Search Results (11)

This study addresses the critical need for radiometrically accurate and consistent hyperspectral data as the remote sensing community moves towards a hyperspectral world. Previous calibration efforts on Hyperion, the first on-orbit hyperspectral sensors, have exhibited temporal stability and absolute accuracy limitations. This work has developed and validated a novel cross-calibration methodology to address these challenges. Also, this work adds two other hyperspectral sensors, DLR Earth Sensing Imaging Spectrometer (DESIS) and Earth Surface mineral Dust Source Investigation instrument (EMIT), to maintain temporal continuity and enhance spatial coverage along with spectral resolution. The study established a robust approach for calibrating Hyperion using DESIS and EMIT. The methodology involves several key processes. First is a temporal stability assessment on Extended Pseudo Invariant Calibration Sites (EPICS) Cluster13–Global Temporal Stable (GTS) over North Africa (Cluster13–GTS) using Landsat Sensors Landsat 7 (ETM+), Landsat 8 (OLI). Second, a temporal trend correction model was developed for DESIS and Hyperion using statistically selected models. Third, absolute calibration was developed for DESIS and EMIT using multiple vicarious calibration sites, resulting in an overall absolute calibration uncertainty of 2.7–5.4% across the DESIS spectrum and 3.1–6% on non-absorption bands for EMIT. Finally, Hyperion was cross-calibrated using calibrated DESIS and EMIT as reference (with traceability to ground reference) with a calibration uncertainty within the range of 7.9–12.9% across non-absorption bands. The study also validates these calibration coefficients using OLI over Cluster13–GTS. The validation provided results suggesting a statistical similarity between the absolute calibrated hyperspectral sensors mean TOA (top-of-atmosphere) reflectance with that of OLI. This study offers a valuable contribution to the community by fulfilling the above-mentioned needs, enabling more reliable intercomparison, and combining multiple hyperspectral datasets for various applications. Full article

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. Full article

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. Full article

The satellite imagery of nighttime lights (NTLs) has been studied to understand human activities, economic development, and more recently, the ecological impact of brighter night skies. The Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) offers perhaps the most advanced nighttime imaging capabilities to date, but its large pixel size and single band capture large-scale changes in NTL while missing granular but important details, such as lighting type and brightness. To better understand individual NTL sources in a region, the spectra of nighttime lights captured by the DLR Earth Sensing Imaging Spectrometer (DESIS) were extracted and compared against near-coincident VIIRS DNB imagery. The analysis shows that DESIS’s finer spatial and spectral resolutions can detect individual NTL locations and types beyond what is possible with the DNB. Extracted night light spectra, validated against ground truth measurements, demonstrate DESIS’s ability to accurately detect and identify narrow-band atomic emission lines that characterize the spectra of high-intensity discharge (HID) light sources and the broader spectral features associated with different light-emitting diode (LED) lights. These results suggest the possible application of using hyperspectral data from moderate-resolution sensors to identify lamp construction details, such as illumination source type and light quality in low-light contexts. NTL data from DESIS and other hyperspectral sensors may improve the scientific understanding of light pollution, lighting quality, and energy efficiency by identifying, evaluating, and mapping individual and small groups of light sources. Full article

The satellite cross-calibration radiometer (SCR) is a conceptual on-orbit hyperspectral imaging radiometer that transfers the radiometric calibration from a “gold-standard” reference instrument such as the Landsat 8/9 Operational Land Imager (OLI) to other civil, international, or commercial “client” multispectral satellite systems via near-simultaneous cross-calibration acquisitions. The spectral resolution, spectral range, spatial resolution, and signal-to-noise ratio (SNR) all significantly impact the complexity and cost of hyperspectral SCRs, so it is important to understand their effect on cross-calibration quality. This paper discusses the results of a trade study to quantify the effects of varying ground sample distance (GSD), number of independent samples, and instrument/scene noise on cross-calibration gain uncertainties. The trade study used a simulated SCR cross-calibration with near-simultaneous nadir overpasses (SNOs) of the Landsat 8 OLI acting as the reference instrument and the DLR Earth Sensing Imaging Spectrometer (DESIS) acting as a surrogate SCR hyperspectral instrument. Results demonstrate that cross-calibration uncertainty is only minimally affected by spatial resolution and SNR, which may allow SCR instruments to be developed at a lower cost. Full article

