Aerosol Optical Thickness Products Research Articles

Evaluation and Comparison of Multi-Satellite Aerosol Optical Depth Products over East Asia Ocean.

The atmosphere over the ocean is an important research field that involves multiple aspects such as climate change, atmospheric pollution, weather forecasting, and marine ecosystems. It is of great significance for global sustainable development. Satellites provide a wide range of measurements of marine aerosol optical properties and are very important to the study of aerosol characteristics over the ocean. In this study, aerosol optical depth (AOD) data from seventeen AERONET (Aerosol Robotic Network) stations were used as benchmark data to comprehensively evaluate the data accuracy of six aerosol optical thickness products from 2013 to 2020, including MODIS (Moderate-resolution Imaging Spectrometer), VIIRS (Visible Infrared Imaging Radiometer Suite), MISR (Multi-Angle Imaging Spectrometer), OMAERO (OMI/Aura Multi-wavelength algorithm), OMAERUV (OMI/Aura Near UV algorithm), and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) in the East Asian Ocean. In the East Asia Sea, VIIRS AOD products generally have a higher correlation coefficient (R), expected error within ratio (EE within), lower root mean square error (RMSE), and median bias (MB) than MODIS AOD products. The retrieval accuracy of AOD data from VIIRS is the highest in spring. MISR showed a higher EE than other products in the East Asian Ocean but also exhibited systematic underestimation. In most cases, the OMAERUV AOD product data are of better quality than OMAERO, and OMAERO overestimates AOD throughout the year. The CALIPSO AOD product showed an apparent underestimation of the AOD in different seasons (EE Below = 58.98%), but when the AOD range is small (0 < AOD < 0.1), the CALIPSO data accuracy is higher compared with other satellite products under small AOD range. In the South China Sea, VIIRS has higher data accuracy than MISR, while in the Bohai-Yellow Sea, East China Sea, Sea of Japan, and the western Pacific Ocean, MISR has the best data accuracy. MODIS and VIIRS show similar trends in R, EE within, MB, and RMSE under the influence of AOD, Angstrom exponent (AE), and precipitable water. The study on the temporal and spatial distribution of AOD in the East Asian Ocean shows that the annual variation of AOD is different in different sea areas, and the ocean in the coastal area is greatly affected by land-based pollution. In contrast, the AOD values in the offshore areas are lower, and the aerosol type is mainly clean marine type aerosol. These findings can help researchers in the East Asian Ocean choose the most accurate and reliable satellite AOD data product to better study atmospheric aerosols' impact and trends.

How to Measure the Amount of Aerosol Optical Thickness in the Atmosphere in a Simple Way: A Calitoo Handheld Sun-Photometer Measurement

To have useful information about the effects of atmospheric particles on visibility degradation, extinction of solar radiation, climate changes, and tropospheric corrections in remote sensing, aerosol optical thickness (AOT) plays a crucial role. So, the measurement of AOT is an essential task for atmospheric physicists. In this regard, this article presents a systematic way in a simple form using a Calitoo handheld sun-photometer (SPM) to study atmospheric AOT. Zanjan city is selected for a period of six months as an example due to existing reference data for comparison. Hence, we use a schedule distributed in seven times, including 8:00, 9:30, 10:45, solar noon, 13:45, 15:30, and 17:00 in local time. The used wavelengths for recording are 465, 540, and 619 nm. Our data analysis shows that the mean value for AOT at those wavelengths are 0.25, 0.21, and 0.20, respectively. Also, the mean Ångström exponent (AE) extracted from the AOTs at 465, and 619 nm is 0.80. Accordingly, AE versus AOT plot demonstrates that most of the particles in Zanjan’s atmosphere are classified as urban industrial (∼64%) and dust plus mixed (∼36%) particles. In addition, to have a metric, a comparison between the AOT extracted from simultaneous measurements of Calitoo handheld and Cimel automatic SPMs is employed that indicates a strong correlation (r = 0.99) between them. Furthermore, OMI, Aqua MODIS, and Terra MODIS AOT products are compared with the Calitoo SPM AOT data. The results reveal that there is a better relationship between MODIS and Calitoo data than OMI. This study leads us to conclude that it can open new insights by simple instruments to study the atmospheres that are unknown to scientists due to the limitations of geographical stations.

