Explore the vast range of titles available.

Imaging spectrometry from aerial or spaceborne platforms, also known as hyperspectral remote sensing, provides dense sampled and fine structured spectral information for each image pixel, 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 ground sample distance is 30 m with 1024 pixels across track. In this article, a detailed review is given on the applicability of DESIS data based on the specifics of the instrument, the characteristics of the ISS orbit, and the methods applied to generate products. The various DESIS data products available for users are described with the focus on specific processing steps. The results of the data quality and product validation studies show that top-of-atmosphere radiance, geometrically corrected, and bottom-of-atmosphere reflectance products meet the mission requirements. The limitations of the DESIS data products are also subject to a critical examination.

Whether for identification and characterization of materials or for monitoring of the environment, space-based hyperspectral instruments are very useful. Hyperspectral instruments measure several dozens up to hundreds of spectral bands. These data help to reconstruct the spectral properties like reflectance or emission of Earth surface or the absorption of the atmosphere, and to identify constituents on land, water, and in the atmosphere. There are a lot of possible applications, from vegetation and water quality up to greenhouse gas monitoring. But the actual number of hyperspectral space-based missions or hyperspectral space-based data is limited. This will be changed in the next years by different missions. The German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) is one of the new currently existing space-based hyperspectral instruments, launched in 2018 and ready to reduce the gap of space-born hyperspectral data. The instrument is operating onboard the International Space Station, using the Multi-User System for Earth Sensing (MUSES) platform. The instrument has 235 spectral bands in the wavelength range from visible (400 nm) to near-infrared (1000 nm), which results in a 2.5 nm spectral sampling distance and a ground sampling distance of 30 m from 400 km orbit of the International Space Station. In this article, the design of the instrument will be described.

The atmospheric correction of satellite images based on radiative transfer calculations is a prerequisite for many remote sensing applications. The software package ATCOR, developed at the German Aerospace Center (DLR), is a versatile atmospheric correction software, capable of processing data acquired by many different optical satellite sensors. Based on this well established algorithm, a new Python-based atmospheric correction software has been developed to generate L2A products of Sentinel-2, Landsat-8, and of new space-based hyperspectral sensors such as DESIS (DLR Earth Sensing Imaging Spectrometer) and EnMAP (Environmental Mapping and Analysis Program). This paper outlines the underlying algorithms of PACO, and presents the validation results by comparing L2A products generated from Sentinel-2 L1C images with in situ (AERONET and RadCalNet) data within VNIR-SWIR spectral wavelengths range.

Remote sensing datasets and methods are suitable for mapping and managing the natural resources like minerals, clean water, and energy and also govern their sustainability nowadays. Hyperspectral (HS) imaging has immense potential for rock type classification, mineral mapping, and identification. This work demonstrates the potential of feature extraction techniques and unsupervised machine learning methods for the space-borne hyperspectral remote sensing data in characterizing and identifying mineral and classifying rock type in Banswara, Rajasthan, India. Feature extraction techniques can reveal variations within the data, which can help identify geological areas, reduce noise, and check the dimensionality of the data. Singular value decomposition (SVD)-based principal component analysis (PCA), kernel PCA (KPCA), minimum noise fraction (MNF), and independent component analysis (ICA) were tested for lithological mapping using recently launched DLR Earth Sensing Imaging Spectrometer Hyperspectral (DESIS) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) data in order to map geologically significant areas. Unsupervised machine learning methods, such as Iterative Self-Organizing Data Analysis Technique (ISODATA) and K -means, were also employed. Vertex component analysis (VCA) was utilized to check for similarity and identify various spectral features. Our work demonstrates the advantages of using feature extraction algorithms such as PCA and KPCA over MNF and ICA in geological mapping and interpretability. We recommend K -means as the preferred method for lithological classification of hyperspectral remote sensing data. Our work highlights the potential of advanced feature extraction algorithms for mineral mapping using hyperspectral data, providing different ways to identify minerals and ultimately leading to better mineral resource management.

