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  • 1
    Electronic Resource
    Electronic Resource
    Amsterdam : Elsevier
    Habitat International 8 (1984), S. 117-132 
    ISSN: 0197-3975
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Geography , Sociology
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Amsterdam : Elsevier
    Socio-Economic Planning Sciences 18 (1984), S. 381-389 
    ISSN: 0038-0121
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Sociology , Economics
    Type of Medium: Electronic Resource
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  • 3
    Publication Date: 2020-03-05
    Type: info:eu-repo/semantics/article
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  • 4
    Publication Date: 2020-12-10
    Description: Airborne and orbital imaging spectroscopy can facilitate the quantification of chemical and physical attributes of surface materials through analysis of spectral signatures. Prior to analysis, estimates of surface reflectance must be inferred from radiance measurements in a process known as atmospheric correction, which compensates for the distortion of the electromagnetic signal by the atmosphere. Inaccuracies in the correction process can alter characteristic spectral signatures, leading to subsequent mischaracterization of surface properties. Global observations pose new challenges for mapping surface composition, as varied atmospheric conditions and surface biomes challenge traditional atmospheric correction methods. Recent work adopted an optimal estimation (OE) approach for retrieving surface reflectance from observed radiance measurements, providing the reflectance estimates with a posterior probability. This work incorporates these input probabilities to improve the accuracy of surface feature measurements. We demonstrate this using a generic feature-fitting method that is applicable to a wide range of Earth surface studies including geology, ecosystem studies, hydrology and urban studies. Specifically, we use a probabilistic framework based on generalized Tikhonov-regularized least squares, a rigorous formulation for appropriate weighting of features by their observation uncertainty and leveraging of prior knowledge of material abundance for improving estimation accuracy. We demonstrate the validity of this procedure and quantify the increase in model performance by simulating expected accuracies in the reflectance estimation. To evaluate global uncertainties in mineral estimation, we simulate observations representative of the expected global range of atmospheric water vapor and aerosol levels, and characterize the sensitivity of our procedure to those quantities.
    Language: English
    Type: info:eu-repo/semantics/article
    Format: application/pdf
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  • 5
    Publication Date: 2022-05-16
    Description: Remote imaging spectroscopy, also known as hyperspectral imaging, uses Radiative Transfer Models (RTMs) to predict the measured radiance spectrum for a specific surface and atmospheric state. Discrepancies between RTM assumptions and physical reality can cause systematic errors in surface property estimates. We present a statistical method to quantify these model errors without invoking ground reference data. Our approach exploits scene invariants — properties of the environment which are stable over space or time — to estimate RTM discrepancies. We describe techniques for discovering these features opportunistically in flight data. We then demonstrate data-driven methods that estimate the aggregate errors due to model discrepancies without having to explicitly identify the underlying physical mechanisms. The resulting distributions can improve posterior uncertainty predictions in operational retrievals.
    Type: info:eu-repo/semantics/article
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  • 6
    Publication Date: 2020-09-21
    Description: Remote imaging spectroscopy's role in Earth science will grow in the coming decade as a series of globe-spanning spectroscopy missions launch from NASA, ESA, and other agencies. The nature of remote imaging spectroscopy will change, advancing from short regional studies to address global multi-year questions. The diversity of data will also grow with exposure to a wider range of biomes and atmospheric conditions. To execute these new investigations we must reconcile diverse observing conditions to derive consistent global maps. To this end, rigorous uncertainty quantification and propagation enables an optimal synthesis of data accounting for observing conditions and data quality. Understanding data uncertainties is also important for principled hypothesis testing, information content assessment, and informed decision making by end users. We survey prior efforts in uncertainty quantification for imaging spectroscopy, and describe methods for validating the accuracy of uncertainty predictions. We conclude with a discussion of remaining challenges and promising avenues for future research. © (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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  • 7
    Publication Date: 2022-07-25
    Description: Future global Visible Shortwave Infrared Imaging Spectrometers, such as the Surface Biology and Geology (SBG) mission, will regularly cover the Earth's entire terrestrial land area. These missions need high fidelity atmospheric correction to produce consistent maps of terrestrial and aquatic ecosystem traits. However, estimation of surface reflectance and atmospheric state is computationally challenging, and the terabyte data volumes of global missions will exceed available processing capacity. This article describes how missions can overcome this bottleneck using the spatial continuity of atmospheric fields. Contemporary imaging spectrometers oversample atmospheric spatial variability, so it is not necessary to invert every pixel. Spatially sparse solutions can train local linear emulators that provide fast, exact inversions in their vicinity. We find that estimating the atmosphere at 200 m scales can outperform traditional atmospheric correction, improving speed by one to two orders of magnitude with no measurable penalty to accuracy. We validate performance with an airborne field campaign, showing reflectance accuracies with RMSE of 1.1% or better compared to ground measurements of diverse targets. These errors are statistically consistent with retrieval uncertainty budgets. Local emulators can close the efficiency gap and make rigorous model inversion algorithms feasible for global missions such as SBG.
