Dark Target Algorithm Research Articles

Evaluation and Comparison of MODIS Collection 6.1 and Collection 6 Dark Target Aerosol Optical Depth over Mainland China Under Various Conditions Including Spatiotemporal Distribution, Haze Effects, and Underlying Surface

AbstractModifications such as degrading the retrieval quality of mixed pixels in the coastline area and revising surface characterization scheme have been made to the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) Dark Target (DT) aerosol optical depth (AOD) product to address inaccuracy over urban areas. In this study, comprehensive evaluations of modifications to the MODIS C6.1 DT AOD product, in comparison with Collection 6 (C6), are conducted over mainland China under different spatial distributions, seasons, and air quality for the period 2010–2017, combined with validation against Aerosol Robotic Network (AERONET) AOD measurements. The preliminary result showed that the C6.1 DT AOD product in China displayed an overall good performance with high R2 (0.87) and low root‐mean‐square error (0.23) against ground measurements. Moreover, the C6.1 DT AOD product was an overall improvement over C6, with greater correlation and lower uncertainty against ground measurements, especially for the North China Plain and Central China, although this was not the case for Western China. The improvement was also seasonal, being distinct in spring but less pronounced in winter, and negatively correlated with the level of air pollution. Furthermore, the analysis of DT AOD retrievals in different urbanized areas illustrated that the updated DT algorithm worked well in completely urbanized areas, where 97% of C6.1 DT AOD retrievals were an improvement over C6, while approximately 10% of C6.1 DT AOD retrievals were deteriorated in semiurbanized areas. Additionally, the DT AOD retrievals in areas with relatively low enhanced vegetation index and high surface reflectance were significantly improved, mitigating problems associated with the DT algorithm, further improving the reliability of MODIS DT AOD products.

Retrieval and validation of aerosol optical depth using GF-1 WFV cameras data

Conventional AOD (Aerosol Optical Depth) retrieval is restricted to the global and regional scale due to the limited spatial resolution of satellites. This does not allow for aerosol monitoring at the city level. The Chinese GF-1 Wide Field of View (WFV) sensors have sufficiently fine resolution as a data source for AOD retrieval with fine spatial resolution and a 4-day revisit time. In this study, principles similar to those in the Deep Blue (DB) and Dark Target (DT) algorithms were used to retrieve AOD at 100 m spatial resolution from GF-1 WFV images supported by Moderate Resolution Imaging Spectraradiometer (MODIS) surface reflectance (SR) products (MOD09A1). The derived GF-1 WFV AOD were compared with a combination of MOD04_3K DT AOD and MOD04_L2 DB AOD (MODIS AOD) to find that they yield reasonable Spearman correlations (RS > 0.82) over Taiwan and Beijing. The derived GF-1 WFV AOD were also validated against Aerosol Robotic Network (AERONET) AOD; the Spearman correlation values were RS = 0.911 in Beijing and RS = 0.858 in Taiwan.

Evaluating the use of satellite observations to supplement ground-level air quality data in selected cities in low- and middle-income countries

Using data from ground-level measurements, this work evaluates the performance of one chemical transport model (CTM)-based approach using MERRA2 model output and one statistical approach using a generalized additive model to translate remotely-sensed Aerosol Optical Depth (AOD) measurements from the MODIS combined Deep Blue and Dark Target algorithm to surface-level PM2.5 concentrations for nine cities in low and middle-income countries that include a range of environmental conditions (e.g., mountainous, dusty, or coastal conditions) and only have between 1 and 10 ground level monitoring sites available. This evaluation shows that the CTM-based and statistical approaches tested here generally had a low correlation with the true daily-average PM2.5 values within these nine cities, in contrast to previous studies that had showed stronger correlations over other regions. In addition, the uncertainty in the satellite-based estimates of the daily-average PM2.5 concentration at a given location in a city tended to be very large (21–77% for the statistical methods, and 48–85% for the CTM-based methods). Many cities also had significant limitations in the availability of satellite observations of aerosols throughout the year due to their coastal location, persistent clouds, or persistent seasonal snow cover. The satellite-based methods tested in this work appear to work best for low altitude, inland cities like Hanoi and Delhi, but still have significant errors (43–60%) in predictions of daily-average PM2.5 concentrations at sites within these cities. The CTM-based satellite approach tested here tended to underestimate PM2.5 in high-altitude cities (except for Addis Ababa, Ethiopia, where they overestimate). However, this work suggests that under some conditions, adding satellite data to ground-level monitoring (GLM) network data via co-kriging may reduce the number of GLM sites needed to characterize PM2.5 concentrations within a city, but this needs to be determined on a case-by-case basis. This evaluation is then used to make recommendations to countries with sparse GLM networks on how to incorporate the use of satellite observations in their PM2.5 monitoring and under what conditions satellite approaches are likely to be unsuccessful.

