Optical design of the Earth Surface Mineral Dust Source Investigation (EMIT) imaging spectrometer

Bradley, Christine; Thingvold, Erik; Moore, Lori B.; Haag, Justin M.; Raouf, Nasrat; Mouroulis, Pantazis; Green, Robert O.

doi:10.1117/12.2568019

Cited by 22 publications

(14 citation statements)

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“…For spaceborne instruments, the sampling will depend on the swath size and orbital geometry. EMIT's approximately 75 km swath (Bradley et al, 2020) spans distances larger than those considered here, and so we expect sampling to be sufficient regardless of orbital configuration. Similarly, ESA's CHIME contractor notes a 128 km swath, and similar capacities are expected for the missions that address NASA's Surface Biology and Geology (SBG) and Aerosols, Clouds, Convection and Precipitation (ACCP) designated observables.…”

Section: Discussionmentioning

confidence: 87%

New sampling strategy mitigates a solar-geometry-induced bias in sub-kilometre vapour scaling statistics derived from imaging spectroscopy

et al. 2022

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Abstract. Upcoming spaceborne imaging spectrometers will retrieve clear-sky total column water vapour (TCWV) over land at a horizontal resolution of 30–80 m. Here we show how to obtain, from these retrievals, exponents describing the power-law scaling of sub-kilometre horizontal variability in clear-sky bulk planetary boundary layer (PBL) water vapour (q) accounting for realistic non-vertical sunlight paths. We trace direct solar beam paths through large eddy simulations (LES) of shallow convective PBLs and show that retrieved 2-D water vapour fields are “smeared” in the direction of the solar azimuth. This changes the horizontal spatial scaling of the field primarily in that direction, and we address this by calculating exponents perpendicular to the solar azimuth, that is to say flying “across” the sunlight path rather than “towards” or “away” from the Sun. Across 23 LES snapshots, at solar zenith angle SZA = 60∘ the mean bias in calculated exponent is 38 ± 12 % (95 % range) along the solar azimuth, while following our strategy it is 3 ± 9 % and no longer significant. Both bias and root-mean-square error decrease with lower SZA. We include retrieval errors from several sources, including (1) the Earth Surface Mineral Dust Source Investigation (EMIT) instrument noise model, (2) requisite assumptions about the atmospheric thermodynamic profile, and (3) spatially nonuniform aerosol distributions. By only considering the direct beam, we neglect 3-D radiative effects such as light scattered into the field of view by nearby clouds. However, our proposed technique is necessary to counteract the direct-path effect of solar geometries and obtain unique information about sub-kilometre PBL q scaling from upcoming spaceborne spectrometer missions.

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Section: Discussionmentioning

confidence: 87%

New sampling strategy mitigates a solar-geometry-induced bias in sub-kilometre vapour scaling statistics derived from imaging spectroscopy

et al. 2022

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“…A large number of satellite imaging spectrometer missions capable of mapping CH 4 and CO 2 point source emissions have recently began operation or are in development, including PRISMA (Loizzo et al, 2019), EnMAP (Guanter et al, 2015), EMIT (Bradley et al, 2020), SBG (Poulter et al, 2019), and CHIME (Nieke and Rast, 2019). Future work should evaluate the utility of both scene-specific and intra-scene correction of unit enhancement spectra with matched filter approaches, as well as compare results from matched filter approaches to results from DOAS (Ayasse et al, 2019;Cusworth et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Impact of Scene-Specific Enhancement Spectra on Matched Filter Greenhouse Gas Retrievals from Imaging Spectroscopy