A physical knowledge-based machine learning method for near-real-time dust aerosol properties retrieval from the Himawari-8 satellite data

Monitoring dust aerosol properties is critical for the studies of radiative transfer budget, climate change, and air quality. Aerosol optical thickness (AOT) and effective radius (Reff) are two main parameters describing the optical and microphysical properties of airborne dust aerosol. Satellite remote sensing provides an opportunity for estimating the two parameters in spatial coverage and continuously. To take the merits of machine learning algorithms and also utilize the physical knowledge discovered in the conventional retrieval algorithms, a physical-based machine learning method was proposed and applied on the Himawari-8 geostationary satellite for robust retrieval of dust aerosol properties. The main concepts of this study comprise i) constructing the model input data by extracting highly informative features from the Himawari observations according to physical knowledge and ii) exploiting the utility of six state-of-the-art machine learning algorithms in dust aerosol retrieval. The algorithms include artificial neural network (ANN), extreme boost gradient tree (XGBoost), extra tree (ET), random forest (RF), support vector regression (SVR), and kernel Ridge regression (Ridge). The ground-truth AOT and Reff data from AERONET stations were supplied as output labels. The cross-validation technique was adopted for model training and the results show that the ANN model is superior to the other machine learning models for both AOT and Reff estimation, which exhibits the lowest mean absolute error (MAE = 0.0292 and 0.0981) and the highest correlation coefficient (r = 0.98 and 0.84). When validated on an independent dataset, the ANN model achieved the lowest MAE (0.0334 and 0.1487), and the highest r (0.94 and 0.63). More importantly, when compared against representative physical-based algorithms, the developed ANN model still retains the best performance. Furthermore, the ANN model shows an overall better performance than other machine learning models and also the JAXA Himawari-8 Level-2 AOT product, with examples exhibited in three dust storm events and for continuous monitoring of one of the dust storm events. Additionally, feature importance analysis implies that the important features of dust aerosol identified by the ANN model are consistent with that in physical model-based algorithms. In summary, this study shows great potential for generating near-real-time products of dust aerosol properties from Himawari satellite data. These products can provide a scientific basis for climate and meteorological study regarding severe dust storms.

Impact of environmental pollution on the retrieval of hourly aerosol products from Advanced Himawari Imager (AHI) over Beijing

Advanced Himawari Imager (AHI) aboard Himawari-8 provides hourly Aerosol Optical Thickness (AOT) products, widely used to assimilation models and ground-level particulate matter (PM) concentration retrievals. However, the performance of AHI AOT products remains unclear under different air quality conditions. In this study, we evaluate the performance of the AHI hourly AOT products with ground-based AOT observations from four Aerosol Robotic NETwork (AERONET) sites for 2018 over Beijing under different pollution levels and aerosol types. Near-surface PM concentrations are used to categorize air quality as clean, moderate, and heavy pollution, and the aerosol types (biomass burning and urban/industrial (BMA_UIA), maritime (MA), dust (DA), mixed-type (MIXA)) are classified based on the threshold limits of AERONET aerosol properties. Overall, the AHI hourly AOT achieves good consistency with AERONET measurements in Beijing (correlation coefficient (R) = 0.78 and a root-mean-square-error (RMSE) = 0.29). Under clean conditions, the AOT successful retrieval rate is more than 80%, while it is deficient for moderate and heavy pollution. AOT for clean and moderate pollution performs better than that for heavy pollution. Moreover, AHI AOT retrievals for fine mode aerosol (BMA_UIA) are in good agreement with AERONET observations, and the performance is better than that under coarse mode aerosol (MA and DA) conditions. AHI AOT may capture the spatiotemporal distribution of fine particles pollution over Beijing. These findings will provide useful information for the improvement of the aerosol model of the retrieval algorithm and the monitoring of urban fine particles pollution.