Impacts of AOD Correction and Spatial Scale on the Correlation between High-Resolution AOD from Gaofen-1 Satellite and In Situ PM2.5 Measurements in Shenzhen City, China

Satellite-derived aerosol optical depth (AOD) is widely used to estimate surface PM2.5 concentrations. Most AOD products have relatively low spatial resolutions (i.e., ≥1 km). Consequently, insufficient research exists on the relationship between high-resolution (i.e., <1 km) AOD and PM2.5 concentrations. Taking Shenzhen City, China as the study area, we derived AOD at the 16-m spatial resolution for the period 2015–2017 based on Gaofen-1 (GF-1) satellite images and the Dark Target (DT) algorithm. Then, we extracted AOD at spatial scales ranging from 40 m to 5000 m and applied vertical and humidity corrections. We analyzed the correlation between AOD and PM2.5 concentrations, and the impacts of AOD correction and spatial scale on the correlation. It was found that the DT-derived GF-1 AOD at different spatial scales had statistically significant correlations with surface PM2.5 concentrations, and the AOD corrections strengthened the correlations. The correlation coefficients (R) between AOD at different spatial scales and PM2.5 concentrations were 0.234–0.329 and 0.340–0.423 before and after AOD corrections, respectively. In spring, summer, autumn, and winter, PM2.5 concentrations had the best correlations with humidity-corrected AOD, uncorrected AOD, vertical and humidity-corrected AOD, and uncorrected AOD, respectively, indicating a distinct seasonal variation of the aerosol characteristics. At spatial scales of 1–5 km, AOD at finer spatial scales generally had higher correlations with PM2.5 concentrations. However, at spatial scales <1 km, the correlations fluctuated irregularly, which could be attributed to scale mismatches between AOD and PM2.5 measurements. Thus, 1 km appears to be the optimum spatial scale for DT-derived AOD to maximize the correlation with PM2.5 concentrations. It is also recommended to aggregate very high-resolution DT-derived AOD to an appropriate medium resolution (e.g., 1 km) before matching them with in situ PM2.5 measurements in regional air pollution studies.

Retrieval of 500 m Aerosol Optical Depths from MODIS Measurements over Urban Surfaces under Heavy Aerosol Loading Conditions in Winter

Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products are used worldwide for their reliable accuracy. However, the aerosol optical depth (AOD) usually retrieved by the operational dark target (DT) algorithm of MODIS has been missing for most of the urban regions in Central China. This was due to a high surface reflectance and heavy aerosol loading, especially in winter, when a high cloud cover fraction and the frequent occurrence of haze events reduce the number of effective satellite observations. The retrieval of the AOD from limited satellite data is much needed and important for further aerosol investigations. In this paper, we propose an improved AOD retrieval method for 500 m MODIS data, which is based on an extended surface reflectance estimation scheme and dynamic aerosol models derived from ground-based sun-photometric observations. This improved method was applied to retrieve AOD during heavy aerosol loading and effectively complements the scarcity of AOD in correspondence with urban surface of a higher spatial resolution. The validation results showed that the retrieved AOD was consistent with MODIS DT AOD (R = ~0.87; RMSE = ~0.11) and ground measurements (R = ~0.89; RMSE = ~0.15) from both the Terra and the Aqua satellite. The method can be easily applied to different urban environments affected by air pollution and contributes to the research on aerosol.

