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Special Issue "MISR"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmosphere Remote Sensing".

Deadline for manuscript submissions: closed (19 October 2018)

Special Issue Editors

Guest Editor
Dr. David J. Diner

Jet Propulsion Laboratory, MS 233-200, 4800 Oak Grove Drive, Pasadena, CA 91109 USA
Website | E-Mail
Phone: 8183546319
Interests: remote sensing instrument development; atmospheric optics; aerosol climate, environmental, and health impacts; planetary atmospheres
Guest Editor
Prof. Thomas P. Ackerman

Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Box 355672, Seattle, WA 98195 USA
Website | E-Mail
Interests: science and ethics of climate engineering; ocean–atmosphere coupling and the effects of cloud feedbacks; use of satellite and ground-based data to evaluate climate model cloud properties; aerosol impacts on climate
Guest Editor
Prof. Eugene E. Clothiaux

Department of Meteorology and Atmospheric Science, 503 Walker Building, Pennsylvania State University, University Park, PA 16802 USA
Website | E-Mail
Interests: atmospheric radiative transfer; ground- and satellite-based observations of clouds and the surface; cloud and radiative transfer parameterizations in numerical weather prediction and climate models; data assimilation
Guest Editor
Dr. Robert J. Swap

NASA Goddard Space Flight Center, Mail Code 614, Greenbelt, MD 20771 USA
Website | E-Mail
Interests: atmosphere–land–ocean interactions; coupling between the natural and human environment; synergies between satellite and ground-based sensor networks

Special Issue Information

Dear Colleagues,

The Multi-angle Imaging SpectroRadiometer (MISR) instrument has been flying aboard NASA’s Terra satellite for more than 18 years. The moderately high resolution observations at nine view angles have enabled the generation of long-term data records, which are still being acquired, of well-calibrated and georectified multiangular imagery; aerosol properties over land and ocean; aerosol plume injection heights and wind speeds; cloud-top heights, albedos, spatial textures, and height-resolved wind vectors; land surface bidirectional reflectance factors, albedos, and canopy structural parameters; maps of ice sheet roughness; and other Earth atmospheric and surface parameters that capitalize on the unique instrument design.

MISR data continue to be used in a diverse set of science applications, including studies of climate forcing and feedbacks, response by aerosols and clouds, impacts of particulate matter on human health, changes to structure of the land surface and cryosphere, and development of new remote sensing methodologies, such as passive mapping of tropospheric winds and their benefits for weather forecasting. The nearly two-decade-long record of MISR data makes it timely to announce a Special Issue devoted to MISR applications and results. Topics of interest include, but are not limited to, those mentioned above, with emphasis on recent scientific findings and studies making use of the long-term data record. Papers on novel algorithmic approaches, product validation, and long-term instrument calibration are also invited.

Dr. David J. Diner
Prof. Thomas P. Ackerman
Prof. Eugene E. Clothiaux
Dr. Robert J. Swap
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Multiangle imaging
  • Aerosol climate, environmental, and human health impacts
  • Cloud-climate interactions
  • Land surface structure
  • Aerosol-cloud-surface interactions

Published Papers (5 papers)