PERFORMANCE OF AHI HOURLY AEROSOL OPTICAL PROPERTY DURING FREQUENT HAZE-FOG EVENTS: A CASE STUDY OF BEIJING

Abstract. The Advanced Himawari Imager (AHI) onboard Himawari-8, a next-generation geostationary meteorological satellite, provided firstly the full-disk aerosol observations every 10 min at sub-kilometer spatial resolution(5 km). This is responsible for retrieving the ground-level particulate matter of fewer than 2.5 micrometers and improving assimilation model. However, the representativeness of AHI L3 hourly Aerosol Optical Thickness (AOT) products remains unclear under different air quality conditions, Especially, over frequently polluted urban areas that feature complex surface characteristics and aerosol models. In this study, One-to-one comprehensive comparisons were conducted to evaluate the performance of three types of AHI L3 AOT products (version 3.0) based on the Aerosol Robotic NETwork (AERONET) aerosol measurements over Beijing. The overall comparisons of AHI and ground AOTs show the AHI merged AOT perform best, which the R is 0.87, RMSE is 0.25 and 52.5% of retrievals fall within the envelope of Expect Error (EE, ±(0.05 + 0.2 * AOTground)). For the different primary pollutants, the results suggested the three types AHI hourly AOT products are more suitable for the fine particulate matters (PM2.5) retrievals, especially the merged AOT with 0.87 of R, 0.29 of RMSE and 58.8% of within EE. Furthermore, when the slight and moderate pollution happened over Beijing, the AHI hourly AOT products perform well. And when the heavy pollution happened, the performance of the AHI merge AOT and L2 mean AOT is better. a case during low to high pollution suggested that AHI merged AOT can capture the similar spatial pattern to the MODIS (Deep Blue) DB or (Dark Target) DTDB merged AOT and has good consistency with ground-based air quality monitoring. These results demonstrate the AHI hourly merged AOT is a promising aerosol retrieval for air quality.

Evaluation of JAXA Himawari-8-AHI Level-3 Aerosol Products over Eastern China

A novel geostationary satellite, the H8/AHI (Himawari-8/Advanced Himawari Imager), greatly improved the scan times per day covering East Asia, and the operational products have been stably provided for a period of time. Currently, atmospheric aerosol pollution is a major concern in China. H8/AHI aerosol products with a high temporal resolution are helpful for real-time monitoring of subtle aerosol variation. However, the H8/AHI aerosol optical thickness (AOT) product has been updated three times since its launch, and the evaluation of this dataset is currently rare. In order to validate its accuracy, this study compared the H8/AHI Level-3 (L3) hourly AOT products of all versions with measurements obtained from eleven sunphotometer sites located in eastern China from 2015 to 2018. Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 AOT products from the same period were also used for inter-comparison. Although the H8/AHI AOT retrievals in version 010 show a moderate agreement with ground-based observations (correlation coefficient (R): 0.66–0.85), and the time series analysis shows that it can effectively monitor hourly variation, it suffers from an obvious underestimation of 0.3 compared to ground-based and MODIS observations. After the retrieval algorithm updated the predefined aerosol model, the overall underestimation of AHI AOTs was solved (version 010 slope: 0.43–0.62, version 030 slope: 0.75–1.02), and the AOTs in version 030 show a high agreement with observations from ten sites (R: 0.73–0.91). In addition, the surface reflectance dataset derived from the minimum reflectivity model in version 010 is inaccurate in parts of eastern China, for both “bright” and “dark” land surfaces, which leads to the overestimation of the AOT values under low aerosol loads at the Beijing and Xianghe sites. After the update of the surface dataset in version 030, this phenomenon was alleviated, resulting in no significant difference in scatterplots under different surface conditions. The AOTs of H8/AHI version 030 show a significant improvement compared to the previous two versions, but the spatial distribution of AHI is still different from MODIS AOT products due to the differences in sensors and algorithms. Therefore, although the evaluation in this study demonstrates the effectiveness of H8/AHI AOT products for aerosol monitoring at fine temporal resolutions, the performance of H8/AHI AOT products needs further study by considering more conditions.