Analysis of AOD from MODIS-Merged DT–DB Products Over the Arabian Peninsula

The satellite-based aerosol estimation has attracted immense scientific attention in a multiplicity of applications related to global warming, climate change, and air pollution research globally. Numerous aerosols from widespread deserts across the Arabian Peninsula have a significant function in the regional climate and air quality. The aim of this study is to analyze the spatiotemporal variations of aerosols over the Arabian Peninsula for the period 2003–2017. For this, the study uses the Terra and Aqua Satellite on-board Moderate Resolution Imaging Spectroradiometer (MODIS)-based Merged Dark Target (DT) and Deep Blue (DB) collection of six aerosol products (550 nm). The study also uses the Aerosol Robotic Network (AERONET)-derived AOD to evaluate MODIS-Merged DT–DB AOD across the peninsula. A linear regression technique, root mean square error, mean absolute error, relative mean bias (RMB), and EE envelope (expected error) methods are used to calculate the uncertainty in the MODIS-merged DT–DB AOD products. The results of the spatial distribution show that Terra demonstrates the highest AOD over the southern Red Sea and the west coast of the Arabian Gulf that extends up to central Saudi Arabia. However, the Aqua displays the highest AOD mostly over the southern Red Sea. AOD is increasing annually at Kuwait University, Dhabi, and Hamim stations while decreasing at KAUST and Masdar-Institute. The Solar Village (for Aqua) shows a slightly increasing trend), Dhadnah, and Mezaira showing an almost a stable condition. Aqua has the better retrievals of AOD, which fall within EE envelope (~ 60‒87%) for seven stations, whereas the Terra shows better AOD retrieval only for Dhabi (EE = ~ 81%). However, RMB reveals the correct information that the merged AOD product overestimates at all stations for both satellites, while the Aqua underestimates for the Solar Village, Kuwait University, and Dhadnah. This under- and overestimation is due to the defective surface reflectance computation and inaccurate use of aerosol model schemes in the Look-Up Table. Further qualitative research is recommended to enhance the aerosol retrieval efficiency of the DB and DT algorithms over the bright-reflecting surface.

Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China

Abstract. A new multiangle implementation of the atmospheric correction (MAIAC) algorithm has been applied in the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and has recently provided globally high-spatial-resolution aerosol optical depth (AOD) products at 1 km. Moreover, several improvements have been modified in the classical Dark Target (DT) and Deep Blue (DB) aerosol retrieval algorithms in MODIS Collection 6.1 products. Thus, validation and comparison of the MAIAC, DT, and DB algorithms are urgent in China. In this paper, we present a comprehensive assessment and comparison of AOD products at a 550 nm wavelength based on three aerosol retrieval algorithms in the MODIS sensor using ground-truth measurements from AErosol RObotic NETwork (AERONET) sites over China from 2000 to 2017. In general, MAIAC products achieved better accuracy than DT and DB products in the overall validation and accuracy improvement of DB products after the QA filter, demonstrating the highest values among the three products. In addition, the DT algorithms had higher aerosol retrievals in cropland, forest, and ocean land types than the other two products, and the MAIAC algorithms were more accurate in grassland, built-up, unoccupied, and mixed land types among the three products. In the geometry dependency analysis, the solar zenith angle, scattering angle, and relative azimuth angle, excluding the view zenith angle, significantly affected the performance of the three aerosol retrieval algorithms. The three products showed different accuracies with varying regions and seasons. Similar spatial patterns were found for the three products, but the MAIAC retrievals were smaller in the North China Plain and higher in Yunnan Province compared with the DT and DB retrievals before the QA filter. After the QA filter, the DB retrievals were significantly lower than the MAIAC retrievals in south China. Moreover, the spatiotemporal completeness of the MAIAC product was also better than the DT and DB products.

MODIS AOD sampling rate and its effect on PM2.5 estimation in North China

Much attention has been paid to develop methods to estimate particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) from satellite aerosol optical depth (AOD). One of fundamental limitation of these methods is lack of AOD and thereby PM2.5 cannot be derived from satellites when clouds are present or when surface conditions are not favorable. This would probably result in an inherent clear-sky biased estimate of PM2.5 for air quality assessment that requires continuous 24-h measurements at all-sky conditions. Using the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and PM2.5 data in North China, a highly polluted area with large spatiotemporal variabilities of AOD and PM2.5 values, missing MODIS AOD retrievals and its potential effect on PM2.5 estimation are studied. The MODIS dark target (DT) algorithm produces very few AODs in winter, with a regional observation rate of 4%, which limits its statistical significance for PM2.5 air quality monitoring. This limitation applies to MODIS DT AOD products at 10-km and 3-km resolutions since they are derived from the same retrieval core (Remer et al., 2013). In contrast, The MODIS deep blue (DB) AOD product complements the MODIS DT AOD coverage, which is remarkable in winter. The MODIS DT and DB merged product has comparable accuracy to that of the DT and DB products but shows a larger sampling rate, therefore, it is more suitable for estimating surface PM2.5. While the regional mean PM2.5 values in the presence and absence of AOD retrievals in spring and summer are comparable, but the former is substantially lower than the latter in autumn by 11.2 μgm−3 and winter by 8.5 μgm−3 on average. The difference in some stations even exceeds 20 μg m−3. Methods to fill missing AOD values in North China are crucial to provide an unbiased sampling and estimate of PM2.5 concentration in all-sky conditions, likely by integrating satellite, surface and modeling data.

