INSAT-3D cloud microphysical product: retrieval and validation

The 6-channel Imager onboard the Indian geostationary satellite Indian National Satellite-3D (INSAT-3D) provides half-hourly multispectral images over the Indian monsoon region. The availability of shortwave infrared (SWIR) (1.6 μm) channel along with visible (0.6 μm) channel observations provide an opportunity to estimate cloud microphysical parameters (CMP) over the India and surrounding regions with high temporal frequency. In this paper, we describe the retrieval and validation of two important CMPs, i.e. cloud optical thickness (COT) and cloud effective radius (CER) over the ocean from INSAT-3D. This is the first time; a CMP product has been made available for INSAT-3D. We describe here the development of the forward model, based on a look-up-table (LUT) approach using Radiative transfer simulations. The inversion is carried out by selecting the vector (CMP) which provides the best match between the observed and simulated radiances. The present retrieval is limited to water clouds over ocean only. The retrieved INSAT-3D CMP were then compared with MODerate Resolution Imaging Spectroradiometer (MODIS) product for the months of January and July 2016. For cloudy month (July), the mean correlation between INSAT-3D and MODIS was 0.73 and 0.47 for COT and CER, respectively. Similarly, for the month of January with less cloud cover, the mean correlation was 0.60 and 0.40 for COT and CER, respectively. INSAT-3D products are available every half hourly in real time through web portal www.mosdac.gov.in and will be valuable for studying short-term variation in cloud-microphysics over the equatorial Indian Ocean.     

Comparison and evaluation of dust detection algorithms using MODIS Aqua/Terra Level 1B data and MODIS/OMI dust products in the Middle East

Dust storms have a major impact on air quality, economic loss, and human health over large regions of the Middle East. Because of the broad extent of dust storms and also political–security issues in this region, satellite data are an important source of dust detection and mapping. The aim of this study was to compare and evaluate the performance of five main dust detection algorithms, including Ackerman, Miller, normalized difference dust index (NDDI), Roskovensky and Liou, and thermal-infrared dust index (TDI), using MODIS Level 1B and also MODIS Deep Blue AOD and OMI AI products in two dust events originating from Iraq and Saudi Arabia. Overall, results showed that the performance of the algorithms varied from event to event and it was not possible to use the published dust/no-dust thresholds for the algorithms tested in the study area. The MODIS AOD and OMI AI products were very effective for initial dust detection and the AOD and AI images correlated highly with the dust images at provincial scale (p-value <0.001), but the application of these products was limited at local scale due to their poor spatial resolution. Results also indicated that algorithms based on MODIS thermal infrared (TIR) bands or a combination of TIR and reflectance bands were better indicators of dust than reflectance-based ones. Among the TIR- based algorithms, TDI performed the best over water surfaces and dust sources, and accounted for approximately 93% and 90% of variations in the AOD and OMI AI data.     

Detection of Asia dust storms using multisensor satellite measurements

Observations from visible, infrared and microwave satellite instruments are integrated to detect dust storm over northwestern China. Microwave measurements are used to detect the dust storm underneath ice clouds, while visible and infrared measurements are utilized for delineating the cloud-free dust systems. Detection is based on microwave polarized brightness temperature differences (ΔT_b = T_bv − T_bh) among two channels of 89 GHz and 23.8 GHz and infrared brightness temperature difference (BTD) between channels at 11 and 12 μm. It is shown that the integrated approach is better than the method solely based on infrared BTD in storm detection, especially for those dust systems covered by ice clouds. This approach is applied for the Asia dust storms cases using the data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer (AMSR-E) onboard Aqua satellite.     

Retrieval of land surface albedo and temperature using data from the Indian geostationary satellite: a case study for the winter months

The shortwave and longwave radiation budget at land surfaces is largely dependent on two fundamental quantities, the albedo and the land surface temperature (LST). A time series (November 2005 to March 2006) of daily data from the Indian geostationary satellite Kalpana‐1 Very High Resolution Radiometer (K1VHRR) sensor in the visible (VIS), water vapour (WV) and thermal infrared (TIR) bands from noontime (0900 GMT) observations were processed to retrieve these quantities in clear skies for five winter months. Cloud detection was carried out using bispectral threshold tests (in both VIS and TIR bands) in a dekadal time series. Surface albedo was retrieved using a simple atmospheric transmission model. K1VHRR albedo was compared with Moderate Resolution Imaging Spectroradiometer (MODIS) AQUA noontime albedo over different land targets (agriculture, forest, desert, scrub and snow) that showed minimum differences over agriculture and forest. The comparison of spatial albedo over different landscapes yielded a root mean square deviation (RMSD) of 0.021 in VHRR albedo (9% of MODIS albedo). A mono‐window algorithm was implemented with a single TIR band to retrieve the LST. Its accuracy was also verified over different land targets by comparison with aggregated MODIS AQUA LST. The maximum RMSD was obtained over agriculture. Spatial comparison of VHRR and AQUA LSTs over homogeneous and heterogeneous landscape cutouts revealed an overall RMSD of 2.3 K. An improvement in the retrieval accuracy is expected to be achieved with atmospheric products from the sounder and split thermal bands in the imager of future INSAT 3D missions.     

