Aerosol Optical Depth Algorithm Research Articles

Window-Based Filtering Aerosol Retrieval Algorithm of Fine-Scale Remote Sensing Images: A Case Using Sentinel-2 Data in Beijing Region

The satellite-based Aerosol Optical Depth (AOD) retrieval algorithms are generally needed to construct Land Surface Reflectance (LSR) database. However, errors are unavoidable due to the surface complexity, especially for the short observation period and high-resolution images, such as Sentinel-2 Multi-Spectral Instrument (MSI) data. To address this, reference day images are used instead of the LSR database. The surface is assumed to be Lambertian; however, the fact is that not all pixels meet it well. Therefore, we proposed a window-based AOD retrieval algorithm, which can ignore the unreliable/non-Lambertian pixels in a retrieval window based on two main filtering processes. Finally, using Sentinel-2 Band 1 (60 m), the AODs (120 m) of 134 reference images to 43 reference images were retrieved by this algorithm from 2017 to 2021 in Beijing region, China. The results show that the retrieved AOD with the proposed algorithm exhibits good agreement with the ground-based measured AOD (R > 0.97). The high-resolution AOD presents comparable spatial distributions to the Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm AOD (1 km) products. Moreover, the very little noise and very high spatial continuity of retrieval AOD imply that this algorithm could be ported to other algorithms as part of improving AOD quality.

Retrieving High-Resolution Aerosol Optical Depth from GF-4 PMS Imagery in Eastern China

Gaofen 4 (GF-4) is a geostationary satellite, with a panchromatic and multispectral sensor (PMS) onboard, and has great potential in observing atmospheric aerosols. In this study, we developed an aerosol optical depth (AOD) retrieval algorithm for the GF-4 satellite. AOD retrieval was realized based on the pre-calculated surface reflectance database and 6S radiative transfer model. We customized the unique aerosol type according to the long time series aerosol parameters provided by the Aerosol Robotic Network (AERONET) site. The solar zenith angle, relative azimuth angle, and satellite zenith angle of the GF-4 panchromatic multispectral sensor image were calculated pixel-by-pixel. Our 1 km AOD retrievals were validated against AERONET Version 3 measurements and compared with MOD04 C6 AOD products at different resolutions. The results showed that our GF-4 AOD algorithm had a good robustness in both bright urban areas and dark rural areas. A total of 71.33% of the AOD retrievals fell within the expected errors of ±(0.05% + 20%); root-mean-square error (RMSE) and mean absolute error (MAE) were 0.922 and 0.122, respectively. The accuracy of GF-4 AOD in rural areas was slightly higher than that in urban areas. In comparison with MOD04 products, the accuracy of GF-4 AOD was much higher than that of MOD04 3 km and 10 km dark target AOD, but slightly worse than that of MOD04 10 km deep blue AOD. For different values of land surface reflectance (LSR), the accuracy of GF-4 AOD gradually deteriorated with an increase in the LSR. These results have theoretical and practical significance for aerosol research and can improve retrieval algorithms using the GF-4 satellite.

A Dual-Channel Aerosol Optical Depth Retrieval Algorithm Incorporating the BRDF Effect from AVHRR over Eastern Asia

A NOAA/AVHRR dual-channel method over land is proposed to simultaneously retrieve aerosol optical depth (AOD) at 0.55 μm, and surface reflectance at 0.63 and 0.85 μm. Compared with previous well-established one-channel retrieval algorithms, this algorithm takes advantage of the surface reflectance ratio between 0.63 and 0.85 μm in an attempt to account for the effect induced by the surface bidirectional reflectance distribution function (BRDF). This effect cannot be negligible due to the orbit drift and phasing running of NOAA satellites, both of which potentially cause large solar angular variation. Meanwhile, the observation posture change of AVHRR would cause large sensor angular variation in time series measurements. The used surface reflectance ratio based on dual channels at 0.63 and 0.85 μm is found more reasonable to be assumed as unchanged during a certain period of time, compared to the traditional ratio when addressing the BRDF issue. AOD retrievals have been carried out over Eastern Asia. Validation against aerosol robotic network (AERONET) measurements shows that up to 83% of AOD validation collocations are within error lines (±0.15 ± 0.15τ, τ is AOD) with an R of 0.88 and an root mean square error (RMSE) of 0.15. The dual-channel algorithm taking into account the surface BRDF effect is proved outperforming the conventional 0.63 μm-channel method. It indicates that our algorithm has the potential to be applied to the retrieval of long series of AOD, especially to the AOD retrieval of the sensors which lack a shortwave infrared channel required in the MODerate resolution Imaging Spectroradiometer (MODIS) dark target AOD algorithm.