XBAER-derived aerosol optical thickness from OLCI/Sentinel-3 observation

Abstract. A cloud identification algorithm used for cloud masking, which is based on the spatial variability of reflectances at the top of the atmosphere in visible wavelengths, has been developed for the retrieval of aerosol properties by MODIS. It is shown that the spatial pattern of cloud reflectance, as observed from space, is very different from that of aerosols. Clouds show a high spatial variability in the scale of a hundred metres to a few kilometres, whereas aerosols in general are homogeneous. The concept of spatial variability of reflectances at the top of the atmosphere is mainly applicable over the ocean, where the surface background is sufficiently homogeneous for the separation between aerosols and clouds. Aerosol retrievals require a sufficiently accurate cloud identification to be able to mask these ground scenes. However, a conservative mask will exclude strong aerosol episodes and a less conservative mask could introduce cloud contamination that biases the retrieved aerosol optical properties (e.g. aerosol optical depth and effective radii). A detailed study on the effect of cloud contamination on aerosol retrievals has been performed and parameters are established determining the threshold value for the MODIS aerosol cloud mask (3×3-STD) over the ocean. The 3×3-STD algorithm discussed in this paper is the operational cloud mask used for MODIS aerosol retrievals over the ocean. A prolonged pollution haze event occurred in the northeast part of China during the period 16–21 December 2016. To assess the impact of such events, the amounts and distribution of aerosol particles, formed in such events, need to be quantified. The newly launched Ocean Land Colour Instrument (OLCI) onboard Sentinel-3 is the successor of the MEdium Resolution Imaging Spectrometer (MERIS). It provides measurements of the radiance and reflectance at the top of the atmosphere, which can be used to retrieve the aerosol optical thickness (AOT) from synoptic to global scales. In this study, the recently developed AOT retrieval algorithm eXtensible Bremen AErosol Retrieval (XBAER) has been applied to data from the OLCI instrument for the first time to illustrate the feasibility of applying XBAER to the data from this new instrument. The first global retrieval results show similar patterns of aerosol optical thickness, AOT, to those from MODIS and MISR aerosol products. The AOT retrieved from OLCI is validated by comparison with AERONET observations and a correlation coefficient of 0.819 and bias (root mean square) of 0.115 is obtained. The haze episode is well captured by the OLCI-derived AOT product. XBAER is shown to retrieve AOT well from the observations of MERIS and OLCI.

An improved algorithm for retrieving the fine-mode fraction of aerosol optical thickness, part 1: Algorithm development

The fine-mode fraction (FMF) can be a useful tool to separate natural aerosols from man-made aerosols and to assist in estimating surface concentrations of particulate matter with a diameter<2.5μm. A LookUp Table-based Spectral Deconvolution Algorithm (LUT-SDA) was developed here for satellite-based applications using data such as MODerate resolution Imaging Spectroradiometer (MODIS) measurements. This method was validated against ground-based FMF retrievals from the Aerosol Robotic Network (AERONET). The LUT-SDA was then applied to two MODIS-retrieved aerosol optical thickness (AOT) products for the period of December 2013 to July 2015: the MODIS Collection 6 (C6) Dark Target (DT) AOT product and the simplified high-resolution MODIS Aerosol Retrieval Algorithm (SARA) AOT product. In comparison with the MODIS C6 FMF product in three study areas (Beijing, Hong Kong, and Osaka), FMFs estimated by the LUT-SDA agreed more closely with those retrieved from the AERONET with a very low bias. Eighty percent of the FMF values fell within the expected error range of ±0.4. The root mean square error (RMSE) was 0.168 with few anomalous values, whereas the RMSE for the MODIS FMF was 0.340 with more anomalous values. The LUT-SDA FMF estimated using SARA AOT data conveys more detailed information on urban pollution than that from MODIS C6 DT AOT data. As a demonstration, the seasonally-averaged spatial distribution of the FMF in Beijing was obtained from the LUT-SDA applied to SARA AOT data and compared with that of the AERONET-retrieved FMF. Their seasonal trends agreed well.

Saharan dust detection using multi-sensor satellite measurements.

Contemporary scientists have vested interest in trying to understand the climatology of the North Atlantic Basin since this region is considered as the genesis for hurricane formation that eventually get shipped to the tropical Atlantic region and the Caribbean. The effects of atmospheric water cycle and the climate of West Africa and the Atlantic basin are hugely impacted by the radiative forcing of Saharan dust. The focus area in this paper would be to improve the dust detection schemes by employing the use of multi sensor measurements in the thermal emissive wavelengths using legacy sensors such as Terra (T) and Aqua (A) MODerate-resolution Imaging Spectroradiometer (MODIS), fusing with Ozone Monitoring Instrument (OMI). Previous work by Hao and Qu (2007) had considered a limited number of thermal infrared channels which led to a correlation coefficient R2 value of 0.765 between the Aerosol Optical Thickness (AOT) at 550 nm and the modeled dust index. In this work, we extend the thermal infrared based dust detection by employing additional channels: the 8.55 μm which has shown high sensitivity to the Saharan dust, along with water vapor channel of 7.1 μm and cloud top channel of 13.1 μm. Also, the dust pixels were clearly identified using the OMI based aerosol types. The dust pixels were cleanly segregated from the other aerosol types such as sulfates, biomass, and other carbonaceous aerosols. These improvements led to a much higher correlation coefficient R2 value of 0.85 between the modified dust index and the AOT in comparison to the previous work. The key limitations from the current AOT products based on MODIS and were put to test by validating the improved dust detection algorithm. Two improvements were noted. First, the dust measurement radiometry using MODIS is significantly improved by at least an order of 2. Second the spatial measurements are enhanced by a factor of at least 10.