Exploring aerosols near clouds with high-spatial-resolution aircraft remote sensing during SEAC4RS.

Since aerosols are important to our climate system, we seek to observe the variability of aerosol properties within cloud systems. When applied to the satellite-borne Moderate-resolution Imaging Spectroradiometer (MODIS), the Dark Target (DT) retrieval algorithm provides global aerosol optical depth (AOD at 0.55 μm) in cloud-free scenes. Since MODIS' resolution (500 m pixels, 3 km or 10 km product) is too coarse for studying near-cloud aerosol, we ported the DT algorithm to the high-resolution (~50 m pixels) enhanced-MODIS Airborne Simulator (eMAS), which flew on the high-altitude ER-2 during the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) Airborne Science Campaign over the U.S. in 2013. We find that even with aggressive cloud screening, the ~0.5 km eMAS retrievals show enhanced AOD, especially within 6 km of a detected cloud. To determine the cause of the enhanced AOD, we analyze additional eMAS products (cloud retrievals and degraded-resolution AOD), co-registered Cloud Physics Lidar (CPL) profiles, MODIS aerosol retrievals, and ground-based Aerosol Robotic Network (AERONET) observations. We also define spatial metrics to indicate local cloud distributions near each retrieval, and then separate into near-cloud and far-from-cloud environments. The comparisons show that low cloud masking is robust, and unscreened thin cirrus would have only a small impact on retrieved AOD. Some of the enhancement is consistent with clear-cloud transition zone microphysics such as aerosol swelling. However, 3D radiation interaction between clouds and the surrounding clear air appears to be the primary cause of the high AOD near clouds.

Long-term spatiotemporal variations of aerosol optical depth over Yellow and Bohai Sea.

In this study, MODerate resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) level-2 Dark Target (DT) Aerosol Optical Depth (AOD) observations at 550nm (AOD550) for the highest quality flag assurance (QA = 3) were obtained to analyze spatiotemporal variations of aerosol optical properties over the Yellow and the Bohai Sea from 2002 to 2017. Spectral AOD observations at 470nm (AOD470) and 660nm (AOD660) were obtained to calculate Angstrom Exponent (AE470-660) and classify the aerosol types including clean continental (CC), clean maritime (CM) biomass and urban industrial (BUI), dust (D), and mixed (MXD) aerosol types. Results showed a very distinct spatial pattern of AOD distribution over the Bohai Sea which looks suspicious, i.e., high aerosol loadings (AOD > 0.8) throughout the entire time period, whereas relative low AOD distribution was observed over the adjacent land pixels especially in autumn and winter, which suggested that the DT algorithm might be influenced by a large number of sediments located in the Bohai Sea. Significant differences in spatial distributions were found in different seasons in terms of area coverage as a maximum number of pixels were available during autumn, and regional high and low aerosol loadings were observed during autumn and summer, respectively. Trend analysis from 2002 to 2017 showed that AOD was increased up to 0.04 over the Bohai Sea and decreased up to 0.04 over the Yellow Sea, and this trend varies from month to month. Aerosol classification showed significant contributions of BUI and CC over the region, and contributions of CM, DUST, and MXD aerosols over the Yellow Sea were relatively high compared to the Bohai Sea.

