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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 semimonthly 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 (16 papers)

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Open AccessArticle
Net Cloud Thinning, Low-Level Cloud Diminishment, and Hadley Circulation Weakening of Precipitating Clouds with Tropical West Pacific SST Using MISR and Other Satellite and Reanalysis Data
Remote Sens. 2019, 11(10), 1250; https://doi.org/10.3390/rs11101250
Received: 17 April 2019 / Revised: 21 May 2019 / Accepted: 22 May 2019 / Published: 27 May 2019
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Abstract
Daily gridded Multi-Angle Imaging Spectroradiometer (MISR) satellite data are used in conjunction with CERES, TRMM, and ERA-Interim reanalysis data to investigate horizontal and vertical high cloud structure, top-of-atmosphere (TOA) net cloud forcing and albedo, and dynamics relationships against local SST and precipitation as [...] Read more.
Daily gridded Multi-Angle Imaging Spectroradiometer (MISR) satellite data are used in conjunction with CERES, TRMM, and ERA-Interim reanalysis data to investigate horizontal and vertical high cloud structure, top-of-atmosphere (TOA) net cloud forcing and albedo, and dynamics relationships against local SST and precipitation as a function of the mean Tropical West Pacific (TWP; 120°E to 155°W; 30°S–30°N) SST. As the TWP warms, the SST mode (~29.5 °C) is constant, but the area of the mode grows, indicating increased kurtosis of SSTs and decreased SST gradients overall. This is associated with weaker low-level convergence and mid-tropospheric ascent (ω500) over the highest SSTs as the TWP warms, but also a broader area of weak ascent away from the deepest convection, albeit stronger when compared to when the mean TWP is cooler. These associated dynamics changes are collocated with less anvil and thick cloud cover over the highest SSTs and similar thin cold cloud fraction when the TWP is warmer, but broadly more anvil and cirrus clouds over lower local SSTs (SST < 27 °C). For all TWP SST quintiles, anvil cloud fraction, defined as clouds with tops > 9 km and TOA albedos between 0.3–0.6, is closely associated with rain rate, making it an excellent proxy for precipitation; but for a given heavier rain rate, cirrus clouds are more abundant with increasing domain-mean TWP SST. Clouds locally over SSTs between 29–30 °C have a much less negative net cloud forcing, up to 25 W m−2 greater, when the TWP is warm versus cool. When the local rain rate increases, while the net cloud fraction with tops < 9 km decreases, mid-level clouds (4 km < Ztop < 9 km) modestly increase. In contrast, combined low-level and mid-level clouds decrease as the domain-wide SST increases (−10% deg−1). More cirrus clouds for heavily precipitating systems exert a stronger positive TOA effect when the TWP is warmer, and anvil clouds over a higher TWP SST are less reflective and have a weaker cooling effect. For all precipitating systems, total high cloud cover increases modestly with higher TWP SST quintiles, and anvil + cirrus clouds are more expansive, suggesting more detrainment when TWP SSTs are higher. Total-domain anvil cloud fraction scales mostly with domain-mean ω500, but cirrus clouds mostly increase with domain-mean SST, invoking an explanation other than circulation. The overall thinning and greater top-heaviness of clouds over the TWP with warming are possible TWP positive feedbacks not previously identified. Full article
(This article belongs to the Special Issue MISR)
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Open AccessArticle
Super-Resolution Restoration of MISR Images Using the UCL MAGiGAN System
Remote Sens. 2019, 11(1), 52; https://doi.org/10.3390/rs11010052
Received: 26 October 2018 / Revised: 21 December 2018 / Accepted: 21 December 2018 / Published: 29 December 2018
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Abstract
High spatial resolution Earth observation imagery is considered desirable for many scientific and commercial applications. Given repeat multi-angle imagery, an imaging instrument with a specified spatial resolution, we can use image processing and deep learning techniques to enhance the spatial resolution. In this [...] Read more.
