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At least 199 records · Page 11

Deep Learning System for Efficient Processing of Geostationary Satellite Imagery

Improved capabilities of Earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), and Himawari-8/9 (JAXA), we present an interchangeable set of machine models to perform spectral adjustment among sensors, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools on the NASA Earth eXchange (NEX) to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate surface reflectance, surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Thomas Vandal↗

Development of a Consistent MODIS and VIIRS Cloud Detection Approach for CERES

A consistent cloud fraction record across various satellite platforms is essential for maintaining a long-term and stable climate data record of Earth's energy budget. With the Aqua satellite nearing the end of its operational lifetime, the continuation of this record relies on utilizing VIIRS observations from NOAA20 for cloud detection in NASA’s Clouds and Earth’s Radiant Energy System (CERES) project. However, integrating data from VIIRS and MODIS instruments poses challenges due to their distinct characteristics, such as varying spatial resolutions and different spectral channels. As a result, deriving consistent cloud properties from these two sensors without introducing artificial discontinuities in the time series remains a complex and challenging task. This paper will present progress toward developing a unified MODIS and VIIRS cloud mask using common channels to produce consistent cloud properties for CERES next edition (Ed5) Earth radiation budget data products. The fundamental approach taken in the CERES cloud mask is to compare the observed radiances to the expected background clear sky radiances. Therefore, one vital step is to compute clear sky radiances with a radiative transfer model that accurately accounts for satellite-specific, spectrally dependent surface reflectance, surface emission, and atmospheric absorption. Refined radiative transfer models and updated ancillary data inputs including surface emissivity maps, IGBP, snow and ice maps are incorporated into the processing framework to improve cloud detection consistency and accuracy. Pixel level cloud mask results and monthly global cloud fraction comparisons between MODIS and VIIRS will be presented to evaluate their consistency. Remaining challenges will be discussed. It is expected that this work will contribute consistent cloud properties for CERES that adequately bridges the MODIS and VIIRS imager data records.

CERES↗

The role of specular reflectance in surface anisotropy

For smooth surfaces, specular reflection constitutes a major source of anisotropy in the surface bidirectional reflectance and introduces a strong illumination angle dependence in diurnal albedo. A prior formulation has been employed by the authors to account for the surface anisotropy of other natural surfaces. It is shown that this formulation is appropriate to successfully explain the specular characteristics of other rough surfaces as well as that of the ocean. The angular dependence of the diffuse component of the reflected radiance is explained using the Ahmad-Deering formulation, which is based on Chandrasekhar's radiative transfer solution. Results for a smooth silt-playa lake bed in a volcanic region of Nevada are presented. It is shown that this formulation is capable of explaining the specular features measured in the field and can be effectively used in any surface bidirectional reflectance model.

Ahmad, Suraiya P.↗

On the reflectivity of complex mesh surfaces

Poorer than expected surface reflectivity was observed in an early Tracking and Data Relay Satellite System antenna utilizing a tricot mesh weave. This poor reflectivity was determined to be caused by inadequate electrical contact at wire crossover points. A proper mathematical and numerical approach to assess the impact of wire junctions on reflectivity performance is developed. A mathematical method is presented for computing the surface reflectivity of complex mesh configurations like those on unfurlable-type spacecraft antennas. The method is based on the Floquet mode expansion to establish an integral equation for mesh wire currents. The equation is solved using the method of moments with triangular basis functions. It is observed that it is necessary to give special attention to the junction treatment among different branches of the mesh configurations. A vector junction current approach that resulted in satisfactory solutions for the current is described. The results of numerical simulations are compared against measured data and excellent agreement is observed.

Imbriale, William A.↗

Enhanced Deep Blue Aerosol Retrieval Algorithm: The Second Generation

The aerosol products retrieved using the MODIS collection 5.1 Deep Blue algorithm have provided useful information about aerosol properties over bright-reflecting land surfaces, such as desert, semi-arid, and urban regions. However, many components of the C5.1 retrieval algorithm needed to be improved; for example, the use of a static surface database to estimate surface reflectances. This is particularly important over regions of mixed vegetated and non- vegetated surfaces, which may undergo strong seasonal changes in land cover. In order to address this issue, we develop a hybrid approach, which takes advantage of the combination of pre-calculated surface reflectance database and normalized difference vegetation index in determining the surface reflectance for aerosol retrievals. As a result, the spatial coverage of aerosol data generated by the enhanced Deep Blue algorithm has been extended from the arid and semi-arid regions to the entire land areas.