Improved merge schemes for MODIS Collection 6.1 Dark Target and Deep Blue combined aerosol products

Our previous study illustrated that the operational Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) Dark Target (DT) and Deep Blue (DB) combined products (denoted as DTB0) are not always the best in most regions due to its unsuitable merge approach. Therefore, the objective of this study is to develop an improved merge approach to increase the spatiotemporal data coverage and reduce the estimation uncertainty. For this, three tests, i.e., a land-use-type test, a surface-relief test, and an aerosol-type test are performed according to the strengths and weakness of the performances of the DT and DB algorithms with their high-quality assurance retrievals (QA = 3 for DT and QA ≥ 2 for DB) against the newest Aerosol Robotic Network (AERONET) Version 3 Level 2.0 measurements. Based on this, new merged DT and DB products (denoted as DTB1) are generated. The Terra and Aqua DTB1 products are then validated against AERONET measurements at 286 sites on site, continental, and global scales, and for varying underlying surfaces and elevated terrains from 2013 to 2017. The DTB0 products for the same period are collected for comparison. More than 90% of the sites now have more data points, and the performances of the DTB1 products are improved with an increased percentage of the data falling within the expected error [±(0.05 + 15%)] envelope and reduced mean absolute errors and root-mean-square errors compared with DTB0 products at most sites. Separate- and equal-number comparisons show that the DTB1 products significantly improve both the data coverage and data quality. The new merged products are more accurate and less affected by varying surface structures than the operational products. These results suggest that the improved merge approach is more robust and can be used for generating more accurate global aerosol products.

Evaluation of MODIS and two reanalysis aerosol optical depth products over AERONET sites

This study selects 400 sites from Aerosol Robotic Network (AERONET) over 2002–2015 to develop an aerosol optical depth (AOD) evaluation dataset, which is used to evaluate two reanalysis AOD datasets (MERRAero and MERRA-2) from Modern-Era Retrospective analysis for Research and Applications (MERRA) projects, and satellite AOD datasets from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) products. Each AOD dataset is first evaluated individually, and then, a comparison of the MODIS, MERRAero and MERRA-2 datasets is conducted. The results show that the reanalysis MERRA-2 AOD dataset has a comparable accuracy (RMSE = 0.119, MBE = −0.008) with the satellite MODIS AOD datasets (RMSE = 0.110, MBE = 0.011). The MERRA-2 AOD dataset has similar spatial distributions with MODIS data and can capture the dynamic changes of AOD temporally. Compared to MERRAero, the MERRA-2 product improves, but it still slightly underestimates AERONET AOD in general. The MODIS AOD datasets perform differently compared to AERONET AOD due to different algorithms and spatial resolutions. Compared to AERONET AOD, the MODIS AOD datasets generated by the deep blue algorithm are negative biased, while the MODIS AOD datasets from dark target algorithm and the merged dataset are positively biased. The precisions of the Aqua MODIS AOD datasets are higher than the corresponding Terra MODIS AOD datasets.

Characterizing the 2015 Indonesia fire event using modified MODIS aerosol retrievals

Abstract. The Indonesian fire and smoke event of 2015 was an extreme episode that affected public health and caused severe economic and environmental damage. The MODIS Dark Target (DT) aerosol algorithm, developed for global applications, significantly underestimated regional aerosol optical depth (AOD) during this episode. The larger-than-global-averaged uncertainties in the DT product over this event were due to both an overly zealous set of masks that mistook heavy smoke plumes for clouds and/or inland water, and also an aerosol model developed for generic global aerosol conditions. Using Aerosol Robotic Network (AERONET) Version 3 sky inversions of local AERONET stations, we created a specific aerosol model for the extreme event. Thus, using this new less-absorbing aerosol model, cloud masking based on results of the MODIS cloud optical properties algorithm, and relaxed thresholds on both inland water tests and upper limits of the AOD retrieval, we created a research algorithm and applied it to 80 appropriate MODIS granules during the event. Collocating and comparing with AERONET AOD shows that the research algorithm doubles the number of MODIS retrievals greater than 1.0, while also significantly improving agreement with AERONET. The final results show that the operational DT algorithm had missed approximately 0.22 of the regional mean AOD, but as much as AOD = 3.0 for individual 0.5∘ grid boxes. This amount of missing AOD can skew the perception of the severity of the event, affect estimates of regional aerosol forcing, and alter aerosol modeling and forecasting that assimilate MODIS aerosol data products. These results will influence the future development of the global DT aerosol algorithm.