The monitoring of coastal areas using remote sensing techniques is an important issue to determine the bio-optical properties of the water column and the seabed composition. New hyperspectral satellite sensors (e.g., PRISMA, DESIS or EnMap) are developed to periodically observe ecosystems. The uncertainties in the retrieved geophysical products remain a key issue to release reliable data useful for the end-users. In this study, an analytical approach based on Information theory is proposed to investigate the Cramér–Rao lower Bounds (CRB) for the uncertainties in the ocean color parameters. Practically, during the inversion process, an a priori knowledge on the estimated parameters is used since their range of variation is supposed to be known. Here, a Bayesian approach is attempted to handle such a priori knowledge. A Bayesian CRB (BCRB) is derived using the Lee et al. semianalytical radiative transfer model dedicated to shallow waters. Both environmental noise and bio-optical parameters are supposed to be random vectors that follow a Gaussian distibution. The calculation of CRB and BCRB is carried out for two hyperspectral images acquired above the French mediterranean coast. The images were obtained from the recently launched hyperspectral sensors, namely the DESIS sensor (DLR Earth Sensing Imaging Spectrometer, German Aerospace Center), and PRISMA (Precursore IpperSpettrale della Mission Applicativa—ASI, Italian Space Adjency) sensor. The comparison between the usual CRB approach, the proposed BCRB approach and experimental errors obtained for the retrieved bathymetry shows the better ability of the BCRB to determine minimum error bounds. Full article

Advances in spaceborne hyperspectral (HS) remote sensing, cloud-computing, and machine learning can help measure, model, map and monitor agricultural crops to address global food and water security issues, such as by providing accurate estimates of crop area and yield to model agricultural productivity. Leveraging these advances, we used the Earth Observing-1 (EO-1) Hyperion historical archive and the new generation DLR Earth Sensing Imaging Spectrometer (DESIS) data to evaluate the performance of hyperspectral narrowbands in classifying major agricultural crops of the U.S. with machine learning (ML) on Google Earth Engine (GEE). EO-1 Hyperion images from the 2010–2013 growing seasons and DESIS images from the 2019 growing season were used to classify three world crops (corn, soybean, and winter wheat) along with other crops and non-crops near Ponca City, Oklahoma, USA. The supervised classification algorithms: Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB), and the unsupervised clustering algorithm WekaXMeans (WXM) were run using selected optimal Hyperion and DESIS HS narrowbands (HNBs). RF and SVM returned the highest overall producer’s, and user’s accuracies, with the performances of NB and WXM being substantially lower. The best accuracies were achieved with two or three images throughout the growing season, especially a combination of an earlier month (June or July) and a later month (August or September). The narrow 2.55 nm bandwidth of DESIS provided numerous spectral features along the 400–1000 nm spectral range relative to smoother Hyperion spectral signatures with 10 nm bandwidth in the 400–2500 nm spectral range. Out of 235 DESIS HNBs, 29 were deemed optimal for agricultural study. Advances in ML and cloud-computing can greatly facilitate HS data analysis, especially as more HS datasets, tools, and algorithms become available on the Cloud. Full article

The DLR Earth Sensing Imaging Spectrometer (DESIS) is the first hyperspectral imaging spectrometer installed in the Multi-User System for Earth Sensing (MUSES) on the International Space Station (ISS) for acquiring routine science grade images from orbit. It was launched on 29 June 2018 and integrated into MUSES. DESIS measures energy in the spectral range of 400 to 1000 nm with high spatial and spectral resolution: 30 m and 2.55 nm, respectively. DESIS data should be sufficiently quantitative and accurate to use it for different applications and research. This work performs a radiometric evaluation of DESIS Level 1 product (Top of Atmosphere (TOA) reflectance) by comparing it with coincident Radiometric Calibration Network (RadCalNet) measurements at Railroad Valley Playa (RVUS), Gobabeb (GONA), and La Crau (LCFR). RVUS, GONA, and LCFR offer 4, 15, and 5 coincident datasets between DESIS and RadCalNet measurements, respectively. The results show an agreement between DESIS and RadCalNet TOA reflectance within ~5% for most spectral regions. However, there is an additional ~5% disagreement across the wavelengths affected by water vapor absorption and atmospheric scattering. Among the three RadCalNet sites, RVUS and GONA show a similar measurement disagreement with DESIS of ~5%, while LCFR differs by ~10%. Agreement between DESIS and RadCalNet measurements is variable across all three sites, likely due to surface type differences. DESIS and RadCalNet agreement show a precision of ~2.5%, 4%, and 7% at RVUS, GONA, and LCFR, respectively. RVUS and GONA, which have a similar surface type, sand, have a similar level of radiometric accuracy and precision, whereas LCFR, which consists of sparse vegetation, has lower accuracy and precision. The observed precision of DESIS Level 1 products from all the sites, especially LCFR, can be improved with a better Bidirectional Reflection Distribution Function (BRDF) characterization of the RadCalNet sites. Full article

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. Full article

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. Full article

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. Full article