Current approaches to retrieving aerosol properties often rely on static or single-time observations, neglecting valuable temporal dynamics, and struggle to estimate multiple parameters beyond aerosol optical depth (AOD). This study presents an advanced time series multi-wavelength aerosol transformer (TMAT) model. TMAT advances beyond AOD retrieval by jointly estimating key, yet challenging, microphysical properties-fine/coarse mode AOD (fAOD/cAOD) and single scattering albedo (SSA)-from Himawari-8 observations at 10-minute temporal and 0.02 degrees spatial resolution. TMAT leverages the end-to-end learning capacity of the Transformer architecture to exploit temporal information in geostationary satellite data and incorporates physically consistent activation and loss functions. TMAT was trained and evaluated using two years of full-disk Himawari-8 AHI observations matched with AERONET/SONET measurements. Site-based 5-fold cross-validation demonstrated TMAT's robust performance. For AOD at 550 nm, fAOD and cAOD, the correlation coefficients (R) were 0.952, 0.955, and 0.762; the root mean square errors (RMSEs) were 0.092, 0.095 and 0.039; and 60.02%, 61.54% and 81.80% of the retrievals met Global Climate Observing System requirements. SSA and absorption AOD retrievals at 440 nm exhibited RMSE (R) values of 0.042 (0.552) and 0.037 (0.736), respectively. For AOD (550 nm) >= 0.3, the R values for the derived & Aring;ngstro & uml;m Exponent and fine mode fraction were 0.786 and 0.817, with corresponding RMSEs of 0.199 and 0.093. TMAT's Transformer attention weights exhibited physically consistent behavior by assigning greater weights to observations with longer atmospheric path lengths and smaller scattering angles, where such geometric conditions enhanced aerosol signal sensitivity. These results pave the way for generating multiple aerosol product using Himawari-8/9 satellite data under the TMAT framework.

The Advanced Very High Resolution Radiometer (AVHRR) series onboard the National Oceanic and Atmospheric Administration (NOAA) and the EUMETSAT Meteorological Operational Satellite (Metop) polar-orbiting satellites have provided continuous Earth observation data since 1979, which facilitates the development of long-term global climate data records. In this paper, a new version of the algorithm for the retrieval of the Aerosol Optical Depth (AOD) over Land (ADL v2.0) using AVHRR data is proposed with improved accuracy, in particular for high AOD values. The surface reflectance estimation scheme is based on a regression model established using simulated AVHRR reflectances spectrally transferred from the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD09/MYD09 product. To address limitations in retrieving high AOD, the surface reflectance is determined using the maximum Normalized Difference Vegetation Index (NDVI) during a certain period of time. To this end, a dynamic NDVI search window is proposed to identify the NDVI that is least affected by aerosols. ADL v2.0 has been applied to provide an AOD dataset covering Mainland China (70o-140 degrees E, 15o-60oN) for the years from 1981 to 2000. This dataset has been evaluated by comparing with AOD data available from the application of the broadband extinction method (BEM) to ground-based solar radiation measurements and from the AVHRR Deep Blue (DB) AOD dataset. The AOD variations retrieved using the BEM data at seven stations (two in North China, two in Northeast China, one in East China, one in Central China, and one in the southwest mountainous region) are well reproduced by the ADL v2.0 algorithm. The comparison with the AVHRR DB AOD dataset shows good agreement with ADL v2.0 retrieval results even though with less valid retrievals for high AOD in Eastern China, Sichuan, and the Guanzhong Basin, as well as over North India.