Daytime and nighttime aerosol optical depth implementation in CÆLIS

Abstract. The University of Valladolid (UVa, Spain) has managed a calibration center of the AErosol RObotic NETwork (AERONET) since 2006. The CÆLIS software tool, developed by UVa, was created to manage the data generated by AERONET photometers for calibration, quality control and data processing purposes. This paper exploits the potential of this tool in order to obtain products like the aerosol optical depth (AOD) and Ångström exponent (AE), which are of high interest for atmospheric and climate studies, as well as to enhance the quality control of the instruments and data managed by CÆLIS. The AOD and cloud screening algorithms implemented in CÆLIS, both based on AERONET version 3, are described in detail. The obtained products are compared with the AERONET database. In general, the differences in daytime AOD between CÆLIS and AERONET are far below the expected uncertainty of the instrument, ranging in mean differences between -1.3×10-4 at 870 nm and 6.2×10-4 at 380 nm. The standard deviations of the differences range from 2.8×10-4 at 675 nm to 8.1×10-4 at 340 nm. The AOD and AE at nighttime calculated by CÆLIS from Moon observations are also presented, showing good continuity between day and nighttime for different locations, aerosol loads and Moon phase angles. Regarding cloud screening, around 99.9 % of the observations classified as cloud-free by CÆLIS are also assumed cloud-free by AERONET; this percentage is similar for the cases considered cloud-contaminated by both databases. The obtained results point out the capability of CÆLIS as a processing system. The AOD algorithm provides the opportunity to use this tool with other instrument types and to retrieve other aerosol products in the future.

Atmospheric Correction of Multispectral VNIR Remote Sensing Data: Algorithm and Inter‐sensor Comparison of Aerosol and Surface Reflectance Products

AbstractOptical imaging satellites, such as SPOT and Cartosat‐2S, provide visible/near infrared (VNIR) multispectral data at very high spatial resolution. The applications of these data sets are associated with precise mapping, monitoring, and change detection of Earth's surface, given that the measurements can be compensated for atmospheric effects. Existing atmospheric correction (AC) algorithms use visible and shortwave infrared channels and therefore cannot be used for AC of data from VNIR sensors. This article describes an algorithm for aerosol optical depth (AOD) retrieval and AC of VNIR imaging data. The AOD algorithm relies on the fact that for vegetated surfaces there exists a visible/NIR surface reflectance relationship due to the absorption of solar radiation by photosynthetic pigments in visible bands, while high reflectance in NIR bands governed by structural discontinuities in the leaves of healthy vegetation. We then describe how retrieved AOD is used to derive surface reflectance. To test the algorithm, the aerosol and surface reflectance products generated from 106 Cartosat‐2S data sets are compared with MODIS‐terra products. The algorithm significantly removes the haze from the images making surface feature visible. The comparison of Cartosat‐2S and MODIS‐terra AOD involving >1,500 data points shows good correlation of 0.95 with a relative difference of ≤25%. Similarly, the comparison of surface reflectance involving >4,500 data points shows good correlation ranging from 0.75 to 0.86 with a relative difference ranging from 24% to 37%. The normalized difference vegetation index shows a correlation of 0.89, with a relative difference of ≤18%. Results show that the given algorithm may be useful for AC of data from VNIR sensors.

Retrieval of Aerosol Optical Depth from the Himawari-8 Advanced Himawari Imager data: Application over Beijing in the summer of 2016

Abstract Himawari-8, a new generation geostationary satellite, with the Advanced Himawari Imager (AHI) providing Full Disk observation with high temporal resolution (10 min) exhibits prominent advantage in monitoring aerosols over East Asia region. The AHI has 16 channels from 0.46 to 13.3 to capture visible and infrared spectral data. In this paper, we developed a regional algorithm to retrieve the Aerosol Optical Depth (AOD) over Beijing area based on the AHI visible and short-wave infrared (SWIR) data from June to August 2016. The AHI surface reflectance at 0.46, 0.64, 0.86 and 2.3 μm channel was obtained using the lowest gas-corrected reflectance based on the three-month top of the atmosphere (TOA) data. The algorithm employed superposing technique and linear regression to iteratively train the AHI surface reflectance, and built optimal surface reflectance relationships between visible and SWIR bands with three Normalized Difference Vegetation Index (NDVI) classifications over Beijing area. Based on these surface reflectance relationships, the AHI AOD data were retrieved through the radiation transmission function. The AOD products from the AErosol RObotic NETwork (AERONET) ground measurement, the MODerate resolution Imaging Spectroradiometer (MODIS) and the Japan Aerospace Exploration Agency (JAXA) official AHI were used to evaluate the performance of our AOD retrievals. At diurnal scale, more than 66% of AHI AOD retrievals fell within the EE in the afternoon, which indicated that these retrievals could capture the regional aerosol variability over Beijing area. In contrast, the AHI JAXA AOD product significantly overestimated aerosol loadings. The low AOD biases (13:00–16:00) under low aerosol conditions from our retrieval algorithm demonstrated that the AHI surface reflectance assumptions are available. However, AHI retrievals in XiangHe overestimated AOD under high aerosol conditions, which indicated that the urban aerosol model is inappropriate over this site. In addition, our AHI AOD algorithm was only applied over Beijing area in the summer of 2016. The retrieval time-independence in this algorithm has not been estimated in fall and winter when NDVI is lower. Future study will investigate this surface algorithm at larger temporal and spatial scales.