This study presents the selection of 20 Extended Pseudo Invariant Calibration Sites (EPICS) for radiometric calibration and the derivation of their hyperspectral profiles using the DLR Earth Sensing Imaging Spectrometer (DESIS) and Hyperion data. The hyperspectral profile of one of these clusters, the GONA-EPICS cluster, was validated against ground truth measurements from the RadCalNet Gobabeb Namibia (GONA) site, demonstrating statistical agreement within their respective uncertainties through Welch’s test. The applicability of these hyperspectral profiles was further evaluated by generating Spectral Band Adjustment Factor (SBAF) between Landsat 8 and Sentinel-2A using the GONA-EPICS hyperspectral profile and comparing them to SBAF values derived from RadCalNet GONA site measurements. SBAF results were statistically the same, while SBAF derived from the combined DESIS and Hyperion data exhibited reduced uncertainty compared to those derived using Hyperion data alone, which is attributed to DESIS’s finer spectral resolution (2.5 nm vs. 10 nm). To assess EPICS applicability in cross-calibration, Cluster 13-GTS, which includes pixels from the Libya 4 CNES ROI, was used as a target. Cross-calibration gains obtained using EPICS and the T2T cross-calibration methodology were compared to those from the traditional cross-calibration approach using Libya 4 CNES ROI. Results demonstrated statistically similar gains, with EPICS achieving an uncertainty better than 6% across all bands compared to 4.4% for the traditional method, while enabling global coverage for daily cross-calibration opportunities. This study introduces globally distributed EPICS with validated hyperspectral profiles, offering enhanced spectral resolution and reliability for radiometric calibration and stability monitoring. The methodology supports efficient global scale sensor calibration and prepares for future hyperspectral missions.

Spiders are commonly found in terrestrial environments and many rely heavily on their silks for fitness related tasks such as reproduction and dispersal. Although rare, a few species occupy aquatic or semi-aquatic habitats and for them, silk-related specializations are also essential to survive in aquatic environments. Most spider silks studied to date are from cob-web and orb-web weaving species, leaving the silks from many other terrestrial spiders as well as water-associated spiders largely undescribed. Here, we characterize silks from three Dictynoidea species: the aquatic spiders Argyroneta aquatica and Desis marina as well as the terrestrial Badumna longinqua . From silk gland RNA-Seq libraries, we report a total of 47 different homologs of the spidroin (spider fibroin) gene family. Some of these 47 spidroins correspond to known spidroin types (aciniform, ampullate, cribellar, pyriform, and tubuliform), while other spidroins represent novel branches of the spidroin gene family. We also report a hydrophobic amino acid motif (GV) that, to date, is found only in the spidroins of aquatic and semi-aquatic spiders. Comparison of spider silk sequences to the silks from other water-associated arthropods, shows that there is a diversity of strategies to function in aquatic environments.

This work aims to show the potential of imaging spectroscopy in assessing water quality and aquatic vegetation in Lake Trasimeno, Italy. Hyperspectral reflectance data from the PRISMA, DESIS and EnMAP missions (2019–2022, summer periods) were compared with in situ measurements from WISPStation and used as inputs for water quality product generation algorithms. The bio-optical model BOMBER was run to simultaneously retrieve water quality parameters (Chlorophyll-a (Chl-a) and Total Suspended Matter, (TSM)) and the coverage of submerged and emergent macrophytes (SM, EM); value-added products, such as Phycocyanin concentration maps, were generated through a machine learning approach. The results showed radiometric agreement between satellite and in situ data, with R2 > 0.9, a Spectral Angle < 10° and water quality mapping errors < 30%. Both SM and EM coverage varied significantly from 2019 (135 ha, 0 ha, respectively) to 2022 (2672 ha, 343 ha), likely influenced by changes in rainfall and lake levels. The areas of greatest variability in Chl-a and TSM were identified in the littoral zones in the western side of the lake, while the highest variation in the fractional cover of SM and density of EM were observed in the south-eastern region; this information could support the water authorities’ monitoring activities. To this end, further developments to improve the reference field data for the validation of water quality products are recommended.