Foote,

Dennison,

Sullivan

et al. 2021

Preprint

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Matched filter techniques have been widely used for retrieval of greenhouse gas enhancements from imaging spectroscopy datasets. While multiple algorithmic techniques and refinements have been proposed, the greenhouse gas target spectrum used for concentration enhancement estimation has remained largely unaltered since the introduction of quantitative matched filter retrievals. The magnitude of retrieved methane and carbon dioxide enhancements, and thereby integrated mass enhancements (IME) and estimated flux of point-source emitters, is heavily dependent on this target spectrum. Current standard use of molecular absorption coefficients to create unit enhancement target spectra does not account for absorption by background concentrations of greenhouse gases, solar and sensor geometry, or atmospheric water vapor absorption. We introduce geometric and atmospheric parameters into the generation of scene-specific unit enhancement spectra to provide target spectra that are compatible with all greenhouse gas retrieval matched filter techniques. Specifically, we use radiative transfer modeling to model four parameters that are expected to change between scenes: solar zenith angle, column water vapor, ground elevation, and sensor altitude. These parameter values are well defined, with low variation within a single scene. A benchmark dataset consisting of ten AVIRIS-NG airborne imaging spectrometer scenes was used to compare IME retrieved using a matched filter algorithm. For methane plumes, IME resulting from use of standard, generic enhancement spectra varied from −22 to +28.7% compared to scene-specific enhancement spectra. Due to differences in spectral shape between the generic and scene-specific enhancement spectra, differences in methane plume IME were linked to surface spectral characteristics in addition to geometric and atmospheric parameters. IME differences were much larger for carbon dioxide plumes, with generic enhancement spectra producing integrated mass enhancements −76.1 to −48.1% compared to scene-specific enhancement spectra. Fluxes calculated from these integrated enhancements would vary by the same percentages, assuming equivalent wind conditions. Methane and carbon dioxide IME were most sensitive to changes in solar zenith angle and ground elevation. We introduce an interpolation approach that can efficiently generate scene-specific unit enhancement spectra for given sets of parameters. Scene-specific target spectra can improve confidence in greenhouse gas retrievals and flux estimates across collections of scenes with diverse geometric and atmospheric conditions.Research Highlights:1. Matched filter greenhouse gas retrievals should account for atmosphere and geometry 2. A benchmark imaging spectroscopy dataset facilitates comparison of retrieval methods 3. Standard approach has −22 to +29% of CH 4 mass compared to scene-specific approach 4. Standard approach omits 48.1-76.1% of CO 2 mass compared to scene-specific approach 5. CH 4 and CO 2 retrievals are most sensitive to solar zenith angle and gro...

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“…NASA's Earth Mineral Dust Source Investigation (EMIT) mission is designed to study the mineralogy of Earth's dust-forming regions using spaceborne imaging spectroscopy [1]. The EMIT instrument is a Dyson imaging spectrometer with an 11° cross-track field of view, with a fast (F1/8) and wide-swath (1240 samples) optical system achieving roughly 7.4 nm spectral sampling across the 380 -2500 nm spectral range at high signal-to-noise [2]. EMIT was launched on July 14, 2022 via SpaceX Dragon and successfully autonomously docked to the forward-facing port of the International Space Station (ISS) [3].…”

Section: Introductionmentioning

confidence: 99%

Topological Generality and Spectral Dimensionality in the Earth Mineral Dust Source Investigation (EMIT) Using Joint Characterization and the Spectral Mixture Residual

Sousa

Small

2023

Remote Sensing

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NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) mission seeks to use spaceborne imaging spectroscopy (hyperspectral imaging) to map the mineralogy of arid dust source regions. Here we apply recent developments in Joint Characterization (JC) and the spectral Mixture Residual (MR) to explore the information content of data from this novel mission. Specifically, for a mosaic of 20 spectrally diverse scenes, we find: (1) a generalized three-endmember (Substrate, Vegetation, Dark; SVD) spectral mixture model is capable of capturing the preponderance (99% in three dimensions) of spectral variance with low misfit (99% pixels with <3.7% RMSE); (2) manifold learning (UMAP) is capable of identifying spatially coherent, physically interpretable clustering relationships in the spectral feature space; (3) UMAP yields results that are at least as informative when applied to the MR as when applied to raw reflectance; (4) SVD fraction information usefully contextualizes UMAP clustering relationships, and vice-versa (JC); and (5) when EMIT data are convolved to spectral response functions of multispectral instruments (Sentinel-2, Landsat 8/9, Planet SuperDove), SVD fractions correlate strongly across sensors, but UMAP clustering relationships for the EMIT hyperspectral feature space are far more informative than for simulated multispectral sensors. Implications are discussed for both the utility of EMIT data in the near-term and for the potential of high signal-to-noise (SNR) spaceborne imaging spectroscopy more generally, to transform the future of optical remote sensing in the years and decades to come.

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Optical design of the Earth Surface Mineral Dust Source Investigation (EMIT) imaging spectrometer

Cited by 22 publications

References 0 publications

New sampling strategy mitigates a solar-geometry-induced bias in sub-kilometre vapour scaling statistics derived from imaging spectroscopy

New sampling strategy mitigates a solar-geometry-induced bias in sub-kilometre vapour scaling statistics derived from imaging spectroscopy

Impact of Scene-Specific Enhancement Spectra on Matched Filter Greenhouse Gas Retrievals from Imaging Spectroscopy

Topological Generality and Spectral Dimensionality in the Earth Mineral Dust Source Investigation (EMIT) Using Joint Characterization and the Spectral Mixture Residual

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