应用米氏理论选择气象卫星探测沙尘暴的波段

无论从时间尺度还是从空间尺度来看，气象卫星都是监测沙尘天气的重要手段。为了使气象卫星能在监测和预测沙尘暴中最大限度地发挥其作用，探测波段的选择是十分重要的。根据米氏理论对沙尘气溶胶的散射和消光等特性进行了计算，并根据其消光作用对气象卫星探测波段的选择作了分析。通过计算得出可见光波段是探测沙尘气溶胶的主要通道，短波红外波段对沙尘气溶胶也很敏感。同时，由于沙尘粒子对不同波段的吸收也不同，可采用不同红外探测波段的辐射亮温来综合判别沙尘区。     

应用米氏理论选择气象卫星探测沙尘暴的波段

无论从时间尺度还是从空间尺度来看,气象卫星都是监测沙尘天气的重要手段。为了使气象卫星能在监测和预测沙尘暴中最大限度地发挥其作用,探测波段的选择是十分重要的。根据米氏理论对沙尘气溶胶的散射和消光等特性进行了计算,并根据其消光作用对气象卫星探测波段的选择作了分析。通过计算得出可见光波段是探测沙尘气溶胶的主要通道,短波红外波段对沙尘气溶胶也很敏感。同时,由于沙尘粒子对不同波段的吸收也不同,可采用不同红外探测波段的辐射亮温来综合判别沙尘区。     

黑河综合遥感联合试验中机载WiDAS数据的预处理方法

机载红外广角双模式成像仪(Wide-angle Infrared Dual-mode line/area Array Scanner\|WiDAS)是黑河综合遥感联合试验中的主要机载传感器之一,它通过可见光到热红外波段的广角成像,获取地表二向反射和热辐射方向性信息,介绍了WiDAS数据的预处理关键算法及关键参数。WiDAS传感器的可见近红外波段与中红外、热红外波段在探测器性能、空间分辨率和目标辐射特性方面都有显著差异,因此它们的预处理具有不同的算法。可见近红外波段的CCD相机用积分球定标,用简单的多项式形变函数进行波段间配准,用6S模型和实测气溶胶光学厚度进行大气校正。中红外、热红外波段的红外相机则用面元黑体定标,波段配准中采用了复合的形变函数和较为复杂的配准算法,大气校正采用MODTRAN模型和实测大气廓线。还介绍了从WiDAS标准预处理产品中提取目标的多角度观测的方法,这些信息为使用WiDAS数据产品开展定量遥感研究和应用提供参考。     

Inter-comparison of INSAT-3D atmospheric motion vectors height with cloud-base height from a Ceilometer

ABSTRACT

The atmospheric motion vectors (AMV) are derived by tracking cloud and moisture features in the subsequent images of geostationary as well as polar satellites. The heights of the AMVs are nothing but the height of cloud tracers used during the retrieval process for tracking. This height is derived using different complex techniques. In this study, a detailed comparison has been performed with the use of ground-based cloud-base height (CBH) measurements from ceilometer CL31, installed at Ahmedabad (23.03°N, 72.54°E), India and height assigned to AMVs which are retrieved from INSAT-3D satellite images. Six months CBH measurement over Ahmedabad from ceilometer CL31 has been used to inter-compare the co-located AMV heights. Although both ground-based and satellite-based techniques have their own limitations, however, it is found from this study that the ceilometer is an excellent instrument to precisely detect low- and mid-level clouds and height-assignments technique of AMVs retrieved from INSAT-3D satellite provides all high-, mid- and low-levels cloud information over this region. As an example, it is found that AMVs height of INSAT-3D is about 867.92, 750.00 and 465.09 hPa on 26 May 2014, 7 July 2014 and 29 October 2014, respectively, which matches very closely with ceilometer-measured CBH of about 873.15, 769.16 and 507.44 hPa, respectively. However, in case multi-level clouds present on rainy days, CBH measurements from ceilometer are differing from INSAT-3D AMV cloud tracer heights.     