Spatiotemporal Assessment of SO<sub>2</sub>, SO<sub>4</sub> and AOD from over MENA Domain from 2006-2016 Using Multiple Satellite and Reanalysis MERRA-2 Data

The sulfur pollutants are the source of a sizeable portion of the air pollution. In this work, the spatial distribution and temporal trend of the mass concentration of two of the critical sulfur pollutants, SO2 and SO4, in addition to the aerosol optical properties (AOD) were analyzed over the region of the Middle East and North Africa (MENA) from multiple satellite resources and Modern Era Retrospective Analysis for Research and Applications version 2 (MERRA2) reanalysis data. The So2 and So4 data used in these analyses are obtained from (MERRA2) with a resolution of 0.5 x 0.625 deg throughout a period of 10 years (2005 to 2015). On the other hand, the temporal trend and spatial distribution of AOD was identified from four different satellite data. (1) moderate resolution imaging spectroradiometer (MODIS) Level 3 AOD data at 550 nm wavelengths from Collection 6 algorithm (combined dark target and deep blue algorithms) are used for 10 years temporal analysis (2006 to 2015). Data were obtained for the period 2006 to 2015 (2) Multi angle imaging spectroradiometer (MISR) with 0.5 deg spatial resolution for the same 10 years (2006 to 2015). (3) Sea Viewing Wide Field of View Sensor (SeaWIFS) with 0.5 deg for the period (2005 to 2010). (4) Ozone Monitoring Instrument (OMI) AOD at 500 nm wavelength with resolution 1 degree.

Analysis of spatial and temporal variability of aerosol optical depth over China using MODIS combined Dark Target and Deep Blue product

This study reviews spatial and temporal variability of aerosol optical depth (AOD) at 550 nm, obtained with Moderate Resolution Imaging Spectroradiometer (MODIS) (Terra) Collection 6.1 aerosol products with use of combined Dark Target and Deep Blue algorithm. Data was analyzed for 18-year period from 2000 to 2017 over the territory of the whole continental China, covering the largest cities as well as various ecological and geographical regions. Spatial distribution of AOD has distinct geographical differences with gradual decrease from the east to the west of the country. The lowest values (up to 0.25) of annual mean AOD at 550 nm occur in sparsely populated areas on the Tibetan Plateau and in the north forest ecosystems in the north-eastern part of China. Areas of desert and semidesert landscapes of Northwest China are characterized by high concentrations of naturally occurring aerosols with moderate values of AOD (0.4–0.7). The most populous regions (Pearl River Delta, Yangtze River Delta, North China Plain, and Sichuan Basin) with the highest density of agricultural and industrial activity are characterized with maximum values of AOD (over 0.7). Seasonal variation of aerosols in the most regions of China has maximum AODs in spring or summer and minimum in autumn or winter. Angstrom exponent (AE), being 0.31–1.7 for the most part of China, was used to detect the size of aerosol particles, with the lowest values (0.31–0.84) in desert north and north-west regions of the country, and the higher values in the south (1.3–1.7). Comparison of results, obtained with MODIS Terra and AERONET (Aerosol Robotic Network) at 550 nm, demonstrate a high interrelation (r = 0.8949), where 68.3% fall within the range of expected errors, set by MODIS over the land (± 0.05 ± 0.15 × AOD). The conducted Pearson’s correlation analysis between various cities of the country showed that cities in one region with shortest distances from one another demonstrated higher correlations, suggesting distinct regional dependence in aerosols distribution.

Validation and Comparison of MODIS C6.1 and C6 Aerosol Products over Beijing, China

The operational Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Products (APs) have provided long-term and wide-spatial-coverage aerosol optical properties across the globe, such as aerosol optical depth (AOD). However, the performance of the latest Collection 6.1 (C6.1) of MODIS APs is still unclear over urban areas that feature complex surface characteristics and aerosol models. The aim of this study was to validate and compare the performance of the MODIS C6.1 and C6 APs (MxD04, x = O for Terra, x = Y for Aqua) over Beijing, China. The results of the Dark Target (DT) and Deep Blue (DB) algorithms were validated against Aerosol Robotic Network (AERONET) ground-based observations at local sites. The retrieval uncertainties and accuracies were evaluated using the expected error (EE: ±0.05 + 15%) and the root-mean-square error (RMSE). It was found that the MODIS C6.1 DT products performed better than the C6 DT products, with a greater percentage (by about 13%–14%) of the retrievals falling within the EE. However, the DT retrievals collected from two collections were significantly overestimated in the Beijing region, with more than 64% and 48% of the samples falling above the EE for the Terra and Aqua satellites, respectively. The MODIS C6.1 DB products performed similarly to the C6 DB products, with 70%–73% of the retrievals matching within the EE and estimation uncertainties. Moreover, the DB algorithm performed much better than DT algorithm over urban areas, especially in winter where abundant missing pixels were found in DT products. To investigate the effects of factors on AOD retrievals, the variability in the assumed surface reflectance and the main optical properties applied in DT and DB algorithms are also analyzed.