High spatial resolution Earth observation imagery is considered desirable for many scientific and commercial applications. Given repeat multi-angle imagery, an imaging instrument with a specified spatial resolution, we can use image processing and deep learning techniques to enhance the spatial resolution. In this paper, we introduce the University College London (UCL) MAGiGAN super-resolution restoration (SRR) system based on multi-angle feature restoration and deep SRR networks. We explore the application of MAGiGAN SRR to a set of 9 MISR red band images (275 m) to produce up to a factor of 3.75 times resolution enhancement. We show SRR results over four different test sites containing different types of image content including urban and rural targets, sea ice and a cloud field. Different image metrics are introduced to assess the overall SRR performance, and these are employed to compare the SRR results with the original MISR input images and higher resolution Landsat images, where available. Significant resolution improvement over various types of image content is demonstrated and the potential of SRR for different scientific application is discussed. Full article
(This article belongs to the Special Issue MISR)
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Open AccessEditor’s ChoiceArticle
Sea Ice Albedo from MISR and MODIS: Production, Validation, and Trend Analysis
Remote Sens. 2019, 11(1), 9; https://doi.org/10.3390/rs11010009
Received: 16 October 2018 / Revised: 13 December 2018 / Accepted: 17 December 2018 / Published: 20 December 2018
Cited by 1 | PDF Full-text (4020 KB) | HTML Full-text | XML Full-text
Abstract
The Multi-angle Imaging SpectroRadiometer (MISR) sensor onboard the Terra satellite provides high accuracy albedo products. MISR deploys nine cameras each at different view angles, which allow a near-simultaneous angular sampling of the surface anisotropy. This is particularly important to measure the near-instantaneous albedo [...] Read more.
The Multi-angle Imaging SpectroRadiometer (MISR) sensor onboard the Terra satellite provides high accuracy albedo products. MISR deploys nine cameras each at different view angles, which allow a near-simultaneous angular sampling of the surface anisotropy. This is particularly important to measure the near-instantaneous albedo of dynamic surface features such as clouds or sea ice. However, MISR’s cloud mask over snow or sea ice is not yet sufficiently robust because MISR’s spectral bands are only located in the visible and the near infrared. To overcome this obstacle, we performed data fusion using a specially processed MISR sea ice albedo product (that was generated at Langley Research Center using Rayleigh correction) combining this with a cloud mask of a sea ice mask product, MOD29, which is derived from the MODerate Resolution Imaging Spectroradiometer (MODIS), which is also, like MISR, onboard the Terra satellite. The accuracy of the MOD29 cloud mask has been assessed as >90% due to the fact that MODIS has a much larger number of spectral bands and covers a much wider range of the solar spectrum. Four daily sea ice products have been created, each with a different averaging time window (24 h, 7 days, 15 days, 31 days). For each time window, the number of samples, mean and standard deviation of MISR cloud-free sea ice albedo is calculated. These products are publicly available on a predefined polar stereographic grid at three spatial resolutions (1 km, 5 km, 25 km). The time span of the generated sea ice albedo covers the months between March and September of each year from 2000 to 2016 inclusive. In addition to data production, an evaluation of the accuracy of sea ice albedo was performed through a comparison with a dataset generated from a tower based albedometer from NOAA/ESRL/GMD/GRAD. This comparison confirms the high accuracy and stability of MISR’s sea ice albedo since its launch in February 2000. We also performed an evaluation of the day-of-year trend of sea ice albedo between 2000 and 2016, which confirm the reduction of sea ice shortwave albedo with an order of 0.4–1%, depending on the day of year and the length of observed time window. Full article
(This article belongs to the Special Issue MISR)
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Open AccessArticle
Differing Responses to Rainfall Suggest More Than One Functional Type of Grassland in South Africa
Remote Sens. 2018, 10(12), 2055; https://doi.org/10.3390/rs10122055
Received: 28 September 2018 / Revised: 6 November 2018 / Accepted: 13 November 2018 / Published: 18 December 2018
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Abstract
Grasslands, which represent around 40% of the terrestrial area, are mostly located in arid and semi-arid zones. Semiarid ecosystems in Africa have been identified as being particularly vulnerable to the impacts of increased human pressure on land, as well as enhanced climate variability. [...] Read more.
Grasslands, which represent around 40% of the terrestrial area, are mostly located in arid and semi-arid zones. Semiarid ecosystems in Africa have been identified as being particularly vulnerable to the impacts of increased human pressure on land, as well as enhanced climate variability. Grasslands are indeed very responsive to variations in precipitation. This study evaluates the sensitivity of the grassland ecosystem to precipitation variability in space and time, by identifying the factors controlling this response, based on monthly precipitation data from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) data from the Multi-angle Imaging SpectroRadiometer-High Resolution (MISR-HR) datasets, used as proxy for productivity, at 60 grassland sites in South Africa. Our results show that MISR-HR products adequately capture the spatial and temporal variability in productivity at scales that are relevant to this study, and they are therefore a good tool to study climate change impacts on ecosystem at small spatial scales over large spatial and temporal domains. We show that combining several determinants and accounting for legacies improves our ability to understand patterns, identify areas of vulnerability, and predict the future of grassland productivity. Mean annual precipitation is a good predictor of mean grassland productivity. The grasslands with a mean annual rainfall above about 530 mm have a different functional response to those receiving less than that amount of rain, on average. On the more arid and less fertile soils, large inter-annual variability reduces productivity. Our study suggests that grasslands on the more marginal soils are the most vulnerable to climate change. Full article
(This article belongs to the Special Issue MISR)
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Open AccessArticle
Inference of an Optimal Ice Particle Model through Latitudinal Analysis of MISR and MODIS Data
Remote Sens. 2018, 10(12), 1981; https://doi.org/10.3390/rs10121981
Received: 4 October 2018 / Revised: 3 December 2018 / Accepted: 5 December 2018 / Published: 7 December 2018
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Abstract
The inference of ice cloud properties from remote sensing data depends on the assumed forward ice particle model, as they are used in the radiative transfer simulations that are part of the retrieval process. The Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (MC6) [...] Read more.
