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.

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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.

Many studies analyzing spaceborne hyperspectral images (HSIs) have so far struggled to deal with a lack of pure pixels due to complex mixtures of urban surface materials. Recently, an alternative concept of gradients in urban surface material composition has been proposed and successfully applied to map cities with spaceborne HSIs without the requirement for a previous determination of pure pixels. The gradient concept treats all pixels as mixed and aims to describe and quantify gradual transitions in the cover fractions of surface materials. This concept presents a promising approach to tackle urban mapping using spaceborne HSIs. However, since gradients are determined in a data-driven way, their transferability within urban areas needs to be investigated. For this purpose, we analyze the robustness of urban surface material gradients and their dependence across six systematic and three simple random sampling schemes. The results show high similarity between nine sampling schemes in the primary gradient feature space (Pspace) and individual gradient feature spaces (Ispaces). Comparing the Pspace with the Ispaces, the Mantel statistics show the resemblance of samples' distribution in the Pspace, and each Ispace is rather strong with high credibility, as the significance level is P < 0.01. Therefore, it can be concluded that the material gradients defined in the test area are independent of the specific sampling scheme. This study paves the way for subsequent analysis of the stability of urban surface material gradients and the interpretation of material gradients in other urban environments.

Over the past decades, enormous efforts have been made to improve the performance of linear or nonlinear mixing models for hyperspectral unmixing (HU), yet their ability to simultaneously generalize various spectral variabilities (SVs) and extract physically meaningful endmembers still remains limited due to the poor ability in data fitting and reconstruction and the sensitivity to various SVs. Inspired by the powerful learning ability of deep learning (DL), we attempt to develop a general DL approach for HU, by fully considering the properties of endmembers extracted from the hyperspectral imagery, called endmember-guided unmixing network (EGU-Net). Beyond the alone autoencoder-like architecture, EGU-Net is a two-stream Siamese deep network, which learns an additional network from the pure or nearly pure endmembers to correct the weights of another unmixing network by sharing network parameters and adding spectrally meaningful constraints (e.g., nonnegativity and sum-to-one) toward a more accurate and interpretable unmixing solution. Furthermore, the resulting general framework is not only limited to pixelwise spectral unmixing but also applicable to spatial information modeling with convolutional operators for spatial-spectral unmixing. Experimental results conducted on three different datasets with the ground truth of abundance maps corresponding to each material demonstrate the effectiveness and superiority of the EGU-Net over state-of-the-art unmixing algorithms. The codes will be available from the website: https://github.com/danfenghong/IEEE_TNNLS_EGU-Net.

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.

Soils contain the largest global carbon pool and thus, play an important role in atmospheric CO2 sequestration through increase in soil organic carbon (SOC) stock (Minasny et al., 2017). Therefore, large scale mapping and reporting of soil status, quality and health is starting to get a requirement amongst policy-makers and implemented in target setting of the Sustainable Development Goals and relevant EU policies. Soil quality and health monitoring is commonly described as a sum of physical, chemical and biological properties of soils. Since many years, multispectral and hyperspectral Earth Observation (EO) have been valuable data sources for analyzing the chemical and physical constitution of top soils (Chabrillat et al., 2019). Mainly two approaches are used, the Digital Soil Modelling (DSM) approach as well as the Spectral Soil Modelling (SSM) approach. Both approaches assimilate EO data products such as information regarding the vegetation dynamics and exposed soil reflectance data.

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].

Soil organic matter is essential for preserving and maintaining a range of soil and ecosystem functions as well as supply and store carbon for climate change mitigation. Digital Soil Mapping techniques will be used to obtain a spatially continuous product, especially over permanently vegetated areas. Recently available satellite remote sensing data, with among other systems the Copernicus Sentinel, will be used as input for environmental covariates. Digital Soil Mapping, coupled together with Remote Sensing products, is a powerful tool to produce soil properties maps and monitoring the changes in soil conditions over time.

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.

Recently, enormous efforts have been made to improve the performance of the linear or nonlinear mixing model for hyperspectral unmixing, yet their ability to handle spectral variability and extract physically meaningful endmembers remains limited. Based on the powerful learning ability of deep learning, we propose a weakly-supervised unmixing network, called WU-Net, to break the bottleneck. Beyond the autoencoder-like architecture, WU-Net learns an additional network from the pure or nearly-pure endmembers to correct the weights of another unmixing network towards a more accurate and interpretable unmixing solution, thus yielding a two-stream deep network. Experimental results conducted on two different datasets, one fully artificial simulation dataset and one simulated EnMap dataset generated from a real HyMap dataset, demonstrate the effectiveness and superiority of WU-Net over several state-of-the-art algorithms.

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.

Ground reflectance was acquired at the Railroad Valley Playa calibration site in Nevada USA using different methods of collection. The data was collected near the time and date of Landsat 8 OLI and Sentinel-2 satellite overpasses so an inter-comparison could be made with the reflectance products to determine which method was more suitable for vicarious calibration. The field spectrometers and reference panels were characterized before the field campaign. A continuous acquisition method was compared to stop and measure collections. Both acquisition methods were collected along an 80 m east-west transect as well as for a series of north-south transects over an 80 × 320 m area, with the stop and measure method being performed at random sampling locations. The measurements were performed using two field spectrometers by three teams of two people to compare the repeatability. The aim of the field campaign was to determine the variability due to the operator and the method of collection.