The monitoring of water bio-physical parameters and the management of aquatic ecosystems are crucial to cope with the current state of inland water degradation. Not only does water quality monitoring support management decision making, it also provides vital insights to better understand changing structural and functional lake processes. Remote sensing has been widely recognized as an essential integrating technique for water quality monitoring, thanks to its capabilities to utilize both historical archive data for thousands of lakes as well as near-real time observations at multiple scales. To date, most of the applications developed for inland water have been based on multispectral and mid to coarse spatial resolution satellites, while a new generation of spaceborne imaging spectroscopy is now available, and future missions are under development. This review aims to present the exploitation of data gathered from two currently orbiting hyperspectral sensors (i.e., PRISMA and DESIS) to retrieve water quality parameters across different aquatic ecosystems, encompassing deep clear lakes and river dammed reservoirs.

The reflectance-based vicarious calibration approach uses measurements at well-understood test sites to provide top-of-atmosphere reference reflectance values suitable for inter-calibration approaches and does not require coincident views. The challenge is that results from such data may suffer from high variability from day to day. Data from high-quality sensors, such as the imaging spectrometers on the International Space Station (ISS) platform, provide an opportunity to use improved fine spectral information about the test sites with various sun/sensor geometries and site surface and atmospheric conditions to improve the test sites’ characterization. The results here are based on data from the DLR Earth Sensing Imaging Spectrometer (DESIS) instrument installed on the ISS since 2018 combined with output from the Radiometric Calibration Network (RadCalNet) site at Railroad Valley Playa (RRV) to decouple the effects of sun/sensor geometry from the RadCalNet predictions. The approach here uses the precessing orbit of the ISS to allow similar sensor view zenith angles at varying sun angles over short periods that limit the impact of any sensor changes and highlight the bi-directional effects of the surface reflectance and atmospheric conditions. DESIS data collected at (i) similar solar angles but varying view angles, (ii) similar sensor angles and varying solar angles, and (iii) similar scatter angles are compared. The DESIS results indicate that the top-of-atmosphere reflectance spectra for RRV at similar solar zenith angles but with varying sensor viewing angles provide more consistent data than those with varying solar zenith but with similar sensor viewing angles. In addition, comparisons of reflectance spectra of the site performed in terms of the sensor view scatter angle show good agreement, indicating that a directional reflectance correction would be straightforward and could offer a significant improvement in the use of RadCalNet data. The work shows that observations from imaging spectroscopy data from DESIS, and eventually Earth Surface Mineral Dust Source Investigation (EMIT), Surface Biology and Geology (SBG), and the climate-quality sensor CLARREO Pathfinder (CPF), provide the opportunity for the development of a model-based, SI-traceable prediction of at-sensor radiance over the RRV site that would serve as the basis for similar site characterizations with error budgets valid for arbitrary view and illumination angles.

On 19th September 2021, a protracted eruption of the Cumbre Vieja Volcano on the Canary Island of La Palma commenced and continued for a duration of 12 weeks. Lava flows starting from the rift zone at the mid-western flank of Cumbre Vieja advanced toward the western coast of the island. The eruption was monitored by different remote sensing satellites, including the Copernicus Sentinel missions and DESIS. The Sentinel-2 Copernicus satellites acquired multispectral data from 15th September onward. On September 30th, and with a difference of ∼2 h with respect to Sentinel-2 A, the DESIS hyperspectral sensor also acquired data from the volcano and then again on 15th October 2021. Typically, mid-infrared (around 3.8 μm) data are used for the thermal analysis of active lava flows. However, neither Sentinel-2 nor DESIS possesses mid-infrared bands and the Sentinel-2 high-wavelengths bands (∼2 μm) have some limitations. Nevertheless, the hyperspectral character of DESIS enables the analysis of active erupting volcanoes in near-infrared wavelengths. The results of this analysis find fluid lava temperatures of about 1100–1200 K but there are problems associated with the high-temperature lava spectral emissivity.