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We are developing a pixel-level cloud-type classifier for the Multi-angle Imaging SpectroRadiometer (MISR), an instrument used to study clouds and aerosols from NASA's Terra satellite.
A recent paper by Mishchenko et al. compares near-coincident MISR, MODIS, and AERONET aerosol optical depth (AOD) products, and reports much poorer agreement than that obtained by the instrument teams and others. We trace the reasons for the discrepancies primarily to differences in (1) the treatment of outliers, (2) the application of absolute vs. relative criteria for testing agreement, and (3) the ways in which seasonally varying spatial distributions of coincident retrievals are taken into account.
The NASA Earth Observing System's Multi-angle Imaging SpectroRadiometer (MISR) has been imaging the planet about once per week for nearly 12 years. These data contain a wealth of information about the current state, as well as the variability of Earth's surface and atmosphere. For airborne particles specifically, horizontal distributions of aerosol optical depth, aerosol type, and near-source aerosol plume height are now routinely derived. The results are being applied to a wide range of problems, from constraining aerosol direct radiative forcing of climate and assessing its seasonal and secular trends, to material transports of smoke and dust, to mapping volcanic ash plumes and near-surface aerosol pollution. The value of these data is multiplied many times by combining it with data from other sources, and using the aggregate to constrain models. This talk will summarize the latest developments, and will also discuss current work aimed at making further advancements in these areas.
The Beaufort and East Siberian Sea (BESS) shows a large increase in surface air temperature (SAT) in the recent decade for months of Sep-Nov, and NASA's Terra satellite have provided valuable measurements for this important decade of the intensified Arctic warming. In particular, MISR data since 2000 and CALIPSO cloud measurements since 2006 reveal a significant increase of low cloud cover in October, which is largest in the daylight Arctic months (March-October). Causes of the warming remain unclear; but increased absorption of summer solar radiation and autumn low cloud formation have been suggested as a positive ice-temperature-cloud feedback in the Arctic. The observed increase of low cloud cover supports the theorized positive ice-temperature-cloud feedback, whereby more open water in the Arctic Ocean increases summer absorption of solar radiation, and subsequent evaporation, which leads to more low clouds in autumn. Trapping longwave radiation, these clouds effectively lengthen the melt season and reduce perennial ice pack formation, making sea ice more vulnerable to the next melt season
The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the five Earth-observing instruments on-board the National Aeronautics and Space Administration (NASA) Earth-Observing System(EOS) Terra spacecraft, launched in December 1999. It has 36 spectral bands with wavelengths ranging from 0.41 to 14.4 mm and collects data at three nadir spatial resolutions: 0.25 km for 2 bands with 40 detectors each, 0.5 km for 5 bands with 20 detectors each and 1 km for the remaining 29 bands with 10 detectors each. MODIS bands are located on four separate focal plane assemblies (FPAs) according to their spectral wavelengths and aligned in the cross-track direction. Detectors of each spectral band are aligned in the along-track direction. MODIS makes observations using a two-sided paddle-wheel scan mirror. Its on-board calibrators (OBCs) for the reflective solar bands (RSBs) include a solar diffuser (SD), a solar diffuser stability monitor (SDSM) and a spectral-radiometric calibration assembly (SRCA). Calibration is performed for each band, detector, sub-sample (for sub-kilometer resolution bands) and mirror side. In this study, a ratio approach is applied to MODIS observed Earth scene reflectances to track the detector-to-detector and mirror side differences. Simultaneous observed reflectances from the Multi-angle Imaging Spectroradiometer (MISR), also onboard the Terra spacecraft, are used with MODIS observed reflectances in this ratio approach for four closely matched spectral bands. Results show that the detector-to-detector difference between two adjacent detectors within each spectral band is typically less than 0.2% and, depending on the wavelengths, the maximum difference among all detectors varies from 0.5% to 0.8%. The mirror side differences are found to be very small for all bands except for band 3 at 0.44 mm. This is the band with the shortest wavelength among the selected matching bands, showing a time-dependent increase for the mirror side difference. This study is part of the effort by the MODIS Characterization Support Team (MCST) in order to track the RSB on-orbit performance for MODIS collection 5 data products. To support MCST efforts for future data re-processing, this analysis will be extended to include more spectral bands and temporal coverage.
MODIS is a wide field-of-view sensor providing daily global observations of the Earth. Currently, global MODIS aerosol retrievals over land are performed with the main Dark Target algorithm complimented with the Deep Blue (DB) Algorithm over bright deserts. The Dark Target algorithm relies on surface parameterization which relates reflectance in MODIS visible bands with the 2.1 micrometer region, whereas the Deep Blue algorithm uses an ancillary angular distribution model of surface reflectance developed from the time series of clear-sky MODIS observations. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has been developed for MODIS. MAIAC uses a time series and an image based processing to perform simultaneous retrievals of aerosol properties and surface bidirectional reflectance. It is a generic algorithm which works over both dark vegetative surfaces and bright deserts and performs retrievals at 1 km resolution. In this work, we will provide a comparative analysis of DB, MAIAC, MISR and OMI aerosol products over bright deserts of northern Africa.