Dust aerosols, recognized as hazardous atmospheric pollutants, significantly impact air quality, human health, and ecosystems. Understanding the dust's spatiotemporal dynamics, sources, and transport paths is crucial, especially in China, as it is home to major dust sources in East Asia and frequently experiences severe dust events. Substantial dust aerosols are produced during dust events, leading to sharp increases in particulate matter concentrations and resulting in health and environmental issues in both the dust source regions and downwind populated areas. Building on this context, this study analyzes the spatiotemporal distribution of dust aerosols in China and its six subregions between 2000 and 2023 using MERRA-2 reanalysis data. Unlike previous studies that focus on climatic drivers and anthropogenic factors influencing dust aerosol trends, this study provides new insights into dust aerosol trends in large provincial cities (population 2-20 million) within the dust-prone "Three-North" region, from the perspective of dust emissions, fluxes, and transport processes. Furthermore, the combination of MERRA-2 simulations and backward (forward) trajectory analyses offers complementary strengths. The results show that (1) spring Dust Aerosol Optical Depth (DAOD) decreased 2000-2023, with the exception of the Taklamakan Desert, where both dust emissions and DAOD increased; (2) increased dust emissions from the Saryesik-Atyrau Basin in eastern Kazakhstan resulted in higher DAOD over the southern Jungger Basin; (3) Taklamakan Desert emissions remained high across all seasons, sustaining elevated DAOD within the Tarim Basin; (4) MERRA-2 fails to capture anthropogenic dust emissions from cropland of the North China Plain, while the back-trajectory analysis based on measurements of particulate matter less than 10 μg/m3 in several cities across the North China and Northeast China suggests dust emissions from this region. This finding highlights the need to improve dust emission parameterizations in reanalysis models like MERRA-2, particularly when studying the impact of anthropogenic activities on dust emissions and air quality. Overall, the study provides essential references for managing the health and environmental impacts of dust aerosols in large densely populated cities of China.

The urban belt along the Yellow River in Ningxia, located in the middle and upper reaches of the Yellow River, serves as the population and economic center of Ningxia. Quantitatively analyzing the spatiotemporal distribution characteristics of the fraction of vegetation cover （FVC） in this region and its driving factors is of great significance for promoting ecological protection and the construction of a leading area for high-quality development in the Yellow River Basin. In this study, Landsat satellite remote sensing data were utilized to derive the vegetation cover from 2001 to 2020 in the cities along the Yellow River in Ningxia using a pixel-based binary model. The spatial pattern and spatiotemporal changes were analyzed. Additionally, meteorological data and topographic information for the same period in this region were combined. Sen+Mann-Kendall trend analysis, Hurst index, and parameter-optimized geographical detector models were used to analyze the driving factors. The results indicated： ① From 2001 to 2020, there was a significant overall increasing trend in vegetation cover in the urban belt along the Yellow River in Ningxia （P<0.01）, with a growth rate of 0.25% per annum. The 20-year average FVC was 33.38%, and the vegetation cover was at a relatively low level. In terms of spatial distribution, the vegetation was high in the northeast and low in the southwest, and the main types were very low vegetation coverage and low vegetation coverage. ② During the 20 years, the vegetation condition of the urban belt along the Yellow River in Ningxia had been significantly improved, and the portion of the area with improved vegetation cover accounted for 62.60%, which was much larger than that of the degraded area, and the average coefficient of variation of FVC was 0.098, which was good for the overall stability. ③ The area with H value of FVC less than 0.5 accounted for 66.15%, which showed strong anti-continuance, the area of FVC with improving trend accounted for 34.84%, the area of continuously stable and unchanged area accounted for 7.8%, the area with degrading trend accounted for 52.9%, and the future trend of FVC was uncertain in 9.0% of the area. ④ The analysis of driving factors revealed that land use type was the primary factor influencing the spatial distribution of vegetation cover in the urban belt along the Yellow River in Ningxia. The explanatory power （Q value） of interactions among various factors was higher than that of individual factors, demonstrating synergistic and nonlinear relationships among them, with no independent relationships. Risk detection showed that each driving factor had its appropriate range for impacting vegetation growth in the study area.

