometric effects would be necessary to produce robust time-series data. The EnMAP data were also used to approximate two in-dices related to the efficiency and spectral quality of the light, the luminous efficiency of radiation (LER) and the spectral G index. Future developments will include ana-lyzing scenes from other cities using similar approaches. Program code used in this work is available at https://github.com/silmae/EnMAP_nightlights.

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 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].

This paper describes the contribution of the DLR team ranking 3rdin Track 1 of the 2020 IEEE GRSS Data Fusion Contest, with results ranking 2ndin Track 2 of the same contest being reported in a companion paper. The classifications are based on refinements of low-resolution MODIS labeling using available higher resolution Sentinel-1 and Sentinel-2 data. Results are initialized with a handcrafted decision tree integrating output from a random forest classifier, and subsequently boosted by detectors for specific classes.

This paper describes the contribution of the DLR team ranking 2ndin Track 2 of the 2020 IEEE GRSS Data Fusion Contest. The semantic classification of multimodal earth observation data proposed is based on the refinement of low-resolution MODIS labels, using as auxiliary training data higher resolution labels available for a validation data set. The classification is initialized with a handcrafted decision tree integrating output from a random forest classifier, and subsequently boosted by detectors for specific classes. The results of the team ranking 3rdin Track 1 of the same contest are reported in a companion paper.

This paper describes the winning contribution to the 2019 IEEE GRSS Data Fusion Contest Multi-view Semantic Stereo Challenge. In this challenge, a digital surface model (DSM) and a semantic segmentation should be derived from a large number of multi-spectral WorldView-3 images. Results from 50 stereo pairs matched using Semi-Global Matching (SGM) are fused into a DSM. Semantic segmentation is performed with an ensemble of FCN networks taking as input RGB, multi-spectral and height data. Their results are then merged with pixel-wise detectors for the classes water and high vegetation. Compared to the second and third placed teams (mIOU-3 scores of 0.73 and 0.7295), our contribution reached a significantly higher score of 0.745.

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.