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.

Currently, some of the implemented atmospheric correction processors for remote sensing spectral sensors, use masking algorithms based on thresholding of spectral indices with sensor Top-Of-Atmosphere (TOA) reflectance. This concept allows the use of a limited amount of spectral bands, which is optimal for multi-spectral sensors (~10-20 bands), but for the case of the high spectral dimensionality of hyper-spectral sensors, spectral thresholding underutilizes the number of available bands. Given this limitation, we propose a masking algorithm which performs spatial and spectral feature extraction based on a 2D convolutional neural network, fitting the model with the available classification maps from the Python-based Atmospheric COrrection (PACO) processor. The training samples are selected based on their uncertainty to belong to a given class, The validation is performed using two independent human expert labelled datasets. The resulting classification maps show an improvement from the original ones of PACO.

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.

The Copernicus Sentinel-2 satellite mission, with its Sentinel-2A and Sentinel-2B units, offers since several years now a massive quantitative and qualitative resource for the Earth Observation community. Since the launch of Sentinel-2A in 2015, and Sentinel-2B in 2017, many lessons have been learnt leading to continuous improvements of the radiometric and the geometric performances. However, the current archive is composed of heterogenous processing baselines with inconsistent product formats and uneven data quality, which limits its use for multi-temporal monitoring applications.To overcome this limitation, the Copernicus program has undertaken a complete reprocessing with the latest processing baseline (05.00). It concerns the L1C (Top-Of-Atmosphere reflectance) and L2A (Surface Reflectance) products. This paper recalls the features of Collection-1 products and gives an overview of the first validation results.

The EnMAP mission, launched in April 2022, is a remote sensing in the optical domain (VNIR / SWIR) with a high spatial (30 m GSD) and spectral (FWHM ~ 6-12 nm) resolution. In general, remote sensing data can suffer from pixel defects caused by various factors like aging, component degradation, vibrations, and transmission failures. These defects result in missing or low-quality data, non-uniformity effects, and out-of-range radiance values. To address this, interpolation techniques are applied during data processing, both at the TOA radiances and intermediate BOA reflectance spectra levels. The interpolation implemented in EnMAP aims to improve the accuracy of derived spectral products by not only correcting defective pixels, but additionally, reconstructing datasets with missing bands, taking advantage of the high spectral/spatial resolution of this data. The algorithm's performance is evaluated by comparing the results with reference values and with more traditional interpolation methods, like for example the one used by the DESIS sensor. The study also explores the algorithm's capabilities in scenarios involving partial loss of radiance data.

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 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.

Sen2Cor is the atmospheric correction processor selected by ESA for operational, systematic processing of Copernicus Sentinel-2 mission data. It is used for generating the Level-2A products distributed to users by the Copernicus SciHub. Accurate atmospheric correction of Sentinel-2 data and knowledge of its uncertainties are preconditions for high quality downstream applications. In this work we present the comparison of Sentinel-2 Bottom-of-Atmosphere products with measurements of surface reflectance on ground. Source of reference measurements are both surface reflectance data from RadCalNet and from dedicated field campaigns. The analysis shows, that the uncertainty of SR-retrieval with Sen2Cor is better than about 7% for bright surfaces and about 17% for darker. In addition to this performance evaluation, the data are also applied to compare the use of reference data coming from permanent operating bright RadCalNet sites and from ad-hoc field campaigns at darker sites.

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].

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.

Most remote sensing applications require atmospheric correction of satellite images and an increasing part exploits multi-temporal data. Sentinel-2 satellites and Landsat-8 provide almost equivalent satellite images and a joint use of both data sources gives the advantage of a denser time series if the quality of atmospheric correction is consistent. The present study investigates the performance of atmospheric correction processor ATCOR and shows, that it gives consistent results for Sentinel-2 and Landsat-8 data enabling a combined use of both satellites. Both satellite sensors provide the same correct shape of surface reflection spectra.

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.

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 high-resolution imaging spectroscopy remote sensing mission EnMAP (Environmental Mapping and Analysis Program, enmap.org) will cover the spectral range from 420 nm to 2450 nm with a spectral sampling distance varying between 5 nm and 12 nm. A smile effect, which is a spectral shift across the swath, of at most 0.2 pixels is expected. The OHB System AG realizes the satellite with the hyperspectral push-broom imager and the on-board characterization equipment including a doped sphere for spectral calibration. The Earth Observation Center (EOC) of the German Aerospace Center (DLR) is responsible for the operational in-flight calibration and realizes the fully-automatic on-ground processors including methods for smile correction. A smile correction taking the spectral calibration into account as well as a simplified atmospheric compensation will be analyzed.