Deep Blue↗

Compensation for the Atmosphere in Radiance Measured by the Airborne Visible/Infrared Imaging Spectrometer and Applications to an Advanced Land Remote Sensing System

The Airborne Visible/Infrared Imaging Spectrometer measures spatial images of the total upwelling spectral radiance from 400 to 2500 nm through 10 nm spectral channels. Quantitative research and application objectives for surface investigations require conversion of the measured radiance to surface reflectance or surface leaving radiance. To calculate apparent surface reflectance an estimation of atmospheric water vapor abundance, cirrus cloud effects, surface pressure elevation and aerosol optical depth is also required. Algorithms for the estimation of these parameters from the AVIRIS data themselves are described. Based upon these determined atmospheric parameters we show an example of the calculation of apparent surface reflectance from the AVIRIS-measured radiance using a radiative transfer code.

Airborne Visible/Infrared Imaging Spectrometer AVI↗

Reflected fluxes for broken clouds over a Lambertian surface

Reflected fluxes are calculated for broken cloudiness (i.e., nonplane parallel) as a function of cloud cover, cloud optical depth, solar zenith angle and surface albedo. These calculations extend previous results for broken cloud reflected fluxes over a black surface. The present study demonstrates that not only radiances but also radiative fluxes over high albedo surfaces may be decreased by the presence of broken cloudiness. Conventional wisdom states that cloud radiances (brightnesses) are always greater than the background. While most cloud retrieval schemes are built around this assumption, it is incorrect for clouds over high albedo surfaces such as found in polar regions. However, the most startling and counterintuitive conclusion of this study is that nonabsorbing finite clouds over a highly reflecting surface will decrease the system albedo. As a result, surface absorption is increased, the result of multiple scattering between surface and cloud layer, controlled by cloud morphology and cloud optical thickness. A simple parameterization of the effects of cloud contamination upon retrieved albedo is given in terms of solar zenith angle, cloud optical depth, surface albedo, cloud cover, and plane-parallel cloud albedo. In this way, the effects of broken cloudiness are modeled in terms of easily computed plane-parallel values.

Welch, Ronald M.↗

BOREAS Aerosol Optical Depth Measurements and Atmospheric Correction

Aerosol optical depth measurements were conducted during all three Intensive Field Campaigns of BOREAS in 1994 by both groundbased and airborne tracking sun photometers. These measurements documented the highly variable aerosol loadings in the BOREAS study areas: very clear days with optical depths at 525 nm of 0.05 alternated with very hazy days (due to smoke from extensive western forest fires) with optical depths greater than 0.5. The airborne sun photometer showed the aerosol layer extended up to 3.5-4 km. Remote sensing missions were largely constrained to clear days, but some occurred under less optimum conditions. The spectral aerosol optical depths were used to derive aerosol size distributions and other aerosol optical properties useful for atmospheric correction of satellite and aircraft remote sensing imagery. Aerosol scattering phase functions and aerosol single scattering albedoes were calculated from Mie scattering theory using reasonable assumptions for the index of refraction for the aersols. Our atmospheric correction procedure Imagecor used the aerosol optical properties to derive surface radiances at each pixel from Landsat Thematic Mapper data of the southern study area on July 25, 1994. Similar efforts under FIFE showed close agreement between atmospherically corrected surface radiances and helicopter measurements of surface radiance of the same sites. Recently we incorporated calculations of the total downwelling irradiance from the 6S radiative transfer program (which vary little over the entire Landsat scene) to derive surface reflectances from the atmospherically corrected surface radiances. Tests using this procedure with both FIFE and HAPEX data sets show good agreement with ground-measured surface reflectances. Surface reflectances derived from the atmospherically corrected radiances of the July 25th Landsat scene will be compared to surface measurements made in BOREAS'southern study area.