Simultaneous retrieval of aerosol optical thickness and chlorophyll concentration from multiwavelength measurement over East China Sea

AbstractA flexible inversion algorithm is proposed for simultaneously retrieving aerosol optical thickness (AOT) and surface chlorophyll a (Chl) concentration from multiwavelength observation over the ocean. In this algorithm, forward radiation calculation is performed by an accurate coupled atmosphere‐ocean model with a comprehensive bio‐optical ocean module. Then, a full‐physical nonlinear optimization approximation approach is used to retrieve AOT and Chl. For AOT retrieval, a global three‐dimensional spectral radiation‐transport aerosol model is used as the a priori constraint to increase the retrieval accuracy of aerosol. To investigate the algorithm's availability, the retrieval experiment is conducted using simulated radiance data to demonstrate that the relative errors in simultaneously determining AOT and Chl can be mostly controlled to within 10% using multiwavelength and angle covering in and out of sunglint. Furthermore, the inversion results are assessed using the actual satellite observation data obtained from Cloud and Aerosol Imager (CAI)/Greenhouse gas Observation SATellite GOSAT and MODerate resolution Imaging Spectroradiometer (MODIS)/Aqua instruments through comparison to Aerosol Robotic Network (AERONET) aerosol and ocean color (OC) products over East China Sea. Both the retrieved AOT and Chl compare favorably to the reported AERONET values, particularly when using the CASE 2 ocean module in turbid water, even when the retrieval is performed in the presence of high aerosol loading and sunglint. Finally, the CAI and MODIS images are used to jointly retrieve the spatial distribution of AOT and Chl in comparison to the MODIS AOT and OC products.

Evaluation of the representativeness of ground-based visibility for analysing the spatial and temporal variability of aerosol optical thickness in China

Although visibility is a widely-used indicator to quantify the aerosol loadings, only a few studies have been analyzed the representativeness of visibility in deriving Aerosol Optical Thickness (AOT). In this paper, ground-based visibility, MODerate-resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging SpectroRadiometer (MISR) monthly AOT products between July 2002 and December 2014 were analyzed in order to extract the dominant modes of variability using the Singular Value Decomposition (SVD) method. The method has significant merit to reduce data dimension and examine both spatial and temporal variability simultaneously. Results indicated that the satellite retrieved AOTs agreed well with ground-based visibility in terms of inter-annual variability. The correlation coefficients in the first deseasonalized mode are greater than 0.65 between visibility and satellite AOT products. However, large differences were observed in the seasonal variability between ground-based visibility and AOT. In addition, Aerosol vertical distribution from LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies (LIVAS) and cloud data from ground-based meteorological station were used to investigate the seasonal variability disagreement. The AOT values derived from LIVAS extinction coefficients between 0 and 500 m above surface have a stronger relationship with visibility, than total column AOT with visibility. It also indicates that seasonal variation of aerosol vertical distribution is the main cause of the disagreement between two parameters, and the uncertainties of satellite products also contribute to the disagreement. Results in this study highlighted that the visibility observation could only be used to depict the inter-annual AOT and more ancillary information could be used for studying seasonal AOT variation.

Satellite-based water quality monitoring in Lake Vänern, Sweden

ABSTRACTLake Vänern, Sweden, is one of Europe’s largest lakes and has a historical, cultural, ecological as well as economic importance. Lake water quality monitoring is required by national and international legislations and directives, but present programmes are insufficient to meet the requirements. To complement in situ based monitoring, the possibility to obtain reliable information about spatial and temporal water quality trends in Lake Vänern from the ENVISAT mission’s MERIS instrument was evaluated. The complete archive (2002–2012) of MERIS (Medium Resolution Imaging Spectrometer) full resolution data was processed using the water processor developed by Free University Berlin (FUB) to derive aerosol optical thickness (AOT), remote-sensing reflectance (Rrs) and water quality parameters: chlorophyll-a (chl-a) concentration, coloured dissolved organic matter absorption at 443 nm (CDOM), and total suspended matter (TSM) concentration. The objective was to investigate if, either, FUB reflectance products in combination with potential lake-specific band ratio algorithms for water quality estimation, or directly, FUB water quality products, could complement the existing monitoring programme.Application of lake-specific band ratio algorithms requires high-quality reflectance products based on correctly estimated AOT. The FUB reflectance and AOT products were evaluated using Aerosol Robotic Network – Ocean Color (AERONET-OC) match-up data measured at station Pålgrunden in Lake Vänern. The mean absolute percentage differences (MAPDs) of the final reflectance retrievals at 413, 443, 490, 555, and 665 nm were 510%, 48%, 33%, 34%, and 33%, respectively, corresponding to a large positive bias in 413 nm, positive bias in 443–555 nm, and a negative bias in 665 nm. AOT was strongly overestimated in all bands.The FUB water quality products were evaluated using match-up in situ data of chl-a, filtered absorbance (AbsF(420)) and turbidity as AbsF(420) is related to CDOM and turbidity is strongly related to TSM. The in situ data was collected within the Swedish national and regional monitoring programmes. In order to widen the range of water constituents and add more data to the analysis, data from four large Swedish lakes (Vänern, Vättern, Mälaren, and Hjälmaren) was included in the analysis. High correlation (r ≥ 0.85) between in situ data and MERIS FUB derived water quality estimates were obtained, but the absolute levels were over- (chl-a) or under- (CDOM) estimated. TSM was retrieved without bias.Calibration algorithms were established for chl-a and CDOM based on the match-up data from all four lakes. After calibration of the MERIS FUB data, realistic time series could be derived that were well in line with in situ measurements. The MAPDs of the final retrievals of chl-a, AbsF(420) and Turbidity in Lake Vänern were 37%, 15%, and 35%, respectively, corresponding to mean absolute differences (MADs) of 0.9 µg l−1, 0.17 m−1, and 0.32 mg l−1 in absolute values.The partly inaccurate reflectance estimations in combination with both positive and negative bias imply that successful application of band ratio algorithms is unlikely. The high correlation between MERIS FUB water quality products and in situ data, on the other hand, shows a potential to complement present water quality monitoring programmes and improve the understanding and representability of the temporally and spatially sparse in situ observations. The monitoring potential shown in this study is applicable to the Sentinel-3 mission’s OLCI (Ocean Land Colour Instrument), which was launched by the European Space Agency (ESA) in February 2016 as a part of the EC Copernicus programme.

Validation and expected error estimation of Suomi‐NPP VIIRS aerosol optical thickness and Ångström exponent with AERONET

AbstractThe new‐generation polar‐orbiting operational environmental sensor, the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite, provides critical daily global aerosol observations. As older satellite sensors age out, the VIIRS aerosol product will become the primary observational source for global assessments of aerosol emission and transport, aerosol meteorological and climatic effects, air quality monitoring, and public health. To prove their validity and to assess their maturity level, the VIIRS aerosol products were compared to the spatiotemporally matched Aerosol Robotic Network (AERONET) measurements. Over land, the VIIRS aerosol optical thickness (AOT) environmental data record (EDR) exhibits an overall global bias against AERONET of −0.0008 with root‐mean‐square error (RMSE) of the biases as 0.12. Over ocean, the mean bias of VIIRS AOT EDR is 0.02 with RMSE of the biases as 0.06. The mean bias of VIIRS Ocean Ångström Exponent (AE) EDR is 0.12 with RMSE of the biases as 0.57. The matchups between each product and its AERONET counterpart allow estimates of expected error in each case. Increased uncertainty in the VIIRS AOT and AE products is linked to specific regions, seasons, surface characteristics, and aerosol types, suggesting opportunity for future modifications as understanding of algorithm assumptions improves. Based on the assessment, the VIIRS AOT EDR over land reached Validated maturity beginning 23 January 2013; the AOT EDR and AE EDR over ocean reached Validated maturity beginning 2 May 2012, excluding the processing error period 15 October to 27 November 2012. These findings demonstrate the integrity and usefulness of the VIIRS aerosol products that will transition from S‐NPP to future polar‐orbiting environmental satellites in the decades to come and become the standard global aerosol data set as the previous generations' missions come to an end.

Evaluation of Polarized Remote Sensing of Aerosol Optical Thickness Retrieval over China

The monitoring capability of a polarized instrument (POLDER) under high aerosol loading conditions over China is investigated. The aerosol optical thickness (AOT), which infers the aerosol burden, is used to measure the satellite monitoring capabilities. AOT products retrieved from POLDER on low aerosol loading days, and products from a radiometric instrument (MODIS) on high and low aerosol loading days, are presented for comparison. Our study reveals that for high aerosol days, the monitoring capability of the polarized instrument is lower than that of the traditional instrument. The accuracy of matched POLDER fine-AOTs is lower than that of MODIS-matched AOTs. On low aerosol loading days, the performance of the polarized instrument is good when monitoring the aerosol optical thickness. Further analysis reveals that for the high aerosol loading days, the mean relative errors of matched POLDER fine AOTs and MODIS AOTs with respect to AERONET measurements are 44% and 16%, respectively. For the low aerosol loading days, the mean relative errors of POLDER and MODIS measurements with respect to AERONET measurements are 41% and 40%, respectively. During high aerosol days, POLDER-retrieved fine-AOTs reveal a poor accuracy with only 14% of matches falling within the error range, which is nearly one fourth of the MODIS regression results (51.59%). For the low aerosol loading days, the POLDER regression results are good. Approximately 62% of the POLDER measurements fall within the expected error range ±(0.05 + 15%) compared with the AERONET observed values.

Improved aerosol retrieval algorithm using Landsat images and its application for PM10 monitoring over urban areas

Aerosol retrieval using MODerate resolution Imaging Spectroradiometer (MODIS) has been well researched over the past decade. However, the application is limited to global- and regional-scale studies, which may not be applicable for urban areas due to its low spatial resolution. To overcome the limitation, this paper proposed an improved aerosol retrieval algorithm for Landsat images (ImAero-Landsat) at spatial resolution of 30m. This ImAero-Landsat algorithm has been improved in the following two aspects: (i) it does not require a comprehensive look up table and thus it is more efficient in AOT retrieval; and (ii) it can be operated in both bright and dark surfaces. The derived aerosol optical thickness (AOT) images were validated with AErosol RObotic NETwork (AERONET) measurements as well as MODIS MOD04 AOT products. Small root mean square errors (RMSEs) of 0.11 and 0.14 and mean absolute difference (MAD) of 0.07 and 0.11 between ImAero-Landsat AOT, with MODIS MOD04 and AERONET products were observed. By correlating with ground based PM10 concentrations, the ImAero-Landsat method outperforms (r2=0.32) than MOD04 AOT products (r2=0.23). In addition, the accuracy of estimating PM10 can be improved to r2=0.55 when the derived AOT was integrated with meteorological parameters. The accuracy is similar to the results derived from AERONET AOT (r2=0.62). This study offers a simple and accurate method to investigate aerosol optical thickness at detailed city-scale. Environmental authorities may use the derived methods for deriving aerosol distribution maps and pinpointing the sources of pollutants in urban areas.

A New Method to Calibrate Shortwave Solar Radiation Measurements

Abstract A method is proposed to simultaneously calibrate shortwave (0.3–4 μm) global, direct, and scattering solar irradiance (GSI, DSI, and SSI, respectively) measurements. The method uses the World Radiation Reference (WRR) as a calibration standard and on-site radiation measurements as inputs. Two simple but effective techniques are used in the calibration. The first is to scale SSI and GSI detection sensitivities under overcast skies, which is based on the assumption that SSI should be equal to GSI if DSI is completely scattered and absorbed. The second is a new method to retrieve aerosol optical thickness (AOT), using the ratio of horizontal DSI (HDSI) to GSI measurements under clear and clean conditions. Thereafter, retrieved AOTs are used to drive a radiative transfer model to calculate atmospheric transmittance and then a ratio of GSI to the transmittance. Deviation of this ratio to the WRR is regarded as an indicator of GSI uncertainty, and the calibration transfer coefficient is derived as the WRR ratio. The method is applied to calibrate radiation measurements at Xianghe, China, during 2005. It is estimated from the derived transfer coefficients on 36 clear and clean days that uncertainties of DSI, GSI, and SSI measurements are within −4.0% to 2.9%, −5.9% to 2.4%, and −6.1% to 4.9%, respectively. The calibration is further validated based on comparisons of AOT at 750 nm retrieved from HDSI/GSI to Aerosol Robotic Network (AERONET) AOT products. The maximum deviation between two AOT products is 0.026. The unique advantage of this method lies in its potential applications in correcting historic radiation measurements and monitoring radiometer performance.

