Improved Cloud and Snow Screening in MAIAC Aerosol Retrievals Using Spectral and Spatial Analysis

An improved cloud/snow screening technique in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is described. It is implemented as part of MAIAC aerosol retrievals based on analysis of spectral residuals and spatial variability. Comparisons with AERONET aerosol observations and a large-scale MODIS data analysis show strong suppression of aerosol optical thickness outliers due to unresolved clouds and snow. At the same time, the developed filter does not reduce the aerosol retrieval capability at high 1 km resolution in strongly inhomogeneous environments, such as near centers of the active fires. Despite significant improvement, the optical depth outliers in high spatial resolution data are and will remain the problem to be addressed by the application-dependent specialized filtering techniques

Discrimination of Biomass Burning Smoke and Clouds in MAIAC Algorithm

The multi-angle implementation of atmospheric correction (MAIAC) algorithm makes aerosol retrievals from MODIS data at 1 km resolution providing information about the fine scale aerosol variability. This information is required in different applications such as urban air quality analysis, aerosol source identification etc. The quality of high resolution aerosol data is directly linked to the quality of cloud mask, in particular detection of small (sub-pixel) and low clouds. This work continues research in this direction, describing a technique to detect small clouds and introducing the smoke test to discriminate the biomass burning smoke from the clouds. The smoke test relies on a relative increase of aerosol absorption at MODIS wavelength 0.412 micrometers as compared to 0.47-0.67 micrometers due to multiple scattering and enhanced absorption by organic carbon released during combustion. This general principle has been successfully used in the OMI detection of absorbing aerosols based on UV measurements. This paper provides the algorithm detail and illustrates its performance on two examples of wildfires in US Pacific North-West and in Georgia/Florida of 2007

Corrigendum to "Discrimination of biomass burning smoke and clouds in MAIAC algorithm" published in Atmos. Chem. Phys., 12, 9679–9686, 2012

No abstract available

Assessing Snow Extent Data Sets over North America to Inform and Improve Trace Gas Retrievals from Solar Backscatter

Accurate representation of surface reflectivity is essential to tropospheric trace gas retrievals from solar backscatter observations. Surface snow cover presents a significant challenge due to its variability and thus snow-covered scenes are often omitted from retrieval data sets; however, the high reflectance of snow is potentially advantageous for trace gas retrievals. We first examine the implications of surface snow on retrievals from the upcoming TEMPO geostationary instrument for North America. We use a radiative transfer model to examine how an increase in surface reflectivity due to snow cover changes the sensitivity of satellite retrievals to NO2 in the lower troposphere. We find that a substantial fraction (>50%) of the TEMPO field of regard can be snow covered in January, and that the average sensitivity to the tropospheric NO2 column substantially increases (doubles) when the surface is snow covered. We then evaluate seven existing satellite-derived or reanalysis snow extent products against ground station observations over North America to assess their capability of informing surface conditions for TEMPO retrievals. The Interactive Multisensor Snow and Ice Mapping System (IMS) had the best agreement with ground observations (accuracy of 93%, precision of 87%, recall of 83%). Multiangle Implementation of Atmospheric Correction (MAIAC) retrievals of MODIS-observed radiances had high precision (90% for Aqua and Terra), but underestimated the presence of snow (recall of 74% for Aqua, 75% for Terra). MAIAC generally outperforms the standard MODIS products (precision of 51%, recall of 43% for Aqua; precision of 69%, recall of 45% for Terra). The Near-real-time Ice and Snow Extent (NISE) product had good precision (83%) but missed a significant number of snow-covered pixels (recall of 45%). The Canadian Meteorological Centre (CMC) Daily Snow Depth Analysis Data set had strong performance metrics (accuracy of 91%, precision of 79%, recall of 82%). We use the F score, which balances precision and recall, to determine overall product performance (F = 85%, 82(82)%, 81%, 58%, 46(54)% for IMS, MAIAC Aqua(Terra), CMC, NISE, MODIS Aqua(Terra) respectively) for providing snow cover information for TEMPO retrievals from solar backscatter observations. We find that using IMS to identify snow cover and enable inclusion of snow-covered scenes in clear-sky conditions across North America in January can increase both the number of observations by a factor of 2.1 and the average sensitivity to the tropospheric NO2 column by a factor of 2.7

Developing Particle Emission Inventories Using Remote Sensing (PEIRS)

Information regarding the magnitude and distribution of PM(sub 2.5) emissions is crucial in establishing effective PM regulations and assessing the associated risk to human health and the ecosystem. At present, emission data is obtained from measured or estimated emission factors of various source types. Collecting such information for every known source is costly and time consuming. For this reason, emission inventories are reported periodically and unknown or smaller sources are often omitted or aggregated at large spatial scale. To address these limitations, we have developed and evaluated a novel method that uses remote sensing data to construct spatially-resolved emission inventories for PM(sub 2.5). This approach enables us to account for all sources within a fixed area, which renders source classification unnecessary. We applied this method to predict emissions in the northeast United States during the period of 2002-2013 using high- resolution 1 km x 1 km Aerosol Optical Depth (AOD). Emission estimates moderately agreed with the EPA National Emission Inventory (R(sup2) = 0.66 approx. 0.71, CV = 17.7 approx. 20%). Predicted emissions are found to correlate with land use parameters suggesting that our method can capture emissions from land use-related sources. In addition, we distinguished small-scale intra-urban variation in emissions reflecting distribution of metropolitan sources. In essence, this study demonstrates the great potential of remote sensing data to predict particle source emissions cost-effectively

Exploring Systematic Offsets Between Aerosol Products from the Two MODIS Sensors

Long-term measurements of global aerosol loading and optical properties are essential for assessing climate-related questions. Using observations of spectral reflectance and radiance, the dark-target (DT) aerosol retrieval algorithm is applied to Moderate Resolution Imaging Spectroradiometer sensors on both Terra (MODIS-T) and Aqua (MODIS-A) satellites, deriving products (known as MOD04 and MYD04, respectively) of global aerosol optical depth (AOD at 0.55microm) over both land and ocean, and an ngstrm exponent (AE derived from 0.55 and 0.86microm) over ocean. Here, we analyze the overlapping time series (since mid-2002) of the Collection 6 (C6) aerosol products. Global monthly mean AOD from MOD04 (Terra with morning overpass) is consistently higher than MYD04 (Aqua with afternoon overpass) by 13% (0.02 over land and 0.015 over ocean), and this offset (MOD04 - MYD04) has seasonal as well as long-term variability. Focusing on 2008 and deriving yearly gridded mean AOD and AE, we find that, over ocean, the MOD04 (morning) AOD is higher and the AE is lower. Over land, there is more variability, but only biomass-burning regions tend to have AOD lower for MOD04. Using simulated aerosol fields from the Goddard Earth Observing System (GEOS-5) Earth system model and sampling separately (in time and space) along each MODIS-observed swath during 2008, the magnitudes of morning versus afternoon offsets of AOD and AE are smaller than those in the C6 products. Since the differences are not easily attributed to either aerosol diurnal cycles or sampling issues, we test additional corrections to the input reflectance data. The first, known as C6+, corrects for long-term changes to each sensor's polarization sensitivity and the response versus the scan angle and to cross-calibration from MODIS-T to MODIS-A. A second convolves the detrending and cross-calibration into scaling factors. Each method was applied upstream of the aerosol retrieval using 2008 data. While both methods reduced the overall AOD offset over land from 0.02 to 0.01, neither significantly reduced the AOD offset over ocean. The overall negative AE offset was reduced. A collection (C6.1) of all MODIS Atmosphere products was released, but we expect that the C6.1 aerosol products will maintain similar overall AOD and AE offsets. We conclude that (a) users should not interpret global differences between Terra and Aqua aerosol products as representing a true diurnal signal in the aerosol. (b) Because the MODIS-A product appears to have an overall smaller bias compared to ground-truth data, it may be more suitable for some applications. However (c), since the AOD offset is only 0.02 and within the noise level for single retrievals, both MODIS products may be adequate for most applications

Seasonal comparisons of Himawari-8 AHI and MODIS vegetation indices over latitudinal australian grassland sites

© 2020 by the authors. The Advanced Himawari Imager (AHI) on board the Himawari-8 geostationary (GEO) satellite offers comparable spectral and spatial resolutions as low earth orbiting (LEO) sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors, but with hypertemporal image acquisition capability. This raises the possibility of improved monitoring of highly dynamic ecosystems, such as grasslands, including fine-scale phenology retrievals from vegetation index (VI) time series. However, identifying and understanding how GEO VI temporal profiles would be different from traditional LEO VIs need to be evaluated, especially with the new generation of geostationary satellites, with unfamiliar observation geometries not experienced with MODIS, VIIRS, or Advanced Very High Resolution Radiometer (AVHRR) VI time series data. The objectives of this study were to investigate the variations in AHI reflectances and normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and two-band EVI (EVI2) in relation to diurnal phase angle variations, and to compare AHI VI seasonal datasets with MODIS VIs (standard and sun and view angle-adjusted VIs) over a functional range of dry grassland sites in eastern Australia. Strong NDVI diurnal variations and negative NDVI hotspot effects were found due to differential red and NIR band sensitivities to diurnal phase angle changes. In contrast, EVI and EVI2 were nearly insensitive to diurnal phase angle variations and displayed nearly flat diurnal profiles without noticeable hotspot influences. At seasonal time scales, AHI NDVI values were consistently lower than MODIS NDVI values, while AHI EVI and EVI2 values were significantly higher than MODIS EVI and EVI2 values, respectively. We attributed the cross-sensor differences in VI patterns to the year-round smaller phase angles and backscatter observations from AHI, in which the sunlit canopies induced a positive EVI/ EVI2 response and negative NDVI response. BRDF adjustments of MODIS VIs to solar noon and to the oblique view zenith angle of AHI resulted in strong cross-sensor convergence of VI values (R2 > 0.94, mean absolute difference <0.02). These results highlight the importance of accounting for cross-sensor observation geometries for generating compatible AHI and MODIS annual VI time series. The strong agreement found in this study shows promise in cross-sensor applications and suggests that a denser time series can be formed through combined GEO and LEO measurement synergies

Reduction of Aerosol Absorption in Beijing Since 2007 from MODIS and AERONET

An analysis of the time series of MODIS-based and AERONET aerosol records over Beijing reveals two distinct periods, before and after 2007. The MODIS data from both the Terra and Aqua satellites were processed with the new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. A comparison of MAIAC and AERONET AOT shows that whereas MAIAC consistently underestimated peak AOT values by 10-20% in the prior period, the bias mostly disappears after mid-2007. Independent analysis of the AERONET dataset reveals little or no change in the effective radii of the fine and coarse fractions and of the Angstrom exponent. At the same time, it shows an increasing trend in the single scattering albedo, by approx.0.02 in 9 years. As MAIAC was using the same aerosol model for the entire 2000-2010 period, the decrease in AOT bias after 2007 can be explained only by a corresponding decrease of aerosol absorption caused by a reduction in local black carbon emissions. The observed changes correlate in time with the Chinese government's broad measures to improve air quality in Beijing during preparations for the Summer Olympics of 2008

Oxidative Aromatization of 4,7-dihydro-6-nitroazolo[1,5-a] Pyrimidines: Synthetic Possibilities and Limitations, Mechanism of Destruction, and the Theoretical and Experimental Substantiation

The reaction tolerance of the multicomponent process between 3-aminoazoles, 1-morpholino2-nitroalkenes, and aldehydes was studied. The main patterns of this reaction have been established. Conditions for the oxidation of 4,7-dihydro-6-nitroazolo[1,5-a]pyrimidines were selected. Previous claims that the 4,7-dihydro-6-nitroazolo[1,5-a]pyrimidines could not be aromatised have now been refuted. Compounds with an electron-donor substituent at position seven undergo decomposition during oxidation. The phenomenon was explained based on experimental data, electro-chemical experiment, and quantum-chemical calculation. The mechanism of oxidative degradation has been proposed. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Funding: The synthetic part was supported by the Ministry of Science and Higher Education of the Russian Federation, State Contract no FEUZ-2020-0058 (H687.42B.223/20). The electrochemical research and the quantum chemical calculations was funded by Russian Foundation for Basic Research (RFBR), project number 20-03-00814