INTER-COMPARISON BETWEEN MODIS SATELLITE-BASED AND AERONET GROUND-BASED AEROSOL OPTICAL DEPTH PRODUCTS IN VIETNAM

Although a number of studies have extensively inter-compared the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite-based aerosol optical depth (AOD) with the Aerosol Robotic Network (AERONET) ground-based AOD on both global and regional scales, almost no similar studies have been conducted for Vietnam - a humid subtropical climate region. For the first time, inter-comparison between the MODIS Terra and Aqua Collection 6.1 (C6.1) Dark Target (DT) 10 km, Deep Blue (DB) 10 km, and merged DT-DB 10 km with the AERONET AODs has been performed in different areas with different surface types and different climatic characteristics in Vietnam. Three investigated AERONET stations are Nghia Do (urban), Son La (mountainous rural), and Bac Lieu (coastal urban) with the studying periods of 2010 - 2016, 2012 - 2017, and 2010 - 2017, respectively. Our findings showed the better performances of DB algorithm than those of DT and DT-DB products in the urban area. Additionally, all MODIS AOD algorithm performed worse over the coastal area compared to those in the non-coastal areas. Generally, the ability of all the MODIS AODs to catch up the monthly-mean AERONET AODs has been expressed in this study.

A simplified aerosol retrieval algorithm for Himawari-8 Advanced Himawari Imager over Beijing

High temporal aerosol optical depth (AOD) is useful for inversing modelling of emissions and for comprehensively understanding the climatology of aerosol. Geostationary satellite has been used to derive AOD over land and ocean. In this study, a novel simplified aerosol retrieval algorithm for Advanced Himawari-8 imager (SARAHI) is developed to retrieve hourly AOD over Beijing from a new-generation geostationary meteorological satellite—Himawari-8, which carries the Advanced Himawari Imager (AHI). The algorithm is based on a simplified radiative transfer model and surface reflectance derived from one-month AHI apparent data. Measurements from AErosol RObotic NETwork (AERONET) sites and JAXA Himawari Monitor Version 2.0 Aerosol Retrieval product (L2ARP2.0) are used to evaluate the performance of AOD retrievals from SARAHI algorithm. There are 73.5%, 78.2%, 74.7% of AOD retrievals falling in the Expected error lines at Beijing_CAMS, Beijing_RADI, and Xianghe sites, respectively. The correlation coefficient is larger than 0.92 with small root mean square error (RMSE) of 0.134–0.164. The retrievals can also reflect the temporal variations of AOD very well. The results indicate that the SARAHI AOD algorithm can derive the regional AOD accurately and is better than the L2ARP2.0 AOD.

Screening for snow/snowmelt in SNPP VIIRS aerosol optical depth algorithm

Abstract. The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (S-NPP) spacecraft provides validated daily global aerosol optical depth (AOD) retrievals; however, a close examination of the VIIRS aerosol product identified residual snow and snowmelt contamination, resulting generally in an overestimation of AOD. The contamination was particularly evident over Northern Hemisphere high-latitude regions during the spring thaw. To improve the product performance, we introduced a new empirical snow and snowmelt screening scheme that combines a normalized difference snow index (NDSI)- and brightness temperature (BT)-based snow test, a snow adjacency test and a spatial homogeneity test (a.k.a. spatial filter). Testing of retrievals for 18 May 2014 indicated that, compared to the previous, visible reflectance anomaly (VRA)-based snow test, the new NDSI- and BT-based snow test screened out an additional 3.44 % of VIIRS AOD retrievals, most of which were over high latitudes experiencing snowmelt. The new snow adjacency test and the homogeneity test degraded another 5.57 % and 0.26 %, respectively, otherwise “good”-quality AOD retrievals. For the VIIRS–AERONET (Aerosol Robotic Network) matchups over Northern Hemisphere high-latitude regions during 3 years of spring (2013–2015), the new scheme also effectively screened out a significant number of the matchups that had anomalously high positive biases attributable to snow and snowmelt contamination. The new snow and snowmelt screening scheme was transferred to the Interface Data Processing Segment (IDPS) VIIRS aerosol algorithm on 22 June 2015. Subsequently no significant snow and snowmelt contamination was found during spring 2016. The scheme is also implemented in the new Enterprise VIIRS aerosol algorithm in the National Oceanic and Atmospheric Administration (NOAA) Enterprise Processing System (EPS) that became operational in 2017.