Accurate aerosol optical depth (AOD) with high temporal resolution is required for dynamic monitoring changes of aerosol properties. The Himawari-8 Advanced Himawari imager (AHI) data with 10-min temporal resolution has been widely used for AOD retrieval but usually involves radiative transfer models and assumptions on surface reflection and aerosol properties that are hard to satisfy. This paper introduces a data-driven algorithm without any priori assumptions on surface or aerosol properties called Time series retrieval of Multi-wavelength AOD by adapting Transformer (TMAT). TMAT directly retrieves AOD at eight-wavelengths every 10-min throughout the day, using AHI top of atmosphere (TOA) reflectances in 6 wavebands and ancillary data. TMAT explores diurnal temporal change patterns of AOD and surface reflection using time series data. TMAT was trained and evaluated using samples derived by matching a year (2019) of AHI full-disk data and AERONET/SONET observations with 12,768 diurnal time series, each of which includes up to 81 10-min 6-band AHI TOA observations and 81 (time) x 8 (wavelength) AOD values per day. There are up to 273,413 (<12,768 x 81) TOA observations in 6 wavebands and up to 1,420,716 (<12,768 x 81 x 8) AOD values in all the time series samples, with filled values (-9999) to represent no data due to cloud contamination and incomplete wavelength configurations of AOD stations. The masking mechanism in Transformer is leveraged to exclude the contribution of the AHI filled values in time series AOD retrieval and exclude the contribution of AOD filled values in training. TMAT was trained with a physical constraint that the retrieved AOD decreases toward longer wavelengths. TMAT AOD was evaluated by separating training and validation samples using site-specific leave-one-station-out validation. TMAT AOD shows extremely high accuracy at all wavelengths, with RMSE = 0.101, R = 0.949 and 81.69% of retrievals within +/- (0.05 + 15%*AODsites) for the AOD at 550 nm. We also validated the accuracy of the NASA multiangle implementation of atmospheric correction (MAIAC) AHI AOD over these sites with RMSE = 0.186, R = 0.832 and 52.10% of retrievals within +/-(0.05 + 15%*AODsites). The & Aring;ngstrom exponent (AE) derived from the multiwavelength TMAT AOD also shows good agreement with AERONET/SONET AE, with a correlation coefficient of 0.620 and an RMSE of 0.291. The TMAT effectiveness to capture diurnal temporal change patterns of surface reflection and AOD was evidenced by the significant reduction of AOD accuracy when using a non-time series deep learning method. The effectiveness of multi-wavelength joint retrieval was evidenced by the significant reduction of AE accuracy when using single wavelength retrieval models. Evidence shows the training and evaluation split methods have a large impact on evaluation accuracy and this study further used ground network

Abstract. Airborne dust aerosols impact negatively the climate, ecosystems, air quality, and human health. To mitigate these impacts, it is crucial to identify their three–dimensional spatiotemporal distribution, transport pathways and driving factors. In this study, the three–dimensional spatiotemporal variations and distribution of dust aerosols in China from 2007 to 2021 were first analyzed using multiple dust datasets, including Modern Era Retrospective Analysis for Research and Applications version 2 (MERRA–2) dust aerosol optical depth (DAOD) data, ultraviolet aerosol index (UVAI) data from the Ozone Monitoring Instrument (OMI), and the Vertical Feature Mask (VFM) product of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP). Also, the transport pathways and potential source regions for dust haze in principle provincial capital cities of West and North China in spring were identified using the Hybrid Single–Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Potential Source Contribution Function (PSCF). Additionally, DAOD variations over different land cover types and the impacts of meteorological driving factors were discussed with geographic detectors. Results indicate that: (1) The multi–year average of DAOD in China from 2007 to 2021 was 0.076. A mutation in annual average DAOD occurred between 2010 and 2011, with an insignificant increasing trend between 2007–2010, a downward trend between 2011–2021 and a significant downward trend between 2014–2017. (2) The Taklamakan Desert exhibited the highest DAOD for the entire China during spring throughout the years, with DAOD values ranging from 0.4 to 0.6 and UVAI values exceeding 2.0. The highest frequency of dust occurrence in the northwest and northern regions is at an altitude of 2–4 km in spring and summer, and of 0–2 km in autumn and winter, while it is at an altitude of 4–6 km for the Qinghai–Tibet region. (3) The dust transport routes for the provincial capital cities can be primarily divided into: western, northwestern, northern, southwestern, and local. Northwest cities are notably affected by dust from surrounding deserts. Dust originating from the Qaidam Basin and the Hexi Corridor can be carried further downwind to inland cities, such as Xining and Lanzhou. A dust backflow was found in Beijing and Tianjin. Moreover, the discussion revealed that barren and cropland had the highest DAOD and additionally precipitation, evaporation, and soil moisture were identified as the strongest driving factors affecting dust aerosol variations. The combination effect of precipitation and temperature had the highest explanatory power, ranging from 0.72–0.84, followed by 0.75–0.81 for precipitation and U10m wind speed and 0.67–0.75 for temperature and evaporation.

Dust detection is essential for environmental protection, climate change assessment, and human health issues. Based on the Fengyun-4A (FY-4A)/Advance Geostationary Radiation Imager (AGRI) images, this paper aimed to examine the performances of two classic dust detection algorithms (i.e., the brightness temperature difference (BTD) and normalized difference dust index (NDDI) thresholding algorithms) as well as two dust products (i.e., the infrared differential dust index (IDDI) and Dust Score products (DST) developed by the China Meteorological Administration). Results show that a threshold below −0.4 for BTD (11–12 µm) is appropriate for dust identification over China and that there is no fixed threshold for NDDI due to its limitations in distinguishing dust from bare ground. The IDDI and DST products presented similar results, where they are capable of detecting dust over all study areas only for daytime. A validation of these four dust detection algorithms has also been conducted with ground-based particulate matter (PM10) concentration measurements for the spring (March to May) of 2021. Results show that the average probability of correct detection (POCD) for BTD, NDDI, IDDI, and DST were 56.15%, 39.39%, 48.22%, and 46.75%, respectively. Overall, BTD performed the best on dust detection over China with its relative higher accuracy followed by IDDI and DST in the spring of 2021. A single threshold for NDDI led to a lower accuracy than those for others. Additionally, we integrated the BTD and IDDI algorithms for verification. The POFD after integration was only 56.17%, and the fusion algorithm had certain advantages over the single algorithm verification.

针对遥感影像目标检测中复杂背景的干扰,小目标检测效果差等问题,提出一种改进YOLOv5(you only look once v5)的遥感影像目标检测模型.针对卷积神经网络下采样导致的特征图中包含的小目标信息较少或消失的问题,引入特征复用以增加特征图中的小目标特征信息;在特征融合阶段时使用EMFFN(efficient multi-scale feature fusion network)的特征融合网络代替原有的PANet(path aggregation network),通过添加跳跃连接以及跨层连接高效融合不同尺度的特征图信息;为了应对复杂背景带来的检测效果变差的问题,提出了一种包含通道与像素的双向特征注意力机制(bidirectional feature attention mechanism,BFAM),以提高模型在复杂背景下的检测效果.实验结果表明,改进后的YOLOv5模型在DIOR数据集与RSOD数据集中分别取得了87.8%和96.6%的检测精度,相较原算法分别提高5.2和1.6个百分点,有效提高了复杂背景下的小目标检测精度.

The Advanced Geostationary Radiation Imager (AGRI) is one of the main imaging sensors on the Fengyun-4A (FY-4A) satellite. Due to the combination of high spatial and temporal resolution, the AGRI is suitable for continuously monitoring atmospheric aerosol. Existing studies only perform AOD retrieval on the dark target area of FY-4A/AGRI, and the full disk AOD retrieval is still under exploration. The Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) based on the Fully Connected Neural Network (FCNN) was used to retrieve FY-4A/AGRI full disk aerosol optical depth (AOD). The data from 111 ground-based Aerosol Robotic Network (AERONET) and Sun–Sky Radiometer Observation Network (SONET) sites were used to train the neural network, and the data from 28 other sites were used for independent validation. FY-4A/AGRI AOD data from 2017 to 2020 were validated over the full disk and three different surface types (vegetated areas, arid areas, and marine and coastal areas). For general validation, the AOD predicted by the application of NNAeroG to FY-4A/AGRI observations is consistent with the ground-based reference AOD data. The validation of the FY-4A/AGRI AOD versus the reference data set shows that the root-mean-square error (RMSE), mean absolute error (MAE), R squared (R2), and percentage of data with errors within the expected error ± (0.05 + 15%) (EE15) are 0.237, 0.145, 0.733, and 58.7%, respectively. The AOD retrieval accuracy over vegetated areas is high but there is potential for improvement of the results over arid areas and marine and coastal areas. AOD retrieval results of FY-4A/AGRI were compared under fine and coarse modes. The retrieved AOD has low accuracy in coarse mode but is better in coarse–fine mixed mode and fine mode. The current AOD products over the ocean of NNAeroG-FY4A/AGRI are not recommended. Further development of algorithms for marine areas is expected to improve the full disk AOD retrieval accuracy.

The 30 m resolution Landsat data have been used for high resolution aerosol optical depth (AOD) retrieval based on radiative transfer models. In this paper, a Landsat-8 AOD retrieval algorithm is proposed based on the deep neural network (DNN). A total of 6390 samples were obtained for model training and validation by collocating 8 years of Landsat-8 top of atmosphere (TOA) data and aerosol robotic network (AERONET) AOD data acquired from 329 AERONET stations over 30°W–160°E and 60°N–60°S. The Google Earth Engine (GEE) cloud-computing platform is used for the collocation to avoid a large download volume of Landsat data. Seventeen predictor variables were used to estimate AOD at 500 nm, including the seven bands TOA reflectance, two bands TOA brightness (BT), solar and viewing zenith and azimuth angles, scattering angle, digital elevation model (DEM), and the meteorological reanalysis total columnar water vapor and ozone concentration. The leave-one-station-out cross-validation showed that the estimated AOD agreed well with AERONET AOD with a correlation coefficient of 0.83, root-mean-square error of 0.15, and approximately 61% AOD retrievals within 0.05 + 20% of the AERONET AOD. Theoretical comparisons with the physical-based methods and the adaptation of the developed DNN method to Sentinel-2 TOA data with a different spectral band configuration are discussed.

High-frequency aerosol observation from a new-generation geostationary meteorological satellite is capable to capture and monitor the spatiotemporal dynamic variation of aerosols, which is of vital significance to environmental research and climate studies. Due to the diversity and complexity of land cover, it is a challenge to retrieve aerosol properties with high accuracy over land, especially over heterogeneous land surfaces. In this study, a geometry-discrete minimum reflectance aerosol retrieval algorithm (GeoMRA) has been proposed to retrieve 10-min high temporal resolution aerosol optical depth (AOD) datasets for geostationary Himawari-8 Advanced Himawari Imager (AHI) sensor, aiming at providing universal bidirectional reflectance distribution function (BRDF) descriptions for different land surfaces with different heterogeneous extent. The AOD retrievals from GeoMRA demonstrate good consistency against the ground-based AERONET measurements in East Asia from 2015 to 2020, with a correlation coefficient ( ${R}$ ) of 0.883 and approximately 65.6% of matchups falling within the expected error envelope of ±(0.05% +15%). Intercomparison between the GeoMRA retrieved AOD and other operational AOD products shows that the GeoMRA AOD retrievals, which generally possess similar spatial distribution and accuracy as Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products, have better performances than the Japan Aerospace Exploration Agency (JAXA) AOD products by providing more accurate AOD retrievals with higher spatial coverage. Moreover, the AOD bias analyses further demonstrate the robustness of GeoMRA algorithm, and an extreme haze event shows that the continuous GeoMRA AOD images illustrate smoother temporal variations than JAXA AOD products, demonstrating its efficacy and reliability in capturing the process of haze transport and monitoring the continuous spatiotemporal variation of aerosol. The above results suggest the considerable accuracy of GeoMRA algorithm for scientific application requirement and demonstrate the robustness of the proposed BRDF scheme in describing heterogeneous surfaces with diverse reflectance distribution.

高分四号(GF-4)是我国第一颗高分辨率对地静止卫星,地表反射率产品对于评估生态环境与减灾防灾具有重要价值.GF-4大气校正算法对地表反射率进行估计,迭代计算观测与计算表观反射率残差最小值得到气溶胶光学厚度,并构建6SV查找表对地表反射率结果进行计算.在精度验证基础上,考虑产品生产的高效率需求,对计算复杂度高的步骤基于图形处理器(GPU)进行内核设计,实现线程、寄存器等性能优化.研究结果表明,基于GPU加速的大气校正算法在性能与能耗上具有较大优势,一景GF-4 PMS的10240×10240像元数的影像数据,相比顺序执行取得57.3的总体加速比,而总体能耗仅为顺序执行的15.5％.

Due to the limitations in the number of satellites and the swath width of satellites (determined by the field of view and height of satellites), it is impossible to monitor global aerosol distribution using polar orbiting satellites at a high frequency. This limits the applicability of aerosol optical depth (AOD) data sets in many fields, such as atmospheric pollutant monitoring and climate change research, where a high-temporal data resolution may be required. Although geostationary satellites have a high-temporal resolution and an extensive observation range, three or more satellites are required to achieve global monitoring of aerosols. In this article, we obtain an hourly and global AOD data set by integrating AOD data sets from four geostationary weather satellites [Geostationary Operational Environmental Satellite (GOES-16), Meteosat Second Generation (MSG-1), MSG-4, and Himawari-8]. The integrated data set will expand the application range beyond the four individual AOD data sets. The integrated geostationary satellite AOD data sets from April to August 2018 were validated using Aerosol Robotic Network (AERONET) data. The data set results were validated against: the mean absolute error, mean bias error, relative mean bias, and root-mean-square error, and values obtained were 0.07, 0.01, 1.08, and 0.11, respectively. The ratio of the error of satellite retrieval within +/-( $0.05+ 0.2\times $ AOD(AERONET)) is 0.69. The spatial coverage and accuracy of the MODIS/C61/AOD product released by NASA were also analyzed as a representative of polar orbit satellites. The analysis results show that the integrated AOD data set has similar accuracy to that of the MODIS/AOD data set and has higher temporal resolution and spatial coverage than the MODIS/AOD data set.

In this study, an improved geographically and temporally weighted regression (IGTWR) model for the estimation of hourly PM2.5 concentration data was applied over central and eastern China in 2017, based on Himawari-8 Advanced Himawari Imager (AHI) data. A generalized distance based on the longitude, latitude, day, hour, and land use type was constructed. AHI aerosol optical depth, surface relative humidity, and boundary layer height (BLH) data were used as independent variables to retrieve the hourly PM2.5 concentrations at 1:00, 2:00, 3:00, 4:00, 5:00, 6:00, 7:00, and 8:00 UTC (Coordinated Universal Time). The model fitting and cross-validation performance were satisfactory. For the model fitting set, the correlation coefficient of determination (R2) between the measured and predicted PM2.5 concentrations was 0.886, and the root-mean-square error (RMSE) of 437,642 samples was only 12.18 µg/m3. The tenfold cross-validation results of the regression model were also acceptable; the correlation coefficient R2 of the measured and predicted results was 0.784, and the RMSE was 20.104 µg/m3, which is only 8 µg/m3 higher than that of the model fitting set. The spatial and temporal characteristics of the hourly PM2.5 concentration in 2017 were revealed. The model also achieved stable performance under haze and dust conditions.

Spectral aerosol optical depth (AOD) estimation from satellite-measured top of atmosphere (TOA) reflectances is challenging because of the complicated TOA-AOD relationship and a nexus of land surface and atmospheric state variations. This task is usually undertaken using a physical model to provide a first estimate of the TOA reflectances which are then optimized by comparison with the satellite data. Recently developed deep neural network (DNN) models provide a powerful tool to represent the complicated relationship statistically. This study presents a methodology based on DNN to estimate AOD using Himawari-8 Advanced Himawari Imager (AHI) TOA observations. A year (2017) of AHI TOA observations over the Himawari-8 full disk collocated in space and time with Aerosol Robotic Network (AERONET) AOD data were used to derive a total of 14,154 training and validation samples. The TOA reflectance in all six AHI solar bands, three TOA reflectance ratios derived based on the dark-target assumptions, sun-sensor geometry, and auxiliary data are used as predictors to estimate AOD at 500 nm. The DNN AOD is validated by separating training and validation samples using random k-fold cross-validation and using AERONET site-specific leave-one-station-out validation, and is compared with a random forest regression estimator and Japan Meteorological Agency (JMA) AOD. The DNN AOD shows high accuracy: (1) RMSE = 0.094, R-2 = 0.915 for k-fold cross-validation, and (2) RMSE = 0.172, R-2 = 0.730 for leave-one-station-out validation. The k-fold cross-validation overestimates the DNN accuracy as the training and validation samples may come from the same AHI pixel location. The leave-one-station-out validation reflects the accuracy for large-area applications where there are no training samples for the pixel location to be estimated. The DNN AOD has better accuracy than the random forest AOD and JMA AOD. In addition, the contribution of the dark-target derived TOA ratio predictors is examined and confirmed, and the sensitivity to the DNN structure is discussed.

气溶胶对人体健康、大气环境及气候变化有着重要影响.利用中分辨率成像光谱仪(MODIS)气溶胶产品,分析了2003-2018年中国大陆地区气溶胶光学厚度(AOD)的时空分布规律和变化特点,探讨了AOD的年际和季节变化规律,重点研究不同区域之间气溶胶时空分布差异,并探讨其影响因素.中国大陆地区AOD整体较高,16年间整体AOD明显下降,尤其是中部及东部地区AOD下降明显,其中2011年达最大值,此后呈明显下降.从空间分布来看,AOD总体呈现西高东低的空间分布特点,气溶胶高值区主要分布在人口密度较大的华中、华东地区以及四川盆地;西北、西南地区的AOD相对较低.气溶胶分布呈明显的季节差异,春季最高,秋季最低,且不同区域由于不同的气溶胶源、地形、气象等因素呈现不一样的季节变化特点.研究了AOD与国内生产总值(GDP)、人口、城市建设用地及归一化植被指数(NDVI)等因素的相关关系,结果表明:AOD与GDP、建设用地面积呈中等相关,与人口总数、NDVI呈强相关.

Himawari-8, operated by the Japan Meteorological Agency (JMA), is a new generation geostationary satellite that provides remote sensing data to retrieve atmospheric aerosol optical depth (AOD) at high spatial (1 km) and high temporal (10 min) resolutions. The Geostationary- National Aeronautics and Space Administration (NASA) Earth exchange (GeoNEX) project recently adapted the multiangle implementation of atmospheric correction (MAIAC) algorithm, originally developed for joint retrieval of AOD and surface anisotropic reflectance with the moderate resolution imaging spectroradiometer (MODIS) data, to generate Earth monitoring products from the latest geostationary satellites including Himawari-8. This study evaluated the GeoNEX Himawari-8 ~1 km MAIAC AOD retrieved over all the aerosol robotic network (AERONET) sites between 6°N–30°N and 91°E–127°E. The corresponding JMA Himawari-8 AOD products were also evaluated for comparison. We only used cloud-free and the best quality satellite AOD retrievals and compiled a total of 16,532 MAIAC-AERONET and 21,737 JMA-AERONET contemporaneous pairs of AOD values for 2017. Statistical analyses showed that both MAIAC and JMA data are highly correlated with AERONET AOD, with the correlation coefficient (R) of ~0.77, and the root mean squared error (RMSE) of ~0.16. The absolute bias of MAIAC AOD (0.02 overestimation) appears smaller than that of the JMA AOD (0.05 underestimation). In comparison with the JMA data, the time series of MAIAC AOD were more consistent with AERONET AOD values and better capture the diurnal variations of the latter. The dependence of MAIAC AOD bias on scattering angles is also discussed.