Technical Note: Dust storms in China decreased during the last 50 years

There are many deserts and dust storms in northwest China. Some researches showed that the speed of desertification of China increased during the last 50 years. This should provide more source materials to dust storm, while in fact the dust storm frequency decreased. Our analysis considered that this is partly due to the global warming, especially the warm of Tibet Plateau which lead to the decrease of Asia monsoon, and then lead to the dust storm frequency decrease.     

Validation of INSAT-3D temperature and moisture sounding retrievals using matched radiosonde measurements

An evaluation of temperature and moisture profiles retrieved from a geostationary Indian National Satellite (INSAT-3D) sounder, launched in 2013, is performed against collocated radiosonde (RAOB) observation measurements of more than 1 year. This evaluation is carried out in terms of bias and root mean square error (RMSE) in temperature and relative humidity. An error analysis is carried out for different surface types, different seasons and day/night cases. The key finding of this study is that INSAT-3D retrievals show good agreement with RAOB measurements with overall RMSE accuracies ~1–2 K and 10–20%, respectively, for temperature and relative humidity in the troposphere. However, the temperature and relative humidity retrievals over land or in dry atmosphere show degraded performance. This degradation might be related to uncertainty in surface emissivity over land and possibility of undetected cloud in dry atmospheric condition. In addition to it, a similar analysis is carried out to assess the relative performance of INSAT-3D-retrieved profiles, Atmospheric Infrared Sounder (AIRS) L2 Standard Physical Retrieval (AIRS-only) version 6 (AIRS2RET) profiles and European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim) reanalysis with respect to spatially and temporally collocated RAOBs. In this analysis, temperature and moisture profiles from RAOBs serve as reference measurements and all retrievals and ERA-interim are compared with RAOBs. AIRS and INSAT-3D temperature retrievals gave comparable accuracies in upper and lower troposphere where as the quality degrades in middle troposphere resulting in larger errors. This may be due to improper bias correction coefficients used for brightness temperature of clear sky pixels before physical retrievals. In case of relative humidity, INSAT-3D profiles have comparable accuracies as AIRS in troposphere.     

MODIS satellite data evaluation for sand and dust storm monitoring in Saudi Arabia

ABSTRACT

The impacts of wind-blown desert sand and dust are a major concern of environmental and climate study due to their global extent. This article investigates the sand and dust storms detection in Saudi Arabia using Moderate Resolution Imaging Spectroradiometer (MODIS) data, both from Terra and Aqua satellite systems for the years 2002–2011. Normalized Difference Dust Index (NDDI) is applied for the detection of sand and dust storms whilst MODIS band 31 is applied to discriminate atmospheric sand and dust from that present on the ground. In addition, the data from Meteosat satellite, AERONET station, and meteorological stations are used to validate NDDI-based sand and dust storm events. The results of the study show that NDDI can successfully identify and differentiate sand and dust storms from clouds whilst MODIS band 31 can discriminate aerial and surface sand and dust over Saudi Arabia. The results also show that the multi-source data, that is MODIS, Meteosat, AERONET, and meteorological stations, can be very valuable for tracking sand and dust storm events. As no such attempt in the past has been made in Saudi Arabia, it is envisaged that the results of this study will be helpful in planning remote-sensing data for the climate change study in the region.     

Synthetic retrieval of hourly net ecosystem exchange using the neural network model with combined MI and GOCI geostationary sensor datasets and ground-based measurements