    Type: info:eu-repo/semantics/article
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  • 8
    Publication Date: 2022-03-10
    Description: Snow and ice melt processes on the Greenland Ice Sheet are a key in Earth’s energy balance and are acutely sensitive to climate change. Melting dynamics are directly related to a decrease in surface albedo, amongst others caused by the accumulation of light-absorbing particles (LAPs). Featuring unique spectral patterns, these accumulations can be mapped and quantified by imaging spectroscopy. We present first results for the retrieval of glacier ice properties from the spaceborne PRISMA imaging spectrometer by applying a recently developed simultaneous inversion of atmospheric and surface state using optimal estimation. The image analyzed in this study was acquired over the South-West margin of the Greenland Ice Sheet in late August 2020. The area is characterized by patterns of both clean and dark ice associated with a high amount of LAPs deposited on the surface. We present retrieval maps and uncertainties for grain size, liquid water, and algae concentration, as well as estimated reflectance spectra for different surface properties. We then show the feasibility of using imaging spectroscopy to interpret multiband sensor data to achieve high accuracy, frequently repeated observations of changing snow and ice conditions. For example, the impurity index calculated from multiband Sentinel-3 OLCI measurements is dependent on dust particles, but we show that algae concentration alone can be predicted from this data with less than 20 % uncertainty. Our study evidence that present and upcoming orbital imaging spectroscopy missions such as PRISMA, EnMAP, CHIME, and the SBG designated observable, can significantly support research of melting ice sheets.
    Type: info:eu-repo/semantics/article
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  • 9
    Publication Date: 2022-08-05
    Description: Snow and ice melt processes are a key in Earth's energy-balance and hydrological cycle. Their quantification facilitates predictions of meltwater runoff as well as distribution and availability of fresh water. They control the balance of the Earth's ice sheets and are acutely sensitive to climate change. These processes decrease the surface reflectance with unique spectral patterns due to the accumulation of liquid water and light absorbing particles (LAP), that require imaging spectroscopy to map and measure. Here we present a new method to retrieve snow grain size, liquid water fraction, and LAP mass mixing ratio from airborne and spaceborne imaging spectroscopy acquisitions. This methodology is based on a simultaneous retrieval of atmospheric and surface parameters using optimal estimation (OE), a retrieval technique which leverages prior knowledge and measurement noise in an inversion that also produces uncertainty estimates. We exploit statistical relationships between surface reflectance spectra and snow and ice properties to estimate their most probable quantities given the reflectance. To test this new algorithm we conducted a sensitivity analysis based on simulated top-of-atmosphere radiance spectra using the upcoming EnMAP orbital imaging spectroscopy mission, demonstrating an accurate estimation performance of snow and ice surface properties. A validation experiment using in-situ measurements of glacier algae mass mixing ratio and surface reflectance from the Greenland Ice Sheet gave uncertainties of ±16.4 μg/gice and less than 3%, respectively. Finally, we evaluated the retrieval capacity for all snow and ice properties with an AVIRIS-NG acquisition from the Greenland Ice Sheet demonstrating this approach's potential and suitability for upcoming orbital imaging spectroscopy missions.
    Language: English
    Type: info:eu-repo/semantics/article
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