Validation of MODIS 3 km land aerosol optical depth from NASA's EOS Terra and Aqua missions

Abstract. In addition to the standard resolution product (10 km), the MODerate resolution Imaging Spectroradiometer (MODIS) Collection 6 (C006) data release included a higher resolution (3 km). Other than accommodations for the two different resolutions, the 10 and 3 km Dark Target (DT) algorithms are basically the same. In this study, we perform global validation of the higher-resolution aerosol optical depth (AOD) over global land by comparing against AErosol RObotic NETwork (AERONET) measurements. The MODIS–AERONET collocated data sets consist of 161 410 high-confidence AOD pairs from 2000 to 2015 for Terra MODIS and 2003 to 2015 for Aqua MODIS. We find that 62.5 and 68.4 % of AODs retrieved from Terra MODIS and Aqua MODIS, respectively, fall within previously published expected error bounds of ±(0.05 + 0.2 × AOD), with a high correlation (R= 0.87). The scatter is not random, but exhibits a mean positive bias of ∼ 0.06 for Terra and ∼ 0.03 for Aqua. These biases for the 3 km product are approximately 0.03 larger than the biases found in similar validations of the 10 km product. The validation results for the 3 km product did not have a relationship to aerosol loading (i.e., true AOD), but did exhibit dependence on quality flags, region, viewing geometry, and aerosol spatial variability. Time series of global MODIS–AERONET differences show that validation is not static, but has changed over the course of both sensors' lifetimes, with Terra MODIS showing more change over time. The likely cause of the change of validation over time is sensor degradation, but changes in the distribution of AERONET stations and differences in the global aerosol system itself could be contributing to the temporal variability of validation.

Preliminary Investigation of a New AHI Aerosol Optical Depth (AOD) Retrieval Algorithm and Evaluation with Multiple Source AOD Measurements in China

The Himawari-8 geostationary weather satellite, which is an Earth observing satellite launched in October 2014, has been applied in climate, environment, and air quality studies. Using hourly observation data from the Advanced Himawari Imager (AHI) on board Himawari-8, a new dark target algorithm was proposed to retrieve the aerosol optical depth (AOD) at 1 km and 5 km resolutions over mainland China. Because of the short satellite operation time and lack of AErosol RObotic NETwork (AERONET) sites across China, we cannot derive robust and representative surface reflectance relationships for the visible to near-infrared channels by atmospheric correction. Therefore, we inherited the empirical reflectance relationship from the Moderate Resolution Imaging Spectroradiometer (MODIS) and we used the AHI and MODIS spectral response functions to make the relationship more suitable for AHI. Ultimately, our AOD products can better reflect the regional characteristics with the AHI sensor. Seasonal averages showed that our product is more similar to MODIS Collection 6 (C6) Dark Target (DT) AOD than the Japan Aerospace Exploration Agency (JAXA) AHI AOD, but the difference is largest in winter. In addition, we evaluated several satellite retrieval products (our AHI AOD, JAXA AHI AOD, the National Oceanic and Atmospheric Administration (NOAA) VIIRS AOD, MODIS DT AOD, and MODIS DB AOD) against AERONET AOD from July 2016 to June 2017. The results showed that our AHI measurements demonstrate good agreement with, but exhibit a little overestimation, as compared to ground-based AERONET measurements with a correlation coefficient of 0.83 and an root-mean-square error (RMSE) of 0.112. The hourly validation also showed stable statistical results. A time series comparison with ground-based observations from two AERONET sites (Beijing-CAMS and XiangHe) showed that our AHI AOD products have trends as those in MODIS DB AOD, but that the bias in Beijing-CAMS is positive and higher than that in XiangHe. This error and the slight overestimation may be caused by the single continental aerosol model assumption and not considering the Normalized Difference Vegetation Index (NDVI).