Net ecosystem carbon dioxide (CO₂) exchange (NEE) is a key parameter for understanding the terrestrial plant ecosystems, but it is difficult to monitor or predict over large areas at fine temporal resolutions. In this research, we estimated the hourly NEE using a combination of the integrated neural network (NN) model with geostationary satellite imagery to overcome the limitations of existing daily polar orbiting satellite-derived carbon flux products. Two sets of satellite imageries (i.e. the meteorological imager (MI) and geostationary ocean colour imager (GOCI) aboard communication, ocean, and meteorological satellite (COMS)) and CO₂ flux data derived from eddy covariance measurements were used to verify the feasibility of applying hourly geostationary satellite imagery with an NN-based approach for estimating NEE at high temporal resolutions. For the NN model, the optimum neuronal architecture was established using an NN with one hidden layer that was trained using the Levenberg–Marquardt back propagation algorithm. The hourly NEE values estimated in test period from the NN model using the combined COMS MI and GOCI imagery and ground measurements as model inputs were compared with the eddy covariance NEE values from the measurement tower, which yielded reliable statistical agreement. The hourly NEE results from the NN model based on COMS MI and GOCI imagery and ground measurement data had the highest accuracy (RMSE = 2.026 μmol m^?2 s^?2, R = 0.975), while the root mean square error (RMSE) and the regression coefficient (R) generated by the NN model based on satellite imagery as the sole input variable were relatively lower (RMSE = 3.230 μmol m^?2 s^?2, R = 0.952). Although the simulations for the satellite-only NEE were showed as lower accuracy than the NN model that included all input variables, the hourly variations in NEE also appeared to describe its daily growth and development pattern well, indicating the possibility of deriving hourly-based products from the proposed NN model using geostationary satellite data as inputs.     

Dust storm detection and monitoring using multi-temporal INSAT-3A-CCD data

Sand and dust on being agitated by winds often trigger huge dust storms which are characterized by high aerosol optical depth (AOD), τ_a, lower Angström exponent (α) and near-zero visibility condition. Geostationary platforms are most suitable for studying the dynamic behaviour of such events. In the present study we have used multi-temporal Indian National Satellite System (INSAT-3A-CCD) data on an hourly basis to characterize a massive dust storm that occurred during 15–16 October 2008 over the northern Arabian Sea. An algorithm has been developed to estimate AOD and the Angström exponent using the near-infrared (NIR) channel of INSAT-3A-CCD. AOD at 550 nm and the Angström exponent, α(810, 550 nm) were computed during 14–18 October 2008. The mean value of τ_a(550 nm) was found to be 1.03 during the dust event, almost two to three times higher than in dust-free conditions. Similarly, the mean α(810, 550 nm) value reduced almost by half during the dust event, indicating the presence of larger particles. In general an increasing trend of AOD values was noticed till early afternoon and a decreasing trend was observed thereafter. INSAT-derived atmospheric parameters were compared with Moderate Resolution Imaging Spectroradiometer-derived AOD and α for similar observation period. A good correlation for τ_a was found with R ²?=?0.92 and root mean square error (RMSE)?=?0.054. However, a R ² value of 0.74 with RMSE of 0.13 was obtained for α. Analysis of air mass back-trajectory indicates the source of this dust event originates from deserts of Afghanistan and Pakistan.     

Land surface emissivity retrieval from combined mid-infrared and thermal infrared data of MSG-SEVIRI

This work addressed the retrieval of Land Surface Emissivity (LSE) from combined mid-infrared and thermal infrared data of Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) onboard the geostationary satellite—Meteosat Second Generation (MSG). To correct for the atmospheric effects in satellite measurements, a new atmospheric correction scheme was developed for both Middle Infra-Red (MIR) and Thermal Infra-Red (TIR) channels. For the MIR channel, because it is less sensitive to the change of water vapor content, the clear-sky and time-nearest European Centre for Median-range Weather Forecast (ECMWF) atmospheric data were used for the images where no atmospheric data are available. For TIR channels, a modified model of Diurnal Temperature Cycle (DTC) used by Göttsche and Olesen [Göttsche, F. M., and Olesen, F. S. (2001). Modeling of diurnal cycles of brightness temperature extracted from METEOSAT data. Remote Sensing of Environment, 76, 337-348.] and Schädlich et al. [Schädlich, S., Göttsche, F. M., and Olesen, F. S. (2001). Influence of land surface parameters and atmosphere on METEOSAT brightness Temperatures and generation of land surface temperature maps by temporally and spatially interpolating atmospheric correction. Remote Sensing of Environment, 75, 39-46.] was adopted. The separation of Land Surface Temperature (LST) and LSE is based on the concept of the Temperature Independent Spectral Indices (TISI) [Becker, F., and Li, Z. L. (1990a). Temperature independent spectral indices in thermal infrared bands. Remote Sensing of Environment, 32, 17-33.] constructed with one channel in MIR and one channel in TIR. The results of two different combinations (combination of channels 4 and 9 and of channels 4 and 10) and two successive days at six specific locations over North Africa show that the retrievals are consistent. The range of emissivity in MSG-SEVIRI channel 4 goes from 0.5 for bare areas to 0.96 for densely vegetated areas, whereas the emissivities in MSG-SEVIRI channels 9 and 10 are usually from 0.9 to 0.95 for bare areas and from 0.95 to 1.0 for vegetated areas. For densely vegetated areas, the emissivities in MSG-SEVIRI channel 9 are larger than the ones in channel 10, whereas the opposite is observed over bare areas. The rms differences between two combinations over the whole studied region are 0.017 for emissivity in channel 4, 0.008 for emissivity in channel 9 and 0.007 for emissivity in channel 10.     