Wrigley, R. C.↗

LITE Measurements of Sea Surface Directional Reflectance

The dependence of sea surface directional reflectance on surface wind speed from space-based lidar measurements of sea surface backscatter. In particular lidar measurements in the nidir angle range from 10-30 degrees appear to be most sensitive to surface wind speed variability in the regime below 10m/s.

Sea Surface Wind Speed↗

A note on solar elevation dependence of clear sky snow albedo

Recent attempts to match shortwave albedo of snow for clear skies using approximate spectral solar fluxes and solutions of the radiative transfer equation for snow were unsuccessful until a separate surface reflection term was introduced. A separate consideration of specular reflection from surface snow grains has been objected to as being ad hoc. Results based on a new parameterization of shortwave radiation are discussed. Compared to the previous radiation models, new model gives higher diffuse insolation and predicts higher albedos. The difference between observed and predicted albedos is substantially reduced without invoking surface reflection.

Choudhury, B. J.↗

GEONEX: Challenges in Producing MODIS-Like Land Products from a New Generation of Geostationary Sensors

The new generation geostationary (GEO) remote sensors (GOES-R ABI, Himawari AHI, and FY4 AGRI) provide high frequency (5-15 minute) observations spatially/spectrally similar to MODIS/VIIRS for land monitoring. These new features of GEO satellite sensors make producing MODIS like land products for terrestrial monitoring possible. The NASA Earth Exchange (NEX) team developed the GEONEX pipeline that is containerized, deployable on NASA Pleiades supercomputer as well as public cloud platforms (e.g. AWS). The processing pipeline is designed to take Himawari Standard Data (HSD) and GOES-16 L1b to generate surface reflectance (SR) and other high-level land remote sensing products. In order to produce low-Earth-orbiting (LEO) remote sensing compatible land products, inter-comparison between Himawari AHI and MODIS Terra/Aqua has been conducted in this research work. Comparisons of TOA reflectance and surface reflectance between AHI and Terra/Aqua are presented. Ray-Matching method was used to locate the co-located pixels, where GEO and LEO sensors look at the land target with similar Viewing Zenith Angle (VZA) and Viewing Azimuth Angle (VAA) simultaneously. Here, we address challenges associated with the selection of qualified pixels of similar solar illumination condition and atmosphere path. We used strict criterion to constrain the pixel selection: the time difference between GEO and LEO observations is less than +-2.5 mins, the cosine of VZA difference is less than 1%, and the VAA difference is less than 10 deg. We also discuss the strong radiometric consistency that the new generation GEO sensors along with the popular LEO sensors would benefit the environmental remote sensing community.

Li, Shuang↗

Retrieval of Surface Directional Reflectance Properties Using Ground Level Multiangle Measurements

Knowledge of the directional reflectance properties of natural surfaces such as soils and vegetation canopies is essential for classification studies and canopy model inversion. Atmospheric correction schemes, using various levels of approximation, are described to retrieve surface bidirectional reflectance factors (BRFs) and directional-hemispherical reftectances (albedos)from multiangle radiance measurements taken at ground level. The retrieval schemes are tested on simulated data incorporating realistic surface BRFs and atmospheric models containing aerosols. Sensitivity of the atmospherically corrected BRFs and associated directional-hemispherical reflectances to various aerosol properties and the sun-view geometry is illustrated. A measurement strategy for obtaining highly accurate surface reflectance properties also is examined in the context of instrument radiametric calibration, knowledge of the atmospheric properties, and sun-view angular coverage.

Martonchik, John V.↗

Surface Polarized Reflectance Analysis For Aerosol Remote Sensing

We study the Earth surface polarized reflectance using data collected by a space-based lidar. Accurate modelling of the surface reflectance supports retrieval algorithm development for the current and future Earth Science missions. Strong polarization of the laser light from Cloud-Aerosol Transport System (CATS) instrument, operated in 2015-2017, and nighttime measurements yield higher signal-to-noise ratio for polarization compared to the previous analysis of reflected, initially unpolarized, solar light.

Earth surface polarized reflectance↗