Aerosol plume height is a key parameter to determine impacts of particulate matters generated from biomass burning, wind-blowing dust, and volcano eruption. Retrieving cloud top height from stereo imageries from two GOES (Geostationary Operational Environmental Satellites) have been demonstrated since 1970's and the principle should work for aerosol plumes if they are optically thick. The stereo technique has also been used by MISR (Multiangle Imaging SpectroRadiometer) since 2000 that has nine look angles along track to provide aerosol height measurements. Knowing the height of volcano aerosol layers is as important as tracking the ash plume flow for aviation safety. Lack of knowledge about ash plume height during the 2010 Eyja'rjallajokull eruption resulted in the largest air-traffic shutdown in Europe since World War II. We will discuss potential applications of Asian GEO satellites to make stereo measurements for dust and volcano plumes.
This paper presents the above three processing steps starting from an accurate and efficient project of multi-angle MISR image data to the ellipsoid surface, followed by a mathematical derivation which separates the cloud motion and height, and finally an automatic image matching and ray intersection algorithm for high resolution cloud top height retrieval.
Each of the nine cameras which compose the Multi-angle Imaging SpectoRadiometer (MISR) has been regorously tested, characterized, and Calibrated.
The theoretical concept, based on modern photogrammetric methods, underlying the design of the MISR science data processing system responsible for the autonomous and continuous georectification of multi-angle imagery is the subject of this paper.
Multi-angle Imaging SpectroRadiometer observations facilitate new atmospheric remote sensing methods. Angular variation of radiometric and geometric scene attributes, in conjunction with new algorithms, enables retrieval of aerosol properties, cloud heights, and cloud-traced winds.
The TOA/Cloud Albedo data contain albedo values, including finely-sampled or local (2.2 km) TOA albedos registered to the RLRA, and two coarsely-sampled (35.2 km resolution) TOA albedos projected to 30-km altitude. The local (2.2 km) albedos do not take the obscuration of cloud features into account, so they should only be treated as traditional albedos when the number of obscured pixels is low. The restrictive and expansive albedos are both available at 35.2 km resolution: the restrictive albedos are only calculated using the radiation upwelling from the pixel under consideration, whereas the expansive albedos use all the radiation emanating from the surrounding area. Therefore, the expansive albedo is closer to the traditional definition of top-of-atmosphere albedos. [Location=GLOBAL] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=35.2 km; Longitude_Resolution=35.2 km; Horizontal_Resolution_Range=10 km - < 50 km or approximately .09 degree - < .5 degree; Temporal_Resolution=about 15 orbits/day].
The Land Surface data include bihemispherical and directional-hemispherical reflectances (albedo), hemispherical directional and bidirectional reflectance factors (BRF), BRF model parameters, leaf-area index (LAI), fraction of photosynthetically active radiation (FPAR), and normalized difference vegetation index (NDVI) on a 1.1 km grid. The land surface data include hemispherical directional reflectance factor, bihemispherical reflectance (i.e., albedo), bidirectional reflectance factor, directional hemispherical reflectance, BRF model parameters, FPAR, and terrain-referenced view and illumination angles. [Location=GLOBAL LAND] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=1.1 km - 17.6 km; Longitude_Resolution=1.1 km - 17.6 km; Horizontal_Resolution_Range=1 km - < 10 km or approximately .01 degree - < .09 degree; Temporal_Resolution=about 15 orbits/day].
The TOA/Cloud Classifiers contain the Angular Signature Cloud Mask (ASCM), a scene classifier calculated using support vector machine technology (SVM) both of which are on a 1.1 km grid, and cloud fractions at 17.6 km resolution that are available in different height bins (low, middle, high) and are also calculated on an angle-by-angle basis. [Location=GLOBAL] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=17.6 km; Longitude_Resolution=17.6 km; Horizontal_Resolution_Range=10 km - < 50 km or approximately .09 degree - < .5 degree; Temporal_Resolution=about 15 orbits/day; Temporal_Resolution_Range=Daily - < Weekly, Daily - < Weekly].
The Land Surface data include bihemispherical and directional-hemispherical reflectances (albedo), hemispherical directional and bidirectional reflectance factors (BRF), BRF model parameters, leaf-area index (LAI), fraction of photosynthetically active radiation (FPAR), and normalized difference vegetation index (NDVI) on a 1.1 km grid. The land surface data include hemispherical directional reflectance factor, bihemispherical reflectance (i.e., albedo), bidirectional reflectance factor, directional hemispherical reflectance, BRF model parameters, FPAR, and terrain-referenced view and illumination angles. [Location=GLOBAL LAND] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=1.1 km - 17.6 km; Longitude_Resolution=1.1 km - 17.6 km; Horizontal_Resolution_Range=1 km - < 10 km or approximately .01 degree - < .09 degree; Temporal_Resolution=about 15 orbits/day; Temporal_Resolution_Range=Daily - < Weekly, Daily - < Weekly].
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