Retrieval of Aerosol Optical Depth in the Arid or Semiarid Region of Northern Xinjiang, China

Satellite remote sensing has been widely used to retrieve aerosol optical depth (AOD), which is an indicator of air quality as well as radiative forcing. The dark target (DT) algorithm is applied to low reflectance areas, such as dense vegetation, and the deep blue (DB) algorithm is adopted for bright-reflecting regions. However, both DT and DB algorithms ignore the effect of surface bidirectional reflectance. This paper provides a method for AOD retrieval in arid or semiarid areas, in which the key points are the accurate estimation of surface reflectance and reasonable assumptions of the aerosol model. To reduce the uncertainty in surface reflectance, a minimum land surface reflectance database at the spatial resolution of 500 m for each month was constructed based on the moderate-resolution imaging spectroradiometer (MODIS) surface reflectance product. Furthermore, a bidirectional reflectance distribution function (BRDF) correction model was adopted to compensate for the effect of surface reflectance anisotropy. The aerosol parameters, including AOD, single scattering albedo, asymmetric factor, Ångström exponent and complex refractive index, are determined based on the observation of two sunphotometers installed in northern Xinjiang from July to August 2014. The AOD retrieved from the MODIS images was validated with ground-based measurements and the Terra-MODIS aerosol product (MOD04). The 500 m AOD retrieved from the MODIS showed high consistency with ground-based AOD measurements, with an average correlation coefficient of ~0.928, root mean square error (RMSE) of ~0.042, mean absolute error (MAE) of ~0.032, and the percentage falling within the expected error (EE) of the collocations is higher than that for the MOD04 DB product. The results demonstrate that the new AOD algorithm is more suitable to represent aerosol conditions over Xinjiang than the DB standard product.

Validation of new satellite aerosol optical depth retrieval algorithm using Raman lidar observations at radiative transfer laboratory in Warsaw

During an exceptionally warm September of 2016, the unique, stable weather conditions over Poland allowed for an extensive testing of the new algorithm developed to improve the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) aerosol optical depth (AOD) retrieval. The development was conducted in the frame of the ESA-ESRIN SAMIRA project. The new AOD algorithm aims at providing the aerosol optical depth maps over the territory of Poland with a high temporal resolution of 15 minutes. It was tested on the data set obtained between 11-16 September 2016, during which a day of relatively clean atmospheric background related to an Arctic airmass inflow was surrounded by a few days with well increased aerosol load of different origin. On the clean reference day, for estimating surface reflectance the AOD forecast available on-line via the Copernicus Atmosphere Monitoring Service (CAMS) was used. The obtained AOD maps were validated against AODs available within the Poland-AOD and AERONET networks, and with AOD values obtained from the PollyXT-UW lidar. of the University of Warsaw (UW).

A surface reflectance scheme for retrieving aerosol optical depth over urban surfaces in MODIS Dark Target retrieval algorithm

Abstract. The MODerate resolution Imaging Spectroradiometer (MODIS) instruments, aboard the two Earth Observing System (EOS) satellites Terra and Aqua, provide aerosol information with nearly daily global coverage at moderate spatial resolution (10 and 3 km). Almost 15 years of aerosol data records are now available from MODIS that can be used for various climate and air-quality applications. However, the application of MODIS aerosol products for air-quality concerns is limited by a reduction in retrieval accuracy over urban surfaces. This is largely because the urban surface reflectance behaves differently than that assumed for natural surfaces. In this study, we address the inaccuracies produced by the MODIS Dark Target (MDT) algorithm aerosol optical depth (AOD) retrievals over urban areas and suggest improvements by modifying the surface reflectance scheme in the algorithm. By integrating MODIS Land Surface Reflectance and Land Cover Type information into the aerosol surface parameterization scheme for urban areas, much of the issues associated with the standard algorithm have been mitigated for our test region, the continental United States (CONUS). The new surface scheme takes into account the change in underlying surface type and is only applied for MODIS pixels with urban percentage (UP) larger than 20 %. Over the urban areas where the new scheme has been applied (UP > 20 %), the number of AOD retrievals falling within expected error (EE %) has increased by 20 %, and the strong positive bias against ground-based sun photometry has been eliminated. However, we note that the new retrieval introduces a small negative bias for AOD values less than 0.1 due to the ultra-sensitivity of the AOD retrieval to the surface parameterization under low atmospheric aerosol loadings. Global application of the new urban surface parameterization appears promising, but further research and analysis are required before global implementation.