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Research

Open AccessArticle Improving Land Cover Classifications with Multiangular Data: MISR Data in Mainland Spain
Remote Sens. 2018, 10(11), 1717; https://doi.org/10.3390/rs10111717
Received: 23 August 2018 / Revised: 18 October 2018 / Accepted: 24 October 2018 / Published: 31 October 2018
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Abstract
In this study, we deal with the application of multiangular data from the Multiangle Imaging Spectroradiometer (MISR) sensor for studying the effect of surface anisotropy and directional information on the classification accuracy for different land covers with different rate of disaggregation classes (from
[...] Read more.
In this study, we deal with the application of multiangular data from the Multiangle Imaging Spectroradiometer (MISR) sensor for studying the effect of surface anisotropy and directional information on the classification accuracy for different land covers with different rate of disaggregation classes (from four to 35 different classes) from a Mediterranean bioregion in Iberian, Spain. We used various MISR band groups from nadir to blue, green, red, and NIR channels at nadir and off-nadir. The MISR data utilized here were provided by the L1B2T product (275 m spatial resolution) and belonged to two different orbits. We performed 23 classifications with the k-means algorithm to test multiangular data, number of clusters, and iteration effects. Our findings confirmed that the multiangular information, in addition to the multispectral information used as the input of the k-means algorithm, improves the land cover classification accuracy, and this improvement increased with the level of disaggregation. A very large number of clusters produced even better improvements than multiangular data. Full article
(This article belongs to the Special Issue MISR)
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Open AccessArticle A Global Analysis of Wildfire Smoke Injection Heights Derived from Space-Based Multi-Angle Imaging
Remote Sens. 2018, 10(10), 1609; https://doi.org/10.3390/rs10101609
Received: 3 September 2018 / Revised: 28 September 2018 / Accepted: 2 October 2018 / Published: 10 October 2018
Cited by 1 | PDF Full-text (11123 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
We present an analysis of over 23,000 globally distributed wildfire smoke plume injection heights derived from Multi-angle Imaging SpectroRadiometer (MISR) space-based, multi-angle stereo imaging. Both pixel-weighted and aerosol optical depth (AOD)-weighted results are given, stratified by region, biome, and month or season. This
[...] Read more.
We present an analysis of over 23,000 globally distributed wildfire smoke plume injection heights derived from Multi-angle Imaging SpectroRadiometer (MISR) space-based, multi-angle stereo imaging. Both pixel-weighted and aerosol optical depth (AOD)-weighted results are given, stratified by region, biome, and month or season. This offers an observational resource for assessing first-principle plume-rise modelling, and can provide some constraints on smoke dispersion modelling for climate and air quality applications. The main limitation is that the satellite is in a sun-synchronous orbit, crossing the equator at about 10:30 a.m. local time on the day side. Overall, plumes occur preferentially during the northern mid-latitude burning season, and the vast majority inject smoke near-surface. However, the heavily forested regions of North and South America, and Africa produce the most frequent elevated plumes and the highest AOD values; some smoke is injected to altitudes well above 2 km in nearly all regions and biomes. Planetary boundary layer (PBL) versus free troposphere injection is a critical factor affecting smoke dispersion and environmental impact, and is affected by both the smoke injection height and the PBL height; an example assessment is made here, but constraining the PBL height for this application warrants further work. Full article
(This article belongs to the Special Issue MISR)
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Graphical abstract