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.

EnMAP (Environmental Mapping and Analysis Program) is a German spaceborne imaging spectrometer Earth observing mission planned for launch in 2019. This paper reflects the status of the mission with an focus to changes of the Ground Segment based on a major review conducted in 2016 and the EnMAP Data Exploitation and Application Development Program and recent activities.

The primary objective of the SCMaP is to supply value added information about soils at three levels: 1) the spatial distribution of exposed soils; 2) temporal statistics of those soils; and, 3) a reflectance soil composite map. The SCMaP is designed for temperate climatic regions that comprise areas of extensive crop based agriculture where soils are commonly covered by vegetation. For the SCMaP satellite based multi-temporal optical imagery is used to generate per-pixel composite images that reflect maximum and minimum photosynthetically active vegetativion. Applying pre-determined thresholds to the maximum and minimum composites an exposed soil mask generated that can be used to build a reflectance soil composite image. The SCMaP has been designed towards free and open access high spatial muti-spectral Landsat and Sentinel 2 data, with results shown here for archived Landsat (4,5,7) imagery from 2010-2014 for all of Germany.

The Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill the current gap in space-based imaging spectroscopy data. An overview of the main characteristics and current status of the mission will be provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to 4 days at Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide.

Urban microclimate models can provide knowledge on the climate, however, most microclimate studies use assumptions and generalizations to define the model area. Remote sensing based data products provide an alternative allowing both the detailed spatial and thematic scale required by urban climate models. This study shows how microclimate simulations for a series of real world urban areas can be supported by using remote sensing data. In an automated process, urban surface information has been derived using airborne hyperspectral data and height information. Results have been integrated into the urban microclimate model ENVI-met and multiple microclimate simulations have been carried out. The impact of the RS-based surface information and the suitability of the applied data and techniques are tested and evaluated. The simulation results show consistent patterns for air temperature, surface temperature and humidity, indicating the plausibility of the approach. Further, the analysis shows the importance of high quality height data, detailed surface material information and albedo.

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.

This paper provides a brief overview of current civilian imaging spectroscopy (hyperspectral) missions currently operating in space or ready for launch for imaging the Earth. This overview is followed by a list of missions currently under development, and the paper concludes with a survey of missions in a planning stage. The latter is probably not a complete list of missions, but provides a good cross-section of sensors, which might be in space around the 2020 time frame.

Urban climate modeling is a means to increase the understanding of the urban climate. In order to model real world environments, area-wide spatial information is required. This paper demonstrates how hyperspectral remote sensing data and additional height data can provide a large part of this information efficiently, reducing the need for extensive field surveys. From the hyperspectral data the surface materials, the LAI and the surface albedo are estimated. The height data supplies the heights of buildings and trees. Using these maps as input for the urban micro climate model ENVI-met, simulations of temperature, wind and humidity among others can be carried out.

The Ground Segment for the future hyperspectral satellite mission EnMAP (Environmental Mapping and Analysis Program) will be designed, implemented and operated by the German Aerospace Center (DLR). The Applied Remote Sensing Cluster (DFD) at DLR is responsible for the establishment of a user interface. This paper provides first issues on design and functionality of the user interface. The user interface consists of two online portals. The EnMAP portal is the central entry point for all users interested to learn about the EnMAP mission, its objectives, status, and results. The EnMAP Data Access Portal (EDAP) provides a set of functions for registered users that will support the international EnMAP user community. The operational services offered through the EnMAP portal will be complemented by a service team, EnMAP Application Support, offering expert advice on the exploitation of EnMAP data.

The urban environment is characterized by an intense use of the available space, where the preservation of open green spaces is of special ecological importance. Because of dynamic urban development and high mapping costs, municipal authorities are interested in effective methods for mapping urban surface cover types that can be used for evaluating ecological conditions in urban structures and supporting updates of biotope mapping. Against this background, airborne hyperspectral remote sensing data of the DAIS 7915 instrument have been analyzed for their potential in automated area-wide differentiation of ecologically meaningful urban surface cover types for a study area in the city of Dresden, Germany. The small urban structures and the high spectral information content of the hyperspectral image data require the development of special methods capable of dealing with the resulting large number of mixed pixels. In this paper, a new approach is presented that combines advantages of classification with linear spectral unmixing. Since standard unmixing techniques are not suitable for an area-wide analysis of urban surfaces representing a large number of spectrally similar endmembers (EMs), the mathematical model, were extended and a new method for pixel-oriented EM selection was developed. This method reduces the number of possible EM combination for each pixel by introducing spectrally pure seedlings and a list of possible EM combinations into a neighborhood-oriented iterative unmixing procedure. The results and their comparison with standard spectral classification methods show that the new pixel- and contest-based approach enables reasonable material-oriented differentiation of urban surfaces.