An improved dark object method to retrieve 500 m-resolution AOT (Aerosol Optical Thickness) image from MODIS data: A case study in the Pearl River Delta area, China

This paper presents an improved Dark Dense Vegetation (DDV) method for retrieving 500m-resolution aerosol optical depth (AOT) based on MOD04-C005 arithmetic with the Moderate Resolution Imaging Spectroradiometer (MODIS) from the National Aeronautics and Space Administration (NASA). The improvements include change of the movement pattern of retrieval window, selection of a more suitable aerosol type, and storage of the look-up table. The method is then applied to obtain the AOT over the Pearl River Delta region (PRD). By comparing the results with the co-temporal ground sunphotometer observations in 2010, the correlation coefficient is found to be 0.794 with RMSE 0.139 and their variations remain consistent. Contrasts between model values in 2008 and MODIS AOT products in the same date also reveal a high accuracy of the improved DDV method. We also performed sensitivity tests to analyze the impacts of several parameters on apparent reflectance at different bands, and the results show that apparent reflectance is much more sensitive to surface reflectance and AOT than to elevation.

Suomi‐NPP VIIRS aerosol algorithms and data products

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on board the Suomi National Polar‐orbiting Partnership (S‐NPP) spacecraft was launched in October 2011. The instrument has 22 spectral channels with band centers from 412 nm to 12,050 nm. The VIIRS aerosol data products are derived primarily from the radiometric channels covering the visible through the short‐wave infrared spectral regions (412 nm to 2250 nm). The major components of the VIIRS aerosol retrieval process are data screening, land inversion, ocean inversion, suspended matter typing, and aggregation. The primary data product produced is the aerosol optical thickness (AOT) environmental data record. A higher resolution AOT intermediate product is also produced. These AOT products and their corresponding retrieval algorithms are described in detail, including theoretical basis, retrieval limitations, and data quality flagging. Preliminary evaluation of the data products has been undertaken by the VIIRS aerosol calibration/validation team using Aerosol Robotic Network ground‐based observations to show that the performance of AOT retrievals meets the requirements specified in the Joint Polar Satellite System Level 1 requirements.

A COMPARISON OF EMPIRICAL AND INTELIGENT METHODS FOR DUST DETECTION USING MODIS SATELLITE DATA

Abstract. Nowadays, dust storm in one of the most important natural hazards which is considered as a national concern in scientific communities. This paper considers the capabilities of some classical and intelligent methods for dust detection from satellite imagery around the Middle East region. In the study of dust detection, MODIS images have been a good candidate due to their suitable spectral and temporal resolution. In this study, physical-based and intelligent methods including decision tree, ANN (Artificial Neural Network) and SVM (Support Vector Machine) have been applied to detect dust storms. Among the mentioned approaches, in this paper, SVM method has been implemented for the first time in domain of dust detection studies. Finally, AOD (Aerosol Optical Depth) images, which are one the referenced standard products of OMI (Ozone Monitoring Instrument) sensor, have been used to assess the accuracy of all the implemented methods. Since the SVM method can distinguish dust storm over lands and oceans simultaneously, therefore the accuracy of SVM method is achieved better than the other applied approaches. As a conclusion, this paper shows that SVM can be a powerful tool for production of dust images with remarkable accuracy in comparison with AOT (Aerosol Optical Thickness) product of NASA.

Air quality evaluation on an urban scale based on MODIS satellite images

Earth-observing satellites have provided satellite image datasets for urban-scale air quality monitoring. Aerosol optical thickness (AOT) at 1km resolution is retrieved from MODIS on Terra Satellite by using the improved algorithm. The 1km AOT data are validated by using AOT measurements from two AERONET stations and the 10km AOT products from MOD04_L2 in the period of October 2004. Then the validated 1km AOT data are compared with ground-based PM2.5 mass concentration in Beijing. Four empirical models which are used to investigate the relationship between AOT data and PM2.5 mass concentration are obtained by regression analysis and their correlations are R2=0.818, R2=0.750, R2=0.699 and R2=0.629 respectively. In order to verify the models, we use these models to retrieve PM2.5 concentration from MODIS AOT on the 11th of October 2012 and then compare it with the PM2.5 concentration from the ground measurements in that day. 50%, 46.4%, 46.4% and 39.3% of the stations are within the expected errors respectively by the four models. Preliminary analysis of these four models indicates that the quadratic model has significant potential to enhance air quality monitoring on an urban scale. Although the limited daily MODIS AOT data, variability of terrain, weather conditions, and many other factors can limit the ability of predicting PM2.5 concentration, the models provide a cost-effective approach for obtaining the distribution of PM2.5 information from satellite image, which complement the defects of the limited ground-based monitoring station measurements.