The inference of ice cloud properties from remote sensing data depends on the assumed forward ice particle model, as they are used in the radiative transfer simulations that are part of the retrieval process. The Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (MC6) ice cloud property retrievals are produced in conjunction with a single-habit ice particle model with a fixed degree of ice particle surface roughness (the MC6 model). In this study, we examine the MC6 model and five other ice models with either smoother or rougher surface textures to determine an optimal model to reproduce the angular variation of the radiation field sampled by the Multi-angle Imaging Spectroradiometer (MISR) as a function of latitude. The spherical albedo difference (SAD) method is used to infer an optimal ice particle model. The method is applied to collocated MISR and MODIS data over ocean for clouds with temperatures ≤233 K during December solstice from 2012–2015. The range of solar zenith angles covered by the MISR cameras is broader at the solstices than at other times of the year, with fewer scattering angles associated with sun glint during the December solstice than the June solstice. The results suggest a latitudinal dependence in an optimal ice particle model, and an additional dependence on the solar zenith angle (SZA) at the time of the observations. The MC6 model is one of the most optimal models on the global scale. In further analysis, the results are filtered by a cloud heterogeneity index to investigate cloudy scenarios that are less susceptible to potential 3D effects. Compared to results for global data, the consistency between measurements and a given model can be distinguished in both the tropics and extra-tropics. The SAD analysis suggests that the optimal model for thick homogeneous clouds corresponds to more roughened ice particles in the tropics than in the extra-tropics. While the MC6 model is one of the models most consistent with the global data, it may not be the most optimal model for the tropics. Full article
(This article belongs to the Special Issue MISR)
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Open AccessArticle
MISR-GOES 3D Winds: Implications for Future LEO-GEO and LEO-LEO Winds
Remote Sens. 2018, 10(12), 1885; https://doi.org/10.3390/rs10121885
Received: 4 October 2018 / Revised: 19 November 2018 / Accepted: 21 November 2018 / Published: 27 November 2018
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Abstract
Global wind observations are fundamental for studying weather and climate dynamics and for operational forecasting. Most wind measurements come from atmospheric motion vectors (AMVs) by tracking the displacement of cloud or water vapor features. These AMVs generally rely on thermal infrared (IR) techniques [...] Read more.
Global wind observations are fundamental for studying weather and climate dynamics and for operational forecasting. Most wind measurements come from atmospheric motion vectors (AMVs) by tracking the displacement of cloud or water vapor features. These AMVs generally rely on thermal infrared (IR) techniques for their height assignments, which are subject to large uncertainties in the presence of weak or reversed vertical temperature gradients near the planetary boundary layer (PBL) and tropopause folds. Stereo imaging can overcome the height assignment problem using geometric parallax for feature height determination. In this study we develop a stereo 3D-Wind algorithm to simultaneously retrieve AMV and height from geostationary (GEO) and low Earth orbit (LEO) satellite imagery and apply it to collocated Geostationary Operational Environmental Satellite (GOES) and Multi-angle Imaging SpectroRadiometer (MISR) imagery. The new algorithm improves AMV and height relative to products from GOES or MISR alone, with an estimated accuracy of <0.5 m/s in AMV and <200 m in height with 2.2 km sampling. The algorithm can be generalized to other LEO-GEO or LEO-LEO combinations for greater spatiotemporal coverage. The technique demonstrated with MISR and GOES has important implications for future high-quality AMV observations, for which a low-cost constellation of CubeSats can play a vital role. Full article
(This article belongs to the Special Issue MISR)
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Open AccessArticle
Instantaneous Top-of-Atmosphere Albedo Comparison between CERES and MISR over the Arctic
Remote Sens. 2018, 10(12), 1882; https://doi.org/10.3390/rs10121882
Received: 9 October 2018 / Revised: 15 November 2018 / Accepted: 21 November 2018 / Published: 25 November 2018
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Abstract
The top-of-atmosphere (TOA) albedo is one of the key parameters in determining the Arctic radiation budget, with continued validation of its retrieval accuracy still required. Based on three years (2007, 2015, 2016) of summertime (May–September) observations from the Clouds and the Earth’s Radiant [...] Read more.
