Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign

Thompson, David R.; Gao, Bo; Green, Robert O.; Roberts, Dar A.; Dennison, Philip E.; Lundeen, S.

doi:10.1016/j.rse.2015.02.010

Cited by 178 publications

(146 citation statements)

References 27 publications

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“…As in Thompson et al (2015Thompson et al ( , 2016 we applied a logarithmic transformation resulting in a nonnegative least-squares problem (Lawson and Hanson, 1974):…”

Section: Methodsmentioning

confidence: 99%

“…We calculated the H 2 O vapor absorption coefficients using the HITRAN 2012 line list (Rothman et al, 2013) via the Oxford Reference Forward Model (Dudhia, 2014). We initialized the vapor abundance using a band ratio retrieval as in Thompson et al (2015) and used this to calculate an "effective" absorption coefficient of band-aggregated vapor lines for use in the least-squares retrieval. Other atmospheric gases did not significantly impact the shape of vapor absorption features.…”

Section: Methodsmentioning

confidence: 99%

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Global spectroscopic survey of cloud thermodynamic phase at high spatial resolution, 2005–2015

et al. 2018

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Abstract. The distribution of ice, liquid, and mixed phase clouds is important for Earth's planetary radiation budget, impacting cloud optical properties, evolution, and solar reflectivity. Most remote orbital thermodynamic phase measurements observe kilometer scales and are insensitive to mixed phases. This under-constrains important processes with outsize radiative forcing impact, such as spatial partitioning in mixed phase clouds. To date, the fine spatial structure of cloud phase has not been measured at global scales. Imaging spectroscopy of reflected solar energy from 1.4 to 1.8 µm can address this gap: it directly measures ice and water absorption, a robust indicator of cloud top thermodynamic phase, with spatial resolution of tens to hundreds of meters. We report the first such global high spatial resolution survey based on data from 2005 to 2015 acquired by the Hyperion imaging spectrometer onboard NASA's Earth Observer 1 (EO-1) spacecraft. Seasonal and latitudinal distributions corroborate observations by the Atmospheric Infrared Sounder (AIRS). For extratropical cloud systems, just 25 % of variance observed at GCM grid scales of 100 km was related to irreducible measurement error, while 75 % was explained by spatial correlations possible at finer resolutions.

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“…As in Thompson et al (2015Thompson et al ( , 2016 we applied a logarithmic transformation resulting in a nonnegative least-squares problem (Lawson and Hanson, 1974):…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Global spectroscopic survey of cloud thermodynamic phase at high spatial resolution, 2005–2015

et al. 2018

Self Cite

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“…The PRISM instrument has 246 spectral bands between 350-1046 nm with a FWHM that ranges from 3.27 nm to 3.46 nm. The image data were atmospherically corrected using a variant of the ATREM model [51][52][53]. The at-surface reflectance products were divided by pi followed by sunglint correction described in Lee et al [22] to generate above-water remote sensing reflectance.…”

Section: Prism Imagery and Preprocessingmentioning

confidence: 99%

Hyperspectral Shallow-Water Remote Sensing with an Enhanced Benthic Classifier

2018

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Hyperspectral remote sensing inversion models utilize spectral information over optically shallow waters to retrieve optical properties of the water column, bottom depth and reflectance, with the latter used in benthic classification. Accuracy of these retrievals is dependent on the spectral endmember(s) used to model the bottom reflectance during the inversion. Without prior knowledge of these endmember(s) current approaches must iterate through a list of endmember-a computationally demanding task. To address this, a novel lookup table classification approach termed HOPE-LUT was developed for selecting the likely benthic endmembers of any hyperspectral image pixel. HOPE-LUT classifies a pixel as sand, mixture or non-sand, then the latter two are resolved into the three most likely classes. Optimization subsequently selects the class (out of the three) that generated the best fit to the remote sensing reflectance. For a coral reef case, modeling results indicate very high benthic classification accuracy (>90%) for depths less than 4 m of common coral reef benthos. These accuracies decrease substantially with increasing depth due to the loss of bottom information, especially the spectral signatures. We applied this technique to hyperspectral airborne imagery of Heron Reef, Great Barrier Reef and generated benthic habitat maps with higher classification accuracy compared to standard inversion models.

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“…The AVIRIS-C radiance data were processed by NASA's Jet Propulsion Laboratory into HyspIRI Precursor Data Products ( Figure 1) and can be downloaded at http://aviris.jpl.nasa.gov. The 30-m simulated HyspIRI products include, in order: orthrorectification and 90 × 90-m Gaussian-weighted resampling of at-sensor radiance to 30 m pixels, addition of noise approximating a HyspIRI visible-SWIR Noise Equivalent Delta Radiance (NEdL) function, and ATREM-based per-pixel atmospheric correction and apparent surface reflectance retrieval [6,30]. Bands in the shortwave infrared with strong atmospheric water vapor absorption and poor signal-to-noise were removed, leaving 186 bands for analysis per season.…”

Section: Simulated Hyspiri Imagerymentioning

confidence: 99%

One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California

Guidici

Clark

2017

Remote Sensing

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Abstract:In this study, a 1-D Convolutional Neural Network (CNN) architecture was developed, trained and utilized to classify single (summer) and three seasons (spring, summer, fall) of hyperspectral imagery over the San Francisco Bay Area, California for the year 2015. For comparison, the Random Forests (RF) and Support Vector Machine (SVM) classifiers were trained and tested with the same data. In order to support space-based hyperspectral applications, all analyses were performed with simulated Hyperspectral Infrared Imager (HyspIRI) imagery. Three-season data improved classifier overall accuracy by 2.0% (SVM), 1.9% (CNN) to 3.5% (RF) over single-season data. The three-season CNN provided an overall classification accuracy of 89.9%, which was comparable to overall accuracy of 89.5% for SVM. Both three-season CNN and SVM outperformed RF by over 7% overall accuracy. Analysis and visualization of the inner products for the CNN provided insight to distinctive features within the spectral-temporal domain. A method for CNN kernel tuning was presented to assess the importance of learned features. We concluded that CNN is a promising candidate for hyperspectral remote sensing applications because of the high classification accuracy and interpretability of its inner products.

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Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign

Cited by 178 publications

References 27 publications

Global spectroscopic survey of cloud thermodynamic phase at high spatial resolution, 2005–2015

Global spectroscopic survey of cloud thermodynamic phase at high spatial resolution, 2005–2015

Hyperspectral Shallow-Water Remote Sensing with an Enhanced Benthic Classifier

One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California

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