Scientific impact of MODIS C5 calibration degradation and C6+ improvements

Abstract. The Collection 6 (C6) MODIS (Moderate Resolution Imaging Spectroradiometer) land and atmosphere data sets are scheduled for release in 2014. C6 contains significant revisions of the calibration approach to account for sensor aging. This analysis documents the presence of systematic temporal trends in the visible and near-infrared (500 m) bands of the Collection 5 (C5) MODIS Terra and, to lesser extent, in MODIS Aqua geophysical data sets. Sensor degradation is largest in the blue band (B3) of the MODIS sensor on Terra and decreases with wavelength. Calibration degradation causes negative global trends in multiple MODIS C5 products including the dark target algorithm's aerosol optical depth over land and Ångström exponent over the ocean, global liquid water and ice cloud optical thickness, as well as surface reflectance and vegetation indices, including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). As the C5 production will be maintained for another year in parallel with C6, one objective of this paper is to raise awareness of the calibration-related trends for the broad MODIS user community. The new C6 calibration approach removes major calibrations trends in the Level 1B (L1B) data. This paper also introduces an enhanced C6+ calibration of the MODIS data set which includes an additional polarization correction (PC) to compensate for the increased polarization sensitivity of MODIS Terra since about 2007, as well as detrending and Terra–Aqua cross-calibration over quasi-stable desert calibration sites. The PC algorithm, developed by the MODIS ocean biology processing group (OBPG), removes residual scan angle, mirror side and seasonal biases from aerosol and surface reflectance (SR) records along with spectral distortions of SR. Using the multiangle implementation of atmospheric correction (MAIAC) algorithm over deserts, we have also developed a detrending and cross-calibration method which removes residual decadal trends on the order of several tenths of 1% of the top-of-atmosphere (TOA) reflectance in the visible and near-infrared MODIS bands B1–B4, and provides a good consistency between the two MODIS sensors. MAIAC analysis over the southern USA shows that the C6+ approach removed an additional negative decadal trend of Terra ΔNDVI ~ 0.01 as compared to Aqua data. This change is particularly important for analysis of vegetation dynamics and trends in the tropics, e.g., Amazon rainforest, where the morning orbit of Terra provides considerably more cloud-free observations compared to the afternoon Aqua measurements.

A Simplified high resolution MODIS Aerosol Retrieval Algorithm (SARA) for use over mixed surfaces

Aerosol Optical Depth (AOD) is a measure of the columnar atmospheric aerosol content. Satellite remote sensing has been used to retrieve AOD over land and ocean at spatial resolutions of several to several tens of km. In most cases, a Radiative Transfer Model (RTM) is used to construct a look-up table (LUT) to act as a map between measurements and physical quantities. In the current study, MODerate resolution Imaging Spectroradiometer (MODIS) measurements were used to develop a Simplified Aerosol Retrieval Algorithm (SARA) for use over Hong Kong at high (500m) spatial resolution, without using a LUT. Instead, RTM calculations were applied directly to the MODIS data, with the aerosol properties derived from a local urban Aerosol Robotic Network (AERONET) station at the Hong Kong Polytechnic University, and surface reflectance from the MOD09GA level-2 daily surface reflectance product. For validation, the SARA AOD at 500m, along with the operational MODIS aerosol product (MOD04 C005) at 10km spatial resolution, was compared with data from a ground-based Microtops II sun photometer at the Hong Kong International Airport, a Sky-radiometer at the City University of Hong Kong, and an AERONET station at Hong Kong Hok Tsui. The 500m AOD retrieved from the SARA showed a high consistency with ground-based AOD measurements, with average correlation coefficient ~0.964, root mean square error (RMSE) ~0.077, and mean absolute error (MAE) ~0.065. The SARA AOD showed a good agreement with MOD04 C005 AOD over both urban and rural areas of Hong Kong, with average correlation coefficient ~0.917, RMSE ~0.087, and MAE ~0.072. The results demonstrate that our simplified AOD algorithm is better able to represent aerosol conditions over Hong Kong than the MOD04 C005 standard product as well as other higher resolution algorithms which have been tested over Hong Kong.