The top-of-atmosphere (TOA) albedo is one of the key parameters in determining the Arctic radiation budget, with continued validation of its retrieval accuracy still required. Based on three years (2007, 2015, 2016) of summertime (May–September) observations from the Clouds and the Earth’s Radiant Energy System (CERES) and the Multi-angle Imaging SpectroRadiometer (MISR), collocated instantaneous albedos for overcast ocean and snow/ice scenes were compared within the Arctic. For samples where both instruments classified the scene as overcast, the relative root-mean-square (RMS) difference between the sample albedos grew as the solar zenith angle (SZA) increased. The RMS differences that were purely due to differential Bidirectional Reflectance Factor (BRF) anisotropic corrections ( σ A D M ) were estimated to be less than 4% for overcast ocean and overcast snow/ice when the SZA ≤ 70°. The significant agreement between the CERES and MISR strongly increased our confidence in using the instruments overcast cloud albedos in Arctic studies. Nevertheless, there was less agreement in the cloud albedos for larger solar zenith angles, where the RMS differences of σ A D M reached 13.5% for overcast ocean scenes when the SZA > 80°. Additionally, inconsistencies between the CERES and MISR scene identifications were examined, resulting in an overall recommendation for improvements to the MISR snow/ice mask and a rework of the MISR Albedo Cloud Designation (ACD) field by incorporating known strengths of the standard MISR cloud masks. Full article
(This article belongs to the Special Issue MISR)
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Open AccessArticle
MISR Radiance Anomalies Induced by Stratospheric Volcanic Aerosols
Remote Sens. 2018, 10(12), 1875; https://doi.org/10.3390/rs10121875
Received: 7 October 2018 / Revised: 15 November 2018 / Accepted: 20 November 2018 / Published: 23 November 2018
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Abstract
The 16-year MISR monthly radiances are analyzed in this study, showing significant enhancements of anisotropic scattering at high latitudes after several major volcanic eruptions with injection heights greater than 14 km. The anomaly of deseasonalized radiance anisotropy between MISR’s DF and DA views [...] Read more.
The 16-year MISR monthly radiances are analyzed in this study, showing significant enhancements of anisotropic scattering at high latitudes after several major volcanic eruptions with injection heights greater than 14 km. The anomaly of deseasonalized radiance anisotropy between MISR’s DF and DA views (70.5° forward and aft) is largest in the blue band with amplitudes amounting to 5–15% of the mean radiance. The anomalous radiance anisotropy is a manifestation of the stronger forward scattering of reflected sunlight due to the direct and indirect effects of stratospheric volcanic aerosols (SVAs). The perturbations of MISR radiance anisotropy from the Kasatochi (August 2008), Sarychev (June 2009), Nabro (June 2011) and Calbuco (April 2015) eruptions are consistent with the poleward transported SVAs observed by CALIOP and OMPS-LP. In a particular scene over the Arctic Ocean, the stratospheric aerosol mid-visible optical depth can reach as high as 0.2–0.5. The enhanced global forward scattering by SVAs has important implications for the shortwave radiation budget. Full article
(This article belongs to the Special Issue MISR)
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Open AccessArticle
Three-Dimensional Cloud Volume Reconstruction from the Multi-angle Imaging SpectroRadiometer
Remote Sens. 2018, 10(11), 1858; https://doi.org/10.3390/rs10111858
Received: 21 September 2018 / Revised: 14 November 2018 / Accepted: 16 November 2018 / Published: 21 November 2018
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Abstract
Characterizing 3-D structure of clouds is needed for a more complete understanding of the Earth’s radiative and latent heat fluxes. Here we develop and explore a ray casting algorithm applied to data from the Multi-angle Imaging SpectroRadiometer (MISR) onboard the Terra satellite, in [...] Read more.