Cloud fraction retrieval using data from Indian geostationary satellites and validation

ABSTRACT

Cloud fraction (CF) is known as the dominant modulator of Earth’s radiation budget, thus regarded as Essential Climate Variable. CF is retrieved using Indian geostationary satellites Kalpana-1 and Indian National Satellite System (INSAT-3D) by calculating the fraction of area covered by the clouds in a given pixel divided by the total area of the pixel. The technique uses multi-channel thresholding for three channels in Kalpana-1, that is, thermal, visible, and water vapour, and four channels in INSAT-3D with mid-infrared channel in addition to the three mentioned for Kalpana-1. A 2-year record of CF at 30-min intervals was generated for the Indian region using the Kalpana-1 and INSAT-3D data. The retrieved CF data were compared against Moderate Resolution Imaging Spectroradiometer (MODIS) CF product in the near vicinity of simultaneous data availability (i.e., within ±15 min interval). This product agrees with MODIS (correlation coefficient 80%) with a root mean square error (RMSE) of 0.30, in spite of ±15 min of time difference between both the satellites. In addition, ground-based Total Sky Imager (TSI-440) retrieved data over Pune is used to validate the satellite retrieved CF over the same region. The probability of detection between retrieved CF and ground-based data is relatively more for range of CF between 0.00 and 0.25, that is, 90% and more than 20% for CF greater than 0.50. In view of the close agreement between retrieved CF from Kalpana-1 and INSAT-3D with MODIS and TSI-440, this product is operational and is being made available through National Information System for Climate and Environment Studies portal for use in better understanding of climate.     

Satellite‐based precipitation estimation using watershed segmentation and growing hierarchical self‐organizing map

This paper outlines the development of a multi‐satellite precipitation estimation methodology that draws on techniques from machine learning and morphology to produce high‐resolution, short‐duration rainfall estimates in an automated fashion. First, cloud systems are identified from geostationary infrared imagery using morphology based watershed segmentation algorithm. Second, a novel pattern recognition technique, growing hierarchical self‐organizing map (GHSOM), is used to classify clouds into a number of clusters with hierarchical architecture. Finally, each cloud cluster is associated with co‐registered passive microwave rainfall observations through a cumulative histogram matching approach. The network was initially trained using remotely sensed geostationary infrared satellite imagery and hourly ground‐radar data in lieu of a dense constellation of polar‐orbiting spacecraft such as the proposed global precipitation measurement (GPM) mission. Ground‐radar and gauge rainfall measurements were used to evaluate this technique for both warm (June 2004) and cold seasons (December 2004–February 2005) at various temporal (daily and monthly) and spatial (0.04° and 0.25°) scales. Significant improvements of estimation accuracy are found classifying the clouds into hierarchical sub‐layers rather than a single layer. Furthermore, 2‐year (2003–2004) satellite rainfall estimates generated by the current algorithm were compared with gauge‐corrected Stage IV radar rainfall at various time scales over continental United States. This study demonstrates the usefulness of the watershed segmentation and the GHSOM in satellite‐based rainfall estimations.     

Evaluation and assimilation of various satellite-derived rainfall products over India

Accurate prediction of rainfall from the numerical weather prediction model is one of the major objectives over tropical regions. In this study, four different satellite-derived rainfall products (viz. merged-rainfall product from TRMM (Tropical Rainfall Measuring Mission) 3B42 and IMERG (Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement)), and Indian meteorological satellite INSAT-3D retrieved HEM (Hydro-Estimator Method) and IMSRA (INSAT Multi-Spectral Rainfall Algorithm) rainfall) are assimilated in the Weather Research and Forecasting (WRF) model using variational method. Before assimilation of satellite retrieved rainfall product in the WRF model, selected rainfall products are compared with ground rainfall from India Meteorological Department during Indian summer monsoon (June–September) 2015. Preliminary validation results show root-mean-square-difference (mean difference) of 18.1 (2.1), 21.3 (2.1), 15.4 (?0.72), and 14.4 (0.5) mm day^?1 in IMSRA, HEM, IMERG, and TRMM 3B42 rainfall, respectively. Further, