Evaluation of Landsat-8 and Sentinel-2A Aerosol Optical Depth Retrievals Across Chinese Cities and Implications for Medium Spatial Resolution Urban Aerosol Monitoring

In urban environments, aerosol distributions may change rapidly due to building and transport infrastructure and human population density variations. The recent availability of medium resolution Landsat-8 and Sentinel-2 satellite data provide the opportunity for aerosol optical depth (AOD) estimation at higher spatial resolution than provided by other satellites. AOD retrieved from 30 m Landsat-8 and 10 m Sentinel-2A data using the Land Surface Reflectance Code (LaSRC) were compared with coincident ground-based Aerosol Robotic Network (AERONET) Version 3 AOD data for 20 Chinese cities in 2016. Stringent selection criteria were used to select contemporaneous data; only satellite and AERONET data acquired within 10 min were considered. The average satellite retrieved AOD over a 1470 m1470 m window centered on each AERONET site was derived to capture fine scale urban AOD variations. AERONET Level 1.5 (cloud-screened) and Level 2.0 (cloud-screened and also quality assured) data were considered. For the 20 urban AERONET sites in 2016 there were 106 (Level 1.5) and 67 (Level 2.0) Landsat-8 AERONET AOD contemporaneous data pairs, and 118 (Level 1.5) and 89 (Level 2.0) Sentinel-2A AOD data pairs. The greatest AOD values (>1.5) occurred in Beijing, suggesting that the Chinese capital was one of the most polluted cities in China in 2016. The LaSRC Landsat-8 and Sentinel-2A AOD retrievals agreed well with the AERONET AOD data (linear regression slopes > 0.96; coefficient of determination r(exp 2) > 0.90; root mean square deviation < 0.175) and demonstrate that the LaSRC is an effective and applicable medium resolution AOD retrieval algorithm over urban environments. The Sentinel-2A AOD retrievals had better accuracy than the Landsat-8 AOD retrievals, which is consistent with previously published research.The implications of the research and the potential for urban aerosol monitoring by combining the freely available Landsat-8 and Sentinel-2 satellite data are discussed

A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land

Cloud and Aerosol Imager (CAI) onboard the Greenhouse Gases Observing Satellite (GOSAT) is a multi-band sensor designed to observe and acquire information on clouds and aerosols. In order to retrieve aerosol optical depth (AOD) over land from the CAI sensor, a Dark Target (DT) algorithm for GOSAT CAI was developed based on the strategy of the Moderate Resolution Imaging Spectroradiometer (MODIS) DT algorithm. When retrieving AOD from satellite platforms, determining surface contributions is a major challenge. In the MODIS DT algorithm, surface signals in the visible wavelengths are estimated based on the relationships between visible channels and shortwave infrared (SWIR) near the 2.1 µm channel. However, the CAI only has a 1.6 µm band to cover the SWIR wavelengths. To resolve the difficulties in determining surface reflectance caused by the lack of 2.1 μm band data, we attempted to analyze the relationship between reflectance at 1.6 µm and at 2.1 µm. We did this using the MODIS surface reflectance product and then connecting the reflectances at 1.6 µm and the visible bands based on the empirical relationship between reflectances at 2.1 µm and the visible bands. We found that the reflectance relationship between 1.6 µm and 2.1 µm is typically dependent on the vegetation conditions, and that reflectances at 2.1 µm can be parameterized as a function of 1.6 µm reflectance and the Vegetation Index (VI). Based on our experimental results, an Aerosol Free Vegetation Index (AFRI2.1)-based regression function connecting the 1.6 µm and 2.1 µm bands was summarized. Under light aerosol loading (AOD at 0.55 µm < 0.1), the 2.1 µm reflectance derived by our method has an extremely high correlation with the true 2.1 µm reflectance (r-value = 0.928). Similar to the MODIS DT algorithms (Collection 5 and Collection 6), a CAI-applicable approach that uses AFRI2.1 and the scattering angle to account for the visible surface signals was proposed. It was then applied to the CAI sensor for AOD retrieval; the retrievals were validated by comparisons with ground-level measurements from Aerosol Robotic Network (AERONET) sites. Validations show that retrievals from the CAI have high agreement with the AERONET measurements, with an r-value of 0.922, and 69.2% of the AOD retrieved data falling within the expected error envelope of ± (0.1 + 15% AODAERONET)

Applying the Dark Target Aerosol Algorithm with Advanced Himawari Imager Observations During the KORUS-AQ Field Campaign

For nearly 2 decades we have been quantitatively observing the Earth's aerosol system from space at one or two times of the day by applying the Dark Target family of algorithms to polar-orbiting satellite sensors, particularly MODIS and VIIRS. With the launch of the Advanced Himawari Imager (AHI) and the Advanced Baseline Imagers (ABIs) into geosynchronous orbits, we have the new ability to expand temporal coverage of the traditional aerosol optical depth (AOD) to resolve the diurnal signature of aerosol loading during daylight hours. The KoreanUnited States Air Quality (KORUS-AQ) campaign taking place in and around the Korean peninsula during MayJune 2016 initiated a special processing of full-disk AHI observations that allowed us to make a preliminary adoption of Dark Target aerosol algorithms to the wavelengths and resolutions of AHI. Here,we describe the adaptation and show retrieval results from AHI for this 2-month period. The AHI-retrieved AOD is collocated in time and space with existing AErosol RObotic NETwork stations across Asia and with collocated Terra and Aqua MODIS retrievals. The new AHI AOD product matches AERONET, and the standard MODIS product does as well, and the agreement between AHI and MODIS retrieved AOD is excellent, as can be expected by maintaining consistency in algorithm architecture and most algorithm assumptions. Furthermore, we show that the new product approximates the AERONET-observed diurnal signature. Examining the diurnal patterns of the new AHI AOD product we find specific areas over land where the diurnal signal is spatially cohesive. For example, in Bangladesh the AOD in-creases by 0.50 from morning to evening, and in northeast China the AOD decreases by 0.25. However, over open ocean the observed diurnal cycle is driven by two artifacts, one associated with solar zenith angles greater than 70t hat may be caused by a radiative transfer model that does not properly represent the spherical Earth and the other artifact associated with the fringes of the 40 degree glint angle mask. This opportunity during KORUS-AQ provides encouragement to move towards an operational Dark Target algorithm for AHI. Future work will need to re-examine masking including snow mask, reevaluate assumed aerosol models for geosynchronous geometry, address the artifacts over the ocean, and investigate size parameter retrieval from the over-ocean algorithm

Earth observations from DSCOVR EPIC instrument

The National Oceanic and Atmospheric Administration (NOAA) Deep Space Climate Observatory (DSCOVR) spacecraft was launched on 11 February 2015 and in June 2015 achieved its orbit at the first Lagrange point (L1), 1.5 million km from Earth toward the sun. There are two National Aeronautics and Space Administration (NASA) Earth-observing instruments on board: the Earth Polychromatic Imaging Camera (EPIC) and the National Institute of Standards and Technology Advanced Radiometer (NISTAR). The purpose of this paper is to describe various capabilities of the DSCOVR EPIC instrument. EPIC views the entire sunlit Earth from sunrise to sunset at the backscattering direction (scattering angles between 168.5° and 175.5°) with 10 narrowband filters: 317, 325, 340, 388, 443, 552, 680, 688, 764, and 779 nm. We discuss a number of preprocessing steps necessary for EPIC calibration including the geolocation algorithm and the radiometric calibration for each wavelength channel in terms of EPIC counts per second for conversion to reflectance units. The principal EPIC products are total ozone (O3) amount, scene reflectivity, erythemal irradiance, ultraviolet (UV) aerosol properties, sulfur dioxide (SO2) for volcanic eruptions, surface spectral reflectance, vegetation properties, and cloud products including cloud height. Finally, we describe the observation of horizontally oriented ice crystals in clouds and the unexpected use of the O2 B-band absorption for vegetation properties.The NASA GSFC DSCOVR project is funded by NASA Earth Science Division. We gratefully acknowledge the work by S. Taylor and B. Fisher for help with the SO2 retrievals and Marshall Sutton, Carl Hostetter, and the EPIC NISTAR project for help with EPIC data. We also would like to thank the EPIC Cloud Algorithm team, especially Dr. Gala Wind, for the contribution to the EPIC cloud products. (NASA Earth Science Division)Accepted manuscrip

Synthesis of satellite (MODIS), aircraft (ICARTT), and surface (IMPROVE, EPA-AQS, AERONET) aerosol observations over eastern North America to improve MODIS aerosol retrievals and constrain surface aerosol concentrations and sources

We use an ensemble of satellite (MODIS), aircraft, and ground-based aerosol observations during the ICARTT field campaign over eastern North America in summer 2004 to (1) examine the consistency between different aerosol measurements, (2) evaluate a new retrieval of aerosol optical depths (AODs) and inferred surface aerosol concentrations (PM2.5) from the MODIS satellite instrument, and (3) apply this collective information to improve our understanding of aerosol sources. The GEOS-Chem global chemical transport model (CTM) provides a transfer platform between the different data sets, allowing us to evaluate the consistency between different aerosol parameters observed at different times and locations. We use an improved MODIS AOD retrieval based on locally derived visible surface reflectances and aerosol properties calculated from GEOS-Chem. Use of GEOS-Chem aerosol optical properties in the MODIS retrieval not only results in an improved AOD product but also allows quantitative evaluation of model aerosol mass from the comparison of simulated and observed AODs. The aircraft measurements show narrower aerosol size distributions than those usually assumed in models, and this has important implications for AOD retrievals. Our MODIS AOD retrieval compares well to the ground-based AERONET data (R = 0.84, slope = 1.02), significantly improving on the MODIS c005 operational product. Inference of surface PM2.5 from our MODIS AOD retrieval shows good correlation to the EPA-AQS data (R = 0.78) but a high regression slope (slope = 1.48). The high slope is seen in all AOD-inferred PM2.5 concentrations (AERONET: slope = 2.04; MODIS c005: slope = 1.51) and could reflect a clear-sky bias in the AOD observations. The ensemble of MODIS, aircraft, and surface data are consistent in pointing to a model overestimate of sulfate in the mid-Atlantic and an underestimate of organic and dust aerosol in the southeastern United States. The sulfate overestimate could reflect an excessive contribution from aqueous-phase production in clouds, while the organic carbon underestimate could possibly be resolved by a new secondary pathway involving dicarbonyls

Optical-microphysical Properties of Saharan Dust Aerosols and Composition Relationship Using a Multi-wavelength Raman Lidar, in Situ Sensors and Modelling: a Case Study Analysis

A strong Saharan dust event that occurred over the city of Athens, Greece (37.9° N, 23.6° E) between 27 March and 3 April 2009 was followed by a synergy of three instruments: a 6-wavelength Raman lidar, a CIMEL sun-sky radiometer and the MODIS sensor. The BSC-DREAM model was used to forecast the dust event and to simulate the vertical profiles of the aerosol concentration. Due to mixture of dust particles with low clouds during most of the reported period, the dust event could be followed by the lidar only during the cloud-free day of 2 April 2009. The lidar data obtained were used to retrieve the vertical profile of the optical (extinction and backscatter coefficients) properties of aerosols in the troposphere. The aerosol optical depth (AOD) values derived from the CIMEL ranged from 0.33-0.91 (355 nm) to 0.18-0.60 (532 nm), while the lidar ratio (LR) values retrieved from the Raman lidar ranged within 75-100 sr (355 nm) and 45-75 sr (532 nm). Inside a selected dust layer region, between 1.8 and 3.5 km height, mean LR values were 83 ± 7 and 54 ± 7 sr, at 355 and 532 nm, respectively, while the Ångström-backscatter-related (ABR 355/532) and Ångström-extinction-related (AER 355/532) were found larger than 1 (1.17 ± 0.08 and 1.11 ± 0.02, respectively), indicating mixing of dust with other particles. Additionally, a retrieval technique representing dust as a mixture of spheres and spheroids was used to derive the mean aerosol microphysical properties (mean and effective radius, number, surface and volume density, and mean refractive index) inside the selected atmospheric layers. Thus, the mean value of the retrieved refractive index was found to be 1.49( ± 0.10) + 0.007( ± 0.007)i, and that of the effective radiuses was 0.30 ± 0.18 μm. The final data set of the aerosol optical and microphysical properties along with the water vapor profiles obtained by Raman lidar were incorporated into the ISORROPIA II model to provide a possible aerosol composition consistent with the retrieved refractive index values. Thus, the inferred chemical properties showed 12-40% of dust content, sulfate composition of 16-60%, and organic carbon content of 15-64%, indicating a possible mixing of dust with haze and smoke. PM10 concentrations levels, PM10 composition results and SEM-EDX (Scanning Electron Microscope-Energy Dispersive X-ray) analysis results on sizes and mineralogy of particles from samples during the Saharan dust transport event were used to evaluate the retrieval

Air pollution assessment over Po valley (Italy) using satellite data and ground station measurements

Due to their effect on human health, the study of atmospheric pollutants is an important concern in the Po valley – northern Italy – one of the main industrialized and populated areas of the country. Our work focuses on the applicability of satellite Aerosol Optical Depth (AOD) retrievals in support of air quality monitoring and assessment in urban environments within the Po valley. This has been accomplished by using the implementation of the International MODIS/AIRS Processing Package (IMAPP) Air Quality Applications software, IDEA-I (Infusing satellite Data into Environmental Applications-International) over the Po valley study area. IDEA-I is a globally configurable software package that uses either Terra or Aqua MODerate resolution Imaging Spectro-radiometer (MODIS) AOD product retrievals to identify local domains of high values of aerosol. For our specific analyses, IDEA-I has been used over the large European domain, centred over the Po Valley. One year (2012) of MODIS AOD product retrievals from MODIS on board NASA’s Terra (MOD04) or Aqua (MYD04) satellite has been considered using IDEA-I in a retrospective study. These retrieved data have been also compared with the Particulate Matter (PM 10 ) measurements from the Italian Agency for Environmental Protection (ARPA) ground-based network stations. The acceptable results obtained by the correlation PM 10 – AOD suggest the satellite AOD as a good substitute for monitoring air quality over the Po valley domain. Yet the 10 km resolution of MODIS – AOD product is considered too large for air quality studies at urban scale. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has been developed for MODIS which provides AOD data at 1 km of spatial resolution. We have evaluated ability of MODIS product MOD04 and MAIAC products to characterize the spatial distribution of aerosols in the urban area through comparison with surface PM 10 measurements. Using MAIAC data at 1 km, we have examined the relationship between PM 10 concentrations, AOD, and AOD normalized by Planetary Boundary Layer (PBL) depths obtained from NOAA National Center for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS), for the same period of analysis. Results show that the MAIAC retrieval provides a high resolution depiction of the AOD within the Po Valley and performs nearly as well in a statistical sense as the standard MODIS retrieval during the time period considered. Results also highlight that normalization by the analyzed PBL depth to obtain an estimate of the mean boundary layer extinction is needed to capture the seasonal cycle of the observed PM 10 over the Po Valley