Characterizing 3-D structure of clouds is needed for a more complete understanding of the Earth’s radiative and latent heat fluxes. Here we develop and explore a ray casting algorithm applied to data from the Multi-angle Imaging SpectroRadiometer (MISR) onboard the Terra satellite, in order to reconstruct 3-D cloud volumes of observed clouds. The ray casting algorithm is first applied to geometrically simple synthetic clouds to show that, under the assumption of perfect, clear-conservative cloud masks, the reconstruction method yields overestimation in the volume whose magnitude depends on the cloud geometry and the resolution of the reconstruction grid relative to the image pixel resolution. The method is then applied to two hand-picked MISR scenes, fully accounting for MISR’s viewing geometry for reconstructions over the Earth’s ellipsoidal surface. The MISR Radiometric Camera-by-camera Cloud Mask (RCCM) at 1.1-km resolution and the custom cloud mask at 275-m resolution independently derived from MISR’s red, green, and blue channels are used as input cloud masks. A wind correction method, termed cloud spreading, is applied to the cloud masks to offset potential cloud movements over short time intervals between the camera views of a scene. The MISR cloud-top height product is used as a constraint to reduce the overestimation at the cloud top. The results for the two selected scenes show that the wind correction using the cloud spreading method increases the reconstructed volume up to 4.7 times greater than without the wind correction, and that the reconstructed volume generated from the RCCM is up to 3.5 times greater than that from the higher-resolution custom cloud mask. Recommendations for improving the presented cloud volume reconstructions, as well as possible future passive remote sensing satellite missions, are discussed. Full article
(This article belongs to the Special Issue MISR)
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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 3 | 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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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
Cited by 1 | PDF Full-text (4792 KB) | HTML Full-text | XML Full-text
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
Cited by 3 | PDF Full-text (7105 KB) | HTML Full-text | XML Full-text
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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Open AccessTechnical Note
Arctic Sea Ice Surface Roughness Estimated from Multi-Angular Reflectance Satellite Imagery
Remote Sens. 2019, 11(1), 50; https://doi.org/10.3390/rs11010050
Received: 21 October 2018 / Revised: 12 December 2018 / Accepted: 14 December 2018 / Published: 29 December 2018
Cited by 2 | PDF Full-text (4303 KB) | HTML Full-text | XML Full-text
Abstract
Sea ice surface roughness affects ice-atmosphere interactions, serves as an indicator of ice age, shows patterns of ice convergence and divergence, affects the spatial extent of summer meltponds, and affects ice albedo. We have developed a method for mapping sea ice surface roughness [...] Read more.
Sea ice surface roughness affects ice-atmosphere interactions, serves as an indicator of ice age, shows patterns of ice convergence and divergence, affects the spatial extent of summer meltponds, and affects ice albedo. We have developed a method for mapping sea ice surface roughness using angular reflectance data from the Multi-angle Imaging SpectroRadiometer (MISR) and lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). Using an empirical data modeling approach, we derived estimates of Arctic sea ice roughness ranging from centimeters to decimeters within the MISR 275-m pixel size. Using independent ATM data for validation, we find that histograms of lidar and multi-angular roughness values were nearly identical for areas with a roughness < 20 cm, but for rougher regions, the MISR-estimated roughness had a narrower range of values than the ATM data. The algorithm was able to accurately identify areas that transition between smooth and rough ice. Because of its coarser spatial scale, MISR-estimated roughness data have a variance about half that of ATM roughness data. Full article
(This article belongs to the Special Issue MISR)
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Open AccessLetter
ENSO and Teleconnections Observed Using MISR Cloud Height Anomalies
Remote Sens. 2019, 11(1), 32; https://doi.org/10.3390/rs11010032
Received: 22 September 2018 / Revised: 21 December 2018 / Accepted: 21 December 2018 / Published: 26 December 2018
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Abstract
Cloud-top height is an important climate variable due to its greenhouse effect, as well as being a useful indicator of circulation patterns. We use effective height anomalies from stereo retrievals at a horizontal resolution of 1.1 km after subtracting regional and seasonal mean [...] Read more.
Cloud-top height is an important climate variable due to its greenhouse effect, as well as being a useful indicator of circulation patterns. We use effective height anomalies from stereo retrievals at a horizontal resolution of 1.1 km after subtracting regional and seasonal mean values. After 18 years, any trend in the global average height anomaly remains hidden by the stronger influence of intermittent El Niño–Southern Oscillation (ENSO) events. However, interesting teleconnections and oscillatory patterns in regional cloud heights are starting to emerge. Different teleconnection patterns are now evident during the El Niño and La Niña phases giving rise to high values of the correlation coefficient between many global regions and the Central Pacific, which shows the greatest connection to ENSO. Cloud heights over the Central Pacific and Maritime Continent oscillate out of phase with each other and have nearly synchronous zero anomalies with a mean separation of about 1.8 years. These are lagged by one month from similar zero values in the Southern Oscillation Index. Surface zonal wind anomalies for these two regions also oscillate out of phase with each other, and are highly correlated with the height anomalies, leading them by one month. Full article
(This article belongs to the Special Issue MISR)
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