Imaging spectrometer stray spectral response: In-flight characterization, correction, and validation

Thompson, David R.; Boardman, Joseph W.; Eastwood, Michael L.; Green, Robert O.; Haag, Justin M.; Mouroulis, Pantazis; Gorp, B. Van

doi:10.1016/j.rse.2017.09.015

Cited by 44 publications

(47 citation statements)

References 33 publications

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“…This level of accuracy is sufficient to make the approximation error a smaller contributor to total uncertainty than other unknowns in the measurement system. For example, it is generally a similar magnitude to relative calibration error of different focal plane array ele- ments, which can vary slightly due to drift between calibrations (Thompson et al, 2018a).…”

Section: Neural Network For Radiative Transfer Modelingmentioning

confidence: 99%

“…PRISM uses a push-broom design and observes a cross-track angular field of view spanning approximately 30 • , and is designed to observe coastal ocean environments in the 350-1050 nm spectral range at approximately 3 nm spectral sampling. The instrument was mounted onboard a high-altitude ER-2 aircraft which overflew Santa Monica, USA, in October 2015 at 20 km above sea level (Thompson et al, 2018b;Trinh et al, 2017). At this altitude, the instrument measured the scene through nearly all of Earth's atmospheric scattering and absorption, providing a challenging test case with relevance to future orbital instruments.…”

Section: Neural Rtm Emulation For Prismmentioning

confidence: 99%

“…Continuing our case study, we begin by computing radiometric calibration factors for the PRISM flight line via vicarious calibration. This procedure, similar to standard practice calibration for imaging spectrometers (Thompson et al, 2018a), projects the residual error in retrieved surface reflectance back into radiance space where it becomes a multiplicative correction factor applied independently to each channel. We generate a "standardized" surface reflectance target by performing a first-principles retrieval for a beach sand radiance spectrum manually selected from the target PRISM image.…”

Section: Atmospheric Correction With the Neural Rtm Emulatormentioning

confidence: 99%

“…Recent work suggested the use of nonparametric function approximators such as neural networks (Verrelst et al, 2016(Verrelst et al, , 2017Thompson et al, 2018a) or Gaussian processes (Martino et al, 2017) for this purpose. Investigators can train such models using prior runs of radiative transfer models over relevant ranges of surface and atmospheric conditions.…”

Section: Introductionmentioning

confidence: 99%

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Neural network radiative transfer for imaging spectroscopy

et al. 2019

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Abstract. Visible–shortwave infrared imaging spectroscopy provides valuable remote measurements of Earth's surface and atmospheric properties. These measurements generally rely on inversions of computationally intensive radiative transfer models (RTMs). RTMs' computational expense makes them difficult to use with high-volume imaging spectrometers, and forces approximations such as lookup table interpolation and surface–atmosphere decoupling. These compromises limit the accuracy and flexibility of the remote retrieval; dramatic speed improvements in radiative transfer models could significantly improve the utility and interpretability of remote spectroscopy for Earth science. This study demonstrates that nonparametric function approximation with neural networks can replicate radiative transfer calculations and generate accurate radiance spectra at multiple wavelengths over a diverse range of surface and atmosphere state parameters. We also demonstrate such models can act as surrogate forward models for atmospheric correction procedures. Incorporating physical knowledge into the network structure provides improved interpretability and model efficiency. We evaluate the approach in atmospheric correction of data from the PRISM airborne imaging spectrometer, and demonstrate accurate emulation of radiative transfer calculations, which run several orders of magnitude faster than first-principles models. These results are particularly amenable to iterative spectrum fitting approaches, providing analytical benefits including statistically rigorous treatment of uncertainty and the potential to recover information on spectrally broad signals.

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Section: Neural Network For Radiative Transfer Modelingmentioning

confidence: 99%

Section: Neural Rtm Emulation For Prismmentioning

confidence: 99%

Section: Atmospheric Correction With the Neural Rtm Emulatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neural network radiative transfer for imaging spectroscopy

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“…However, recent sensor technology and algorithm advancements have greatly improved the quality of airborne imagery [27][28][29]. This has also allowed for better characterization of the atmospheric constituents including solar irradiance [30,31] and techniques for improved calibration of airborne sensors [32].…”

Section: Introductionmentioning

confidence: 99%

Pushing the Limits of Seagrass Remote Sensing in the Turbid Waters of Elkhorn Slough, California

et al. 2019

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Remote sensing imagery has been successfully used to map seagrass in clear waters, but here we evaluate the advantages and limitations of different remote sensing techniques to detect eelgrass in the tidal embayment of Elkhorn Slough, CA. Pseudo true-color imagery from Google Earth and broadband satellite imagery from Sentinel-2 allowed for detection of the various beds, but retrievals particularly in the deeper Vierra bed proved unreliable over time due to variable image quality and environmental conditions. Calibrated water-leaving reflectance spectrum from airborne hyperspectral imagery at 1-m resolution from the Portable Remote Imaging SpectroMeter (PRISM) revealed the extent of both shallow and deep eelgrass beds using the HOPE semi-analytical inversion model. The model was able to reveal subtle differences in spectral shape, even when remote sensing reflectance over the Vierra bed was not visibly distinguishable. Empirical methods exploiting the red edge of reflectance to differentiate submerged vegetation only retrieved the extent of shallow alongshore beds. The HOPE model also accurately retrieved the water column absorption properties, chlorophyll-a, and bathymetry but underestimated the particulate backscattering and suspended matter when benthic reflectance was represented as a horizontal eelgrass leaf. More accurate water column backscattering could be achieved by the use of a darker bottom spectrum representing an eelgrass canopy. These results illustrate how high quality atmospherically-corrected hyperspectral imagery can be used to map eelgrass beds, even in regions prone to sediment resuspension, and to quantify bathymetry and water quality.

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Retrieval of Atmospheric Parameters and Surface Reflectance from Visible and Shortwave Infrared Imaging Spectroscopy Data

et al. 2018

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Remote imaging spectroscopy in the 0.4-2.5-μm visible and shortwave infrared (VSWIR) range captures the majority of solar-reflected energy and enables a wide range of earth surface studies. This spectral range is also influenced by atmospheric effects including absorption from atmospheric gases and aerosols, Rayleigh scattering, and particle scattering. Globally consistent surface measurements must compensate for these atmospheric effects. This article reviews the physical and mathematical foundations of modern VSWIR atmospheric retrieval, focusing on imaging spectrometers. We assess sensitivity of the retrieval to errors in atmospheric state estimation. Finally, we describe some promising avenues of future research to support the next generation of orbital imaging spectrometers.

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Imaging spectrometer stray spectral response: In-flight characterization, correction, and validation

Cited by 44 publications

References 33 publications

Neural network radiative transfer for imaging spectroscopy

Neural network radiative transfer for imaging spectroscopy

Pushing the Limits of Seagrass Remote Sensing in the Turbid Waters of Elkhorn Slough, California

Retrieval of Atmospheric Parameters and Surface Reflectance from Visible and Shortwave Infrared Imaging Spectroscopy Data

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