Comparison of aerosol optical depth from satellite (MODIS), sun photometer and broadband pyrheliometer ground-based observations in Cuba

Abstract. In the present study, we report the first comparison between the aerosol optical depth (AOD) and Ångström exponent (AE) of the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra (AODt) and Aqua (AODa) satellites and those measured using a sun photometer (AODSP) at Camagüey, Cuba, for the period 2008 to 2014. The comparison of Terra and Aqua data includes AOD derived with both deep blue (DB) and dark target (DT) algorithms from MODIS Collection 6. Combined Terra and Aqua (AODta) data were also considered. Assuming an interval of ±30 min around the overpass time and an area of 25 km around the sun photometer site, two coincidence criteria were considered: individual pairs of observations and both spatial and temporal mean values, which we call collocated daily means. The usual statistics (root mean square error, RMSE; mean absolute error, MAE; median bias, BIAS), together with linear regression analysis, are used for this comparison. Results show very similar values for both coincidence criteria: the DT algorithm generally displays better statistics and higher homogeneity than the DB algorithm in the behaviour of AODt, AODa, AODta compared to AODSP. For collocated daily means, (a) RMSEs of 0.060 and 0.062 were obtained for Terra and Aqua with the DT algorithm and 0.084 and 0.065 for the DB algorithm, (b) MAE follows the same patterns, (c) BIAS for both Terra and Aqua presents positive and negative values but its absolute values are lower for the DT algorithm; (d) combined AODta data also give lower values of these three statistical indicators for the DT algorithm; (e) both algorithms present good correlations for comparing AODt, AODa, AODta vs. AODSP, with a slight overestimation of satellite data compared to AODSP, (f). The DT algorithm yields better figures with slopes of 0.96 (Terra), 0.96 (Aqua) and 0.96 (Terra + Aqua) compared to the DB algorithm (1.07, 0.90, 0.99), which displays greater variability. Multi-annual monthly means of AODta establish a first climatology that is more comparable to that given by the sun photometer and their statistical evaluation reveals better agreement with AODSP for the DT algorithm. Results of the AE comparison showed similar results to those reported in the literature concerning the two algorithms' capacity for retrieval. A comparison between broadband aerosol optical depth (BAOD), derived from broadband pyrheliometer observations at the Camagüey site and three other meteorological stations in Cuba, and AOD observations from MODIS on board Terra and Aqua show a poor correlation with slopes below 0.4 for both algorithms. Aqua (Terra) showed RMSE values of 0.073 (0.080) and 0.088 (0.087) for the DB and DT algorithms. As expected, RMSE values are higher than those from the MODIS–sun photometer comparison, but within the same order of magnitude. Results from the BAOD derived from solar radiation measurements demonstrate its reliability in describing climatological AOD series estimates.

Validation of MODIS C6 Dark Target Aerosol Products at 3 km and 10 km Spatial Resolutions Over the China Seas and the Eastern Indian Ocean

In this study, MODerate resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6) level-2 Dark Target (DT) Aerosol Optical Depth (AOD) products at 3 km (DT3K) and 10 km (DT10K) spatial resolutions were validated over the China seas and the eastern Indian Ocean against Maritime Aerosol Network (MAN) Level 1.5 AOD measurements collected through 13 cruises from 2010 to 2014. For this, DT3K and DT10K AOD observations were obtained from four Scientific Data Sets (SDS), i.e., “Effective_Optical_Depth_Average_Ocean” (EODAOAOD), “Effective_Optical_Depth_Best_Ocean” (EODBOAOD), “Image_Optical_Depth_Land_And_Ocean” (IODLAOAOD) and “Optical_Depth_Land_And_Ocean” (ODLAOAOD). The MAN AOD measurements were filtered within (i) ±2 h, (ii) ±4 h, (iii) ±6 h, and (iv) ±12 h of MODIS overpass time. Results showed that the DT10K and DT3K performed equally over the China seas and the eastern Indian Ocean in terms of retrievals quality and agreement with the MAN AOD measurements, whereas the DT3K has less coincident observations than the DT10K. For seasonal analysis, larger underestimation in the DT algorithm was observed in autumn followed by spring, whereas retrievals were well correlated with the MAN AOD data in summer. Overall, this study found that ODLAOAOD observations for the DT3K and DT10K were much better than EODAOAOD, EODBOAOD and IODLAOAOD in terms of high correlation and a large percentage of the AOD retrievals within the Expected Error (EE = +(0.04 + 10%), −(0.02 + 10%)).