Spatial Distribution Of Aerosol Optical Depth 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.

Spatiotemporal changes of aerosol optical depth and its response to urbanization: a case study of Jinan City, China, 2009-2018.

With the insidiously growing impact of urban development on the environment, the issue of air quality has attracted extensive attention nationally and globally. It is of great significance to study the influence of urbanization on air quality for the rational development of cities. MODIS-MAIAC (Moderate Resolution Imaging Spectroradiometer-Multi-Angle Implementation of Atmospheric Correction) Aerosol optical depth (AOD) product, DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) and NPP/VIIRS (Suomi National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite) night-light were used to explore the spatiotemporal variation and correlation between AOD and urbanization development before and after the promulgation of environmental governance policies in Jinan City from 2009 to 2018. Results show that (1) the spatial distribution of AOD in Jinan had the characteristics of high in the north and low in the south, high in the west and low in the east, and low in some parts of the central region; there was a significant seasonal variation in time, with the highest AOD in summer and the lowest in winter. During 2009-2013, the annual average variation of AOD increased by 20.6%, while during 2014-2018, it decreased by 35.3%; (2) The distribution of night-light in Jinan City has progressively expanded, mirroring the city's ongoing development. The spatial distribution of aerosols in urban areas was relatively low compared to the surrounding areas of the city. (3) From 2009 to 2013, there existed a significant positive correlation between the spatial and temporal distribution of AOD and night-light. However, from 2014 to 2018, with the implementation of environmental governance policies, this relationship shifted to a significant negative correlation between the spatial and temporal distribution of AOD and night-light. Through an analysis of the correlation between urban development and aerosol depth in Jinan City over the past decade, it can be concluded that urban development does not inevitably result in elevated AOD levels. Notably, the Jinan government has achieved remarkable results in controlling the atmospheric environment, as evidenced by recent years' improvements.

Aerosol Evolution and Influencing Factor Analysis during Haze Periods in the Guanzhong Area of China Based on Multi-Source Data

Aerosols suspended in the atmosphere negatively affect air quality and public health and promote global climate change. The Guanzhong area in China was selected as the study area. Air quality data from July 2018 to June 2021 were recorded daily, and 19 haze periods were selected for this study. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to simulate the air mass transport trajectory during this haze period to classify the formation process. The spatial distribution of the aerosol optical depth (AOD) was obtained by processing Moderate-resolution Imaging Spectroradiometer (MODIS) data using the dark target (DT) method. Three factors were used to analyze the AOD spatial distribution characteristics based on the perceptual hashing algorithm (PHA): GDP, population density, and topography. Correlations between aerosols and the wind direction, wind speed, and precipitation were analyzed using weather station data. The research results showed that the haze period in Guanzhong was mainly due to locally generated haze (94.7%). The spatial distribution factors are GDP, population density, and topography. The statistical results showed that wind direction mainly affected aerosol diffusion in Guanzhong, while wind speed (r = −0.63) and precipitation (r = −0.66) had a significant influence on aerosol accumulation and diffusion.

An Estimation Method for PM2.5 Based on Aerosol Optical Depth Obtained from Remote Sensing Image Processing and Meteorological Factors

Understanding the spatiotemporal variations in the mass concentrations of particulate matter ≤2.5 µm (PM2.5) in size is important for controlling environmental pollution. Currently, ground measurement points of PM2.5 in China are relatively discrete, thereby limiting spatial coverage. Aerosol optical depth (AOD) data obtained from satellite remote sensing provide insights into spatiotemporal distributions for regional pollution sources. In this study, data from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD (1 km resolution) product from Moderate Resolution Imaging Spectroradiometer (MODIS) and hourly PM2.5 concentration ground measurements from 2015 to 2020 in Dalian, China were used. Although trends in PM2.5 and AOD were consistent over time, there were seasonal differences. Spatial distributions of AOD and PM2.5 were consistent (R2 = 0.922), with higher PM2.5 values in industrial areas. The method of cross-dividing the test set by year was adopted, with AOD and meteorological factors as the input variable and PM2.5 as the output variable. A backpropagation neural network (BPNN) model of joint cross-validation was established; the stability of the model was evaluated. The trend in the predicted values of BPNN was consistent with the monitored values; the estimation result of the BPNN with the introduction of meteorological factors is better; coefficient of determination (R2) and RMSE standard deviation (SD) between the predicted values and the monitored values in the test set were 0.663–0.752 and 0.01–0.05 μg/m3, respectively. The BPNN was simpler and the training time was shorter compared with those of a regression model and support vector regression (SVR). This study demonstrated that BPNN could be effectively applied to the MAIAC AOD data to estimate PM2.5 concentrations.