Open AccessArticle Indirect Estimation of Structural Parameters in South African Forests Using MISR-HR and LiDAR Remote Sensing Data
Remote Sens. 2018, 10(10), 1537; https://doi.org/10.3390/rs10101537
Received: 5 July 2018 / Revised: 21 August 2018 / Accepted: 28 August 2018 / Published: 25 September 2018
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Abstract
Forest structural data are essential for assessing biophysical processes and changes, and promoting sustainable forest management. For 18+ years, the Multi-Angle Imaging SpectroRadiometer (MISR) instrument has been observing the land surface reflectance anisotropy, which is known to be related to vegetation structure. This
[...] Read more.
Forest structural data are essential for assessing biophysical processes and changes, and promoting sustainable forest management. For 18+ years, the Multi-Angle Imaging SpectroRadiometer (MISR) instrument has been observing the land surface reflectance anisotropy, which is known to be related to vegetation structure. This study sought to determine the performance of a new MISR-High Resolution (HR) dataset, recently produced at a full 275 m spatial resolution, and consisting of 36 Bidirectional Reflectance Factors (BRF) and 12 Rahman–Pinty–Verstraete (RPV) parameters, to estimate the mean tree height (Hmean) and canopy cover (CC) across structurally diverse, heterogeneous, and fragmented forest types in South Africa. Airborne LiDAR data were used to train and validate Random Forest models which were tested across various MISR-HR scenarios. The combination of MISR multi-angular and multispectral data was consistently effective in improving the estimation of structural parameters, and produced the lowest relative root mean square error (rRMSE) (33.14% and 38.58%), for Hmean and CC respectively. The combined RPV parameters for all four bands yielded the best results in comparison to the models of the RPV parameters separately: Hmean (R2 = 0.71, rRMSE = 34.84%) and CC (R2 = 0.60, rRMSE = 40.96%). However, the combined RPV parameters for all four bands in comparison to the MISR-HR BRF 36 band model it performed poorer (rRMSE of 5.1% and 6.2% higher for Hmean and CC, respectively). When considered separately, savanna forest type had greater improvement when adding multi-angular data, with the highest accuracies obtained for the Hmean parameter (R2 of 0.67, rRMSE of 31.28%). The findings demonstrate the potential of the optical multi-spectral and multi-directional newly processed data (MISR-HR) for estimating forest structure across Southern African forest types. Full article
(This article belongs to the Special Issue MISR)
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Open AccessArticle How Long should the MISR Record Be when Evaluating Aerosol Optical Depth Climatology in Climate Models?
Remote Sens. 2018, 10(9), 1326; https://doi.org/10.3390/rs10091326
Received: 2 July 2018 / Revised: 31 July 2018 / Accepted: 10 August 2018 / Published: 21 August 2018
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Abstract
This study used the nearly continuous 17-year observation record from the Multi- angle Imaging SpectroRadiometer (MISR) instrument on the National Aeronautics and Space Administration (NASA) Terra Earth Observing System satellite to determine which temporal subsets are long enough to define statistically stable speciated
[...] Read more.
This study used the nearly continuous 17-year observation record from the Multi- angle Imaging SpectroRadiometer (MISR) instrument on the National Aeronautics and Space Administration (NASA) Terra Earth Observing System satellite to determine which temporal subsets are long enough to define statistically stable speciated aerosol optical depth (AOD) climatologies (i.e., AOD by particle types) for purposes of climate model evaluation. A random subsampling of seasonally averaged total and speciated AOD retrievals was performed to quantitatively assess the statistical stability in the climatology, represented by the minimum record length required for the standard deviation of the subsampled mean AODs to be less than a certain threshold. Our results indicate that the multi-year mean speciated AOD from MISR is stable on a global scale; however, there is substantial regional variability in the assessed stability. This implies that in some regions, even 17 years may not provide a long enough sample to define regional mean total and speciated AOD climatologies. We further investigated the agreement between the statistical stability of total AOD retrievals from MISR and the Moderate Resolution Imaging Spectroradiometer (MODIS), also on the NASA Terra satellite. The difference in the minimum record lengths between MISR and MODIS climatologies of total AOD is less than three years for most of the globe, with the exception of certain regions. Finally, we compared the seasonal cycles in the MISR total and speciated AODs with those simulated by three global chemistry transport models in the regions of climatologically stable speciated AODs. We found that only one model reproduced the observed seasonal cycles of the total and non-absorbing AODs over East China, but the seasonal cycles in total and dust AODs in all models are similar to those from MISR in Western Africa. This work provides a new method for considering the statistical stability of satellite-derived climatologies and illustrates the value of MISR’s speciated AOD data record for evaluating aerosols in global models. Full article
(This article belongs to the Special Issue MISR)
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Open AccessArticle Using Multi-Angle Imaging SpectroRadiometer Aerosol Mixture Properties for Air Quality Assessment in Mongolia
Remote Sens. 2018, 10(8), 1317; https://doi.org/10.3390/rs10081317
Received: 6 July 2018 / Revised: 14 August 2018 / Accepted: 16 August 2018 / Published: 20 August 2018
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Abstract
Ulaanbaatar (UB), the capital city of Mongolia, has extremely poor wintertime air quality with fine particulate matter concentrations frequently exceeding 500 μg/m3, over 20 times the daily maximum guideline set by the World Health Organization. Intensive use of sulfur-rich coal for
[...] Read more.
Ulaanbaatar (UB), the capital city of Mongolia, has extremely poor wintertime air quality with fine particulate matter concentrations frequently exceeding 500 μg/m3, over 20 times the daily maximum guideline set by the World Health Organization. Intensive use of sulfur-rich coal for heating and cooking coupled with an atmospheric inversion amplified by the mid-continental Siberian anticyclone drive these high levels of air pollution. Ground-based air quality monitoring in Mongolia is sparse, making use of satellite observations of aerosol optical depth (AOD) instrumental for characterizing air pollution in the region. We harnessed data from the Multi-angle Imaging SpectroRadiometer (MISR) Version 23 (V23) aerosol product, which provides total column AOD and component-particle optical properties for 74 different aerosol mixtures at 4.4 km spatial resolution globally. To test the performance of the V23 product over Mongolia, we compared values of MISR AOD with spatially and temporally matched AOD from the Dalanzadgad AERONET site and find good agreement (correlation r = 0.845, and root-mean-square deviation RMSD = 0.071). Over UB, exploratory principal component analysis indicates that the 74 MISR AOD mixture profiles consisted primarily of small, spherical, non-absorbing aerosols in the wintertime, and contributions from medium and large dust particles in the summertime. Comparing several machine learning methods for relating the 74 MISR mixtures to ground-level pollutants, including particulate matter with aerodynamic diameters smaller than 2.5 μm ( PM 2.5 ) and 10 μm ( PM 10 ), as well as sulfur dioxide ( SO 2 ), a proxy for sulfate particles, we find that Support Vector Machine regression consistently has the highest predictive performance with median test R 2 for PM 2.5 , PM 10 , and SO 2 equal to 0.461, 0.063, and 0.508, respectively. These results indicate that the high-dimensional MISR AOD mixture set can provide reliable predictions of air pollution and can distinguish dominant particle types in the UB region. Full article
(This article belongs to the Special Issue MISR)
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Graphical abstract

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