Aerosols over the Foothills of the Eastern Himalayan Region during Post-monsoon and Winter Seasons

In this study a Microtops II sunphotometer is used for the first time over Birtamode, a rapidly growing city located in the eastern Himalayan foothills of Nepal, to measure aerosol optical depth (AOD). The average AOD for the observation period (October 2018–February 2019) was 0.68 ± 0.39 with the post-monsoon season having a higher value (0.74 ± 0.43) compared to the winter season (0.60 ± 0.32). The Angstrom exponent (α) for post-monsoon and winter are found to be 1.08 ± 0.10 and 1.11 ± 0.16 respectively. During the monitoring period, the majority of AOD values (47%) are above 0.60, indicating moderately polluted conditions in the region. Anthropogenic, biomass burning, and mixed aerosols are identified as the prevalent aerosol types in the study region. The observed aerosol classification is also explained in terms of CAMS near-real-time model datasets. The AOD values retrieved by MODIS, VIIRS, Himawari-8, and CAMS show a good correlation with the observed Microtops AOD with R2 values ranging from 0.60 to 0.94. Moreover, different MODIS aerosol products (DB, DT, and combined DB-DT) are evaluated based on a comparison of Collection 6.1 AOD with the ground truth obtained from Microtops. The spatial distribution of AOD as observed by various satellites are compared and the vertical distribution of aerosol is also explained with the extinction coefficient provided by the CALIOP lidar onboard CALIPSO and aerosol types provided by CAMS.

Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia

Aerosol Optical Depth (AOD) and Fine Mode Fraction (FMF) are important information for air quality research. Both are mainly obtained from satellite data based on a radiative transfer model, which requires heavy computation and has uncertainties. We proposed machine learning-based models to estimate AOD and FMF directly from Geostationary Ocean Color Imager (GOCI) reflectances over East Asia. Hourly AOD and FMF were estimated for 00–07 UTC at a spatial resolution of 6 km using the GOCI reflectances, their channel differences (with 30-day minimum reflectance), solar and satellite viewing geometry, meteorological data, geographical information, and the Day Of the Year (DOY) as input features. Light Gradient Boosting Machine (LightGBM) and Random Forest (RF) machine learning approaches were applied and evaluated using random, spatial, and temporal 10-fold cross-validation with ground-based observation data. LightGBM (R2 = 0.89–0.93 and RMSE = 0.071–0.091 for AOD and R2 = 0.67–0.81 and RMSE = 0.079–0.105 for FMF) and RF (R2 = 0.88–0.92 and RMSE = 0.080–0.095 for AOD and R2 = 0.59–0.76 and RMSE = 0.092–0.118 for FMF) agreed well with the in-situ data. The machine learning models showed much smaller errors when compared to GOCI-based Yonsei aerosol retrieval and the Moderate Resolution Imaging Spectroradiometer Dark Target and Deep Blue algorithms. The Shapley Additive exPlanations values (SHAP)-based feature importance result revealed that the 412 nm band (i.e., ch01) contributed most in both AOD and FMF retrievals. Relative humidity and air temperature were also identified as important factors especially for FMF, which suggests that considering meteorological conditions helps improve AOD and FMF estimation. Besides, spatial distribution of AOD and FMF showed that using the channel difference features to indirectly consider surface reflectance was very helpful for AOD retrieval on bright surfaces.

Identification of Aerosol Pollution Hotspots in Jiangsu Province of China

Aerosol optical depth (AOD) is an important atmospheric parameter for climate change assessment, human health, and for total ecological situation studies both regionally and globally. This study used 21-year (2000–2020) high-resolution (1 km) Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm-based AOD from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra and Aqua satellites. MAIAC AOD was evaluated against Aerosol Robotic Network (AERONET) data across three sites (Xuzhou-CUMT, NUIST, and Taihu) located in Jiangsu Province. The study also investigated the spatiotemporal distributions and variations in AOD, with associated trends, and measured the impact of meteorology on AOD in the 13 cities of Jiangsu Province. The evaluation results demonstrated a high correlation (r = 0.867~0.929) between MAIAC AOD and AERONET data, with lower root mean squared error (RMSE = 0.130~0.287) and mean absolute error (MAE = 0.091~0.198). In addition, the spatial distribution of AOD was higher (&gt;0.60) in most cities except the southeast of Nantong City (AOD &lt; 0.4). Seasonally, higher AOD was seen in summer (&gt;0.70) than in spring, autumn, and winter, whereas monthly AOD peaked in June (&gt;0.9) and had a minimum in December (&lt;0.4) for all the cities. Frequencies of 0.3 ≤ AOD &lt; 0.4 and 0.4 ≤ AOD &lt; 0.5 were relatively common, indicating a turbid atmosphere, which may be associated with anthropogenic activities, increased emissions, and changes in meteorological circumstances. Trend analysis showed significant increases in AOD during 2000–2009 for all the cities, perhaps reflecting a booming economy and industrial development, with significant emissions of sulfur dioxide (SO2), and primary aerosols. China’s strict air pollution control policies and control of vehicular emissions helped to decrease AOD from 2010 to 2019, enhancing air quality throughout the study area. A notably similar pattern was observed for AOD and meteorological parameters (LST: land surface temperature, WV: water vapor, and P: precipitation), signifying that meteorology plays a role in terms of increasing and decreasing AOD.

Spatiotemporal Distribution of Aerosol Optical Depth Based on Landsat Data in the Hinterland of the Guanzhong Basin and Its Relationship with Urbanization

Aerosol optical depth (AOD) is one of the most fundamental optical properties of aerosols that characterizes the attenuation of light by aerosols and is an indicator of regional air pollution. Based on the blue band surface reflectance database from the MOD09A1 products for the period 2000-2019 and the ASTER spectral database, AOD was estimated from Landsat TM/OLI data using the deep blue algorithm (DB). Multi-year average/annual average and seasonal AOD values for the period 2000-2019 were then calculated to analyze the spatial characteristics and temporal variations of AOD using the empirical orthogonal function method (EOF). Furthermore, the impacts of urbanization on the spatio-temporal distribution of AOD were analyzed. The obtained results are summarized as follows:① The multi-year average AOD spatial distribution in the hinterland of the Guanzhong Basin was significantly affected by topography and human activities, with higher AOD values and variationsin areas of low altitude and high-intensity human activities compared to the surrounding mountains. Thus, changes in AOD in the study area are mainly affected by anthropogenic factors. AOD also showed significant seasonal variations, whereby spring (0.34) > summer (0.33) > autumn (0.23) > winter (0.12), and the largest regional differences occurred in summer; ② The annual average AOD (from 2000-2019) showed the trend of "increase-decrease-increase", and reached a maximum in 2005, with the high AOD area gradually moving to the south. The distribution of AOD values in spring and summer was relatively discrete, while it is in a low-value agglomeration state in winter; ③ Three main AOD spatial distribution modes were identified based on the EOF, which had cumulative contribution rate of 83.0%. The spatial distribution trend of AOD showed regional consistency, with feature vectors consistent with the altitude, thus reflecting the difference of AOD at different altitudes. Taking the Qinling Mountains as the dividing line, the AOD presented the "north-south" pattern, AOD showed a "north-south" pattern, reflecting the uniqueness of the regional development in the Guanzhong Basin compared to the southern Qinling Mountains. The "southeast-northwest" distribution pattern indicated that the AOD presented a reverse change trend between urban and non-urban; and ④ The results of correlation analysis between the AOD and urbanization revealed a positive correlation with permanent population density (R2=0.707, P<0.05), impervious surface density (R2=0.377, P<0.05), and industrial POI density (R2=0.727, P<0.5). These results are significant for improving the monitoring of air quality in the Guanzhong Basin and for the construction of an urban ecological environment.

250-m Aerosol Retrieval from FY-3 Satellite in Guangzhou

Aerosol distribution with fine spatial resolution is crucial for atmospheric environmental management. This paper proposes an improved algorithm of aerosol retrieval from 250-m Medium Resolution Spectral Image (MERSI) data of Chinese FY-3 satellites. A mixing model of soil and vegetation was used to calculate the parameters of the algorithm from moderate-resolution imaging spectroradiometer (MODIS) reflectance products in 500-m resolution. The mixing model was used to determine surface reflectance in blue band, and the 250-m aerosol optical depth (AOD) was retrieved through removing surface contributions from MERSI data over Guangzhou. The algorithm was used to monitor two pollution episodes in Guangzhou in 2015, and the results displayed an AOD spatial distribution with 250-m resolution. Compared with the yearly average of MODIS aerosol products in 2015, the 250-m resolution AOD derived from the MERSI data exhibited great potential for identifying air pollution sources. Daily AODs derived from MERSI data were compared with ground results from CE318 measurements. The results revealed a correlation coefficient between the AODs from MERSI and those from the ground measurements of approximately 0.85, and approximately 68% results were within expected error range of ±(0.05 + 15%τ).

Aerosol optical depth (AOD): spatial and temporal variations and association with meteorological covariates in Taklimakan desert, China.

Aerosol optical depth (AOD) is a key parameter that reflects aerosol characteristics. However, research on the AOD of dust aerosols and various environmental variables is scarce. Therefore, we conducted in-depth studies on the distributions and variations of AOD in the Taklimakan Desert and its margins, China. We examined the correlation characteristics between AOD and meteorological factors combined with satellite remote sensing detection methods using MCD19A2-MODIS AOD products (from 2000, 2005, 2010, and 2015), MOD13Q1-MODIS normalized difference vegetation index products, and meteorological data. We analyzed the temporal and spatial distributions of AOD, periodic change trends, and important impacts of meteorological factors on AOD in the Taklimakan Desert and its margins. To explore the relationships between desert aerosols and meteorological factors, a random forest model was used along with environmental variables to predict AOD and rank factor contributions. Results indicated that the monthly average AOD exhibited a clear unimodal curve that reached its maximum in April. The AOD values followed the order spring (0.28) > summer (0.27) > autumn (0.18) > winter (0.17). This seasonality is clear and can be related to the frequent sandstorms occurring in spring and early summer. Interannual AOD showed a gradually increasing trend to 2010 then large changes to 2015. AOD tends to increase from south to north. Based on the general trend, the maximum value of AOD is more dispersed and its low-value area is always stable. The climatic index that has the most significant effect on AOD is relative humidity.

VERIFICATION OF MCD19A2 DATA AND STUDY OF AEROSOL CHARACTERISTICS IN BEIJING-TIANJIN-HEBEI REGION

Abstract. The atmospheric aerosol pollution in the Beijing-Tianjin-Hebei(BTH) region of China is particularly serious. To quickly and accurately evaluate the aerosol pollution in BTH region using the remote sensing method – this article verified the accuracy of the global 1km resolution MCD19A2 aerosol optical depth (AOD) product to clarify its reliability in BTH region, moreover, MCD19A2 was used to study the characteristics of aerosols and its correlation with air quality index (AQI). First, the AOD spatial distribution in BTH region from 2014 to 2018 was obtained through the processes of MCD19A2 such as format conversion, mosaic. Then the AOD results were verified with AERONET measured data through accurate space-time matching, proving the high accuracy and reliability of MCD19A2 (R = 0.9504, MAE = 0.0863, RMSE = 0.1243, EE = 82.26%). Besides, based on the AOD results at different scales, the spatiotemporal characteristics of aerosols in BTH region were analyzed from the aspects of spatial distribution, annual variation, and seasonal variation. Finally, the study shows that MCD19A2 AOD and AQI have a high correlation and similar spatiotemporal characteristics, proving the direct indication of MCD19A2 on air pollution. This article plays an important role in the research of aerosol characteristics, air pollution monitoring and so on.

Spatio-Temporal Distribution and Variation Characteristics of Aerosol Optical Properties in Henan Province

The temporal variation and spatial distribution characteristics of aerosol optical depth (AOD), aerosol column mass concentration (AMC), and fine mode fraction (FMF) were analyzed based on the aerosol product information obtained from MODIS/Terra C6.1 from 2001 to 2018 in the Henan province. The spatial distribution of AOD and AMC in all seasons are high in the east and low in the west, and high in the north and low in the south, which was related to the special topography, population distribution, and number of enterprises in each region. Meanwhile, the spatial distribution of FMF is opposite to that of AOD and AMC. In spring, AMC was the highest, while FMF was the lowest, indicating that it was mainly affected by dust aerosol in spring. AOD and FMF were the highest in summer, while the AMC was lower, mainly because the high values of AOD in summer were caused by the hygroscopic growth of aerosol. The increase of rainwater scouring and secondary aerosol generation led to the domination of fine-mode aerosols in summer. In autumn and winter, the AOD and AMC were relatively low, and the FMF was slightly higher than in spring. The AOD and AMC in the Henan Province showed a decreasing annual trend, while the FMF exhibited an increasing trend. The average monthly peak-valley difference of AOD, AMC, and FMF decreased after 2011.

Retrieval of Aerosol Optical Depth Using Satellite Data Associated with Ground-based Observations over Urban and Rural Areas

Aerosol optical depth (AOD) can be retrieved accurately with sequential ground-based measurements of direct and diffuse solar radiance. However, spatial coverage and location frequency cause certain limitations. Hence, satellite image data are a proper tool for obtaining aerosol optical depth products with more spatial information and patterns of aerosol distribution. Currently, aerosol remote sensing may enhance our understanding of the optimal approach to AOD retrieval over urban and rural areas, and how it differs due to the characteristics of surface reflectivity. The article deals with the concepts of contrast reduction, and dark target approaches are examined using Landsat imaging and the observation of a sun photometer for integrating aerosol optical depth distribution over the city of Taipei in Taiwan. For areas with bright surfaces, such as urban areas, the above concepts were applied using the dispersion coefficient method with a sun photometer, in order to reduce errors considerably in the product. In contrast, a dark target algorithm with a relationship of surface reflectance between the blue (0.49 μm), red (0.66 μm), and infrared (2.1 μm) spectral bands is suitable for moist soils and vegetation areas. The retrieval of AOD spatial distribution is compared with MODIS AOD products and AERONET to verify the accuracy of the results. The RMSE ranged from 0.2 to 0.4, and about 50% of the data were within expected error margins (EE=± (0.05+0.15 AODsunphotometer).

Spatiotemporal Evolution and Driving Mechanism of Aerosol Optical Depth in the Ebinur Lake Basin

Aerosol optical depth (AOD) describes the attenuation of light by aerosols and reflects the degree of regional air pollution to some extent. This study was based on the data from the long-term sequence MOD09A1 from 2000 to 2015 and the generation of a lookup table using the deep blue algorithm (DB) to perform AOD remote sensing estimation on the Landsat TM/ETM+/OLI data from the Ebinur Lake Basin to analyze the temporal and spatial variation characteristics of AOD in the Ebinur Lake Basin and to perform an AOD prediction and factor contribution ranking using the random forest model (RF) combined with environmental variables. The results showed that:① AOD of Ebinur Lake Basin has significant seasonal variation characteristics, and the AOD values were spring (0.414) > summer (0.390) > autumn (0.287), with the largest variation in spring. ② The AOD average of the Ebinur Lake Basin was 0.374, and the interannual variation as a whole showed an upward trend. However, the AOD increased rapidly during 2010-2015, with an interannual increase of 32.32%, which indicated increasing air pollution in the basin over the past 15 years, especially the past five years. ③ The spatial distribution of AOD in the Ebinur Lake Basin was stepped up from the north to the south of Lake Ebinur. In this area, the pollution in Jinghe County was the most prominent, and the AOD value reached 0.483. ④ The RF model had a good predictive effect on AOD, R2=0.866, RMSE=0.042, and evapotranspiration had the most significant effect on AOD in the Ebinur Lake basin.

Large-scale MODIS AOD products recovery: Spatial-temporal hybrid fusion considering aerosol variation mitigation

Aerosol optical depth (AOD) is a pivotal parameter to reflect aerosol properties, such as aerosol radiative forcing and atmospheric corrections of the aerosol effect. Unfortunately, the valid pixels of moderate resolution imaging spectroradiometer (MODIS) AOD products are scarce, which has attracted great attention from scholars. In recent years, numerous AOD recovering algorithms have been proposed and the algorithms merely employing a single temporal AOD image are regarded as the most convenient and flexible for large-scale practical applications. However, current algorithms face the challenge of insufficiently considering the impacts of aerosol variation resulted from the temporal difference. Meanwhile, the improvement of AOD valid pixels is also poor due to the scarce excavation of complementary information. In order to address these issues, a novel algorithm of spatial-temporal hybrid fusion considering aerosol variation mitigation (ST-AVM) is developed to fill the missing pixels in Aqua AOD products with a single Terra AOD image in large scale. The results show that the total recovered AOD products nearly maintain the original accuracy of MODIS. Meanwhile, the AOD coverage is significantly improved in the study areas and the degrees of improvements regionally vary. Overall, the AOD coverage over land is increased by 123.9% (from 20.5% to 45.9%) after the recovery. Besides, the spatial distribution of recovered monthly AOD products remains fairly consistent as the original Aqua. Also, the recovered annual AOD spatial distribution shows more coherent, which indicates the reliability of ST-AVM algorithm.

Aerosol Optical Depth over the Arctic Snow-Covered Regions Derived from Dual-Viewing Satellite Observations

Aerosol properties over the Arctic snow-covered regions are sparsely provided by temporal and spatially limited in situ measurements or active Lidar observations. This introduces large uncertainties for the understanding of aerosol effects on Arctic climate change. In this paper, aerosol optical depth (AOD) is derived using the advanced along-track scanning radiometer (AATSR) instrument. The basic idea is to utilize the dual-viewing observation capability of AATSR to reduce the impacts of AOD uncertainties introduced by the absolute wavelength-dependent error on surface reflectance estimation. AOD is derived assuming that the satellite observed surface reflectance ratio can be well characterized by a snow bidirectional reflectance distribution function (BRDF) model with a certain correction direct from satellite top of the atmosphere (TOA) observation. The aerosol types include an Arctic haze aerosol obtained from campaign measurement and Arctic background aerosol (maritime aerosol) types. The proper aerosol type is selected during the iteration step based on the minimization residual. The algorithm has been used over Spitsbergen for the spring period (April–May) and the AOD spatial distribution indicates that the retrieval AOD can capture the Arctic haze event. The comparison with AERONET observations shows promising results, with a correlation coefficient R = 0.70. The time series analysis shows no systematical biases between AATSR retrieved AOD and AERONET observed ones.

阿拉伯海区域气溶胶时空动态变化及海域叶绿素a浓度特征

PDF HTML阅读 XML下载 导出引用 引用提醒 阿拉伯海区域气溶胶时空动态变化及海域叶绿素a浓度特征 DOI: 10.5846/stxb201804290973 作者: 作者单位: 西南大学地理科学学院,西南大学地理科学学院,中国科学院遥感与数字地球研究所,中国农业科学院农业信息研究所;中国农业科学院农业信息研究所,西南大学地理科学学院 作者简介: 通讯作者: 中图分类号: 基金项目: 中国科学院战略性先导科技专项（XDA19030302） Spatio-temporal dynamic characteristics of aerosol and chlorophyll a concentration in the Arabian Sea Author: Affiliation: School of Geographical Sciences, Southwest University,School of Geographical Sciences, Southwest University,,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:基于2003-2017年的MODIS/Aqua气溶胶和海洋叶绿素a浓度遥感数据资料，分析了阿拉伯海域气溶胶光学厚度（AOD）的时空动态变化及海域叶绿素a浓度（Chl-a）特征，并探讨了该海域气溶胶和海洋叶绿素a浓度的关系。结果表明：阿拉伯海区域AOD和Chl-a的空间分布都呈现出显著的季节性变化，且总体呈现出由近岸向外海逐渐降低的特征，其大小和分布受到陆源沙尘影响显著；海域AOD夏季高于春季，冬季高于秋季，15年间海域AOD整体呈现增加趋势，其中东部海域增加较明显，北部海域无显著变化；海域Chl-a秋冬季明显高于春夏季，变异性较高；由春冬季AOD与Chl-a的相关系数分析发现，二者相关性在阿拉伯海西北部区域显著性相关，且呈现出由北向南、由近海岸向外海区逐渐减弱的趋势。 Abstract:The spatial distribution and temporal variation of aerosol optical depth (AOD) and the characteristics of chlorophyll a concentration (Chl-a) in the Arabian Sea, were inter-annually and seasonally analyzed by utilizing the MODIS/Aqua AOD and chlorophyll products during 2003 to 2017, and the relationship between the aerosol and chlorophyll a concentration was discussed for the sea area. The results showed that the spatial distributions of AOD and Chl-a changed more significantly with the seasons in the Arabian Sea, and generally showed a gradual decrease from offshore to the open seas. The value and spatial distribution of the AOD and Chl-a were obviously affected by the dust in the land-sourced desert. The AOD was higher in summer than spring, and in winter was higher than that in autumn. During the past 15 years there appeared an increasing trend of the AOD, especially in its east coastal area, but no significant change occurred in the north coast. The specific performance of Chl-a was higher in autumn and winter, lower in spring and summer, and its variability was high. From the correlation coefficient analysis between AOD and Chl-a in spring and winter, the correlation between AOD and Chl-a was significantly correlated in the northwestern Arabian Sea, and it showed a trend of decreasing from north to south and from the coast to the outer sea. 参考文献 相似文献 引证文献

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.

Seasonal Variations of Aerosol Optical Depth over East China and India in Relationship to the Asian Monsoon Circulation

Seasonal variation features of aerosol optical depth (AOD) over East China and India in association with the Asian monsoon system are investigated, based on the latest AOD data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, the NCEP Final (FNL) Operational Global Analysis data, the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) data, and the NCEP/NCAR reanalysis data from March 2000 to February 2017. The results indicate that AOD in East China is significantly larger than that in India, especially in spring. The seasonal mean AOD in East China is high in both spring and summer but low in fall and winter. However, the AOD averaged over India is highest in summer and lower in spring, fall, and winter. Analysis reveals that AOD is more closely related to changes in surface wind speed in East China, while no obvious relation is found between precipitation and the AOD distribution on the seasonal timescale. As aerosols are mainly distributed in the atmospheric boundary layer (ABL), the stability of the ABL represented by Richardson number (Ri) is closely correlated with spatial distribution of AOD. The upper and lower tropospheric circulation patterns significantly differ between East China and India, resulting in different effects on the AOD. The effect of advection associated with lower tropospheric circulation on the AOD and the influence of convergence and divergence on the AOD distribution play different roles in maintaining the AOD in East China and India. These results improve our understanding of the mechanism responsible for and differences among the aerosol changes in East China and India.

Seasonal Aerosol Optical Depth (AOD) Variability Using Satellite Data and its Comparison over Saudi Arabia for the Period 2002?2013

ABSTRACTThis study analyzes the spatiotemporal variations of seasonal Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) AOD at 550 nm from the Aqua satellite over Saudi Arabia for the period 2002‒2013. Satellite retrieved AOD is also compared with AERONET AOD over the Solar Village and KAUST station. The result of the seasonal AOD spatial distribution shows that the peak AOD value of 0.6 is observed over Hafr Al Batin, Riyadh, and the Rub Al Khali desert during spring, whereas the Gizan area shows the peak AOD during summer. In contrast, the autumn shows the peak AOD value of 0.5 over Dhahran and in the proximity of Jeddah, whereas Hafr Al Batin, Al Khafji, Al Jubail, and the Rub Al Khali desert show the peak AOD value of 0.4 in winter. Regression analysis shows the AOD increasing trends during spring, summer, and autumn (except for winter) over the entire Saudi Arabia. Over the Solar Village, the AOD increasing trends are also noted during spring and summer, whereas autumn and winter display the AOD decreasing trends. The AOD increasing trends are displayed in all seasons over KAUST. Hence, the AOD increasing/decreasing trends indicate that the number of dust storms either increases or decreases over these regions. Over the Solar Village, the correlation values for MODIS DB AOD versus AERONET AOD are 0.77 (spring), 0.62 (summer), 0.65 (autumn), and 0.75 (winter). Likewise, over KAUST, the correlation values for the same pairing are 0.85 (spring), 0.71 (summer), 0.81 (autumn), and 0.89 (winter). The incorrect aerosol model selection and imperfect surface reflectance calculation are responsible for reducing the correlation. Therefore, this study recommends that the DB algorithm can be used effectively to detect AOD over Saudi Arabia, which will further help to improve the MODIS DB AOD product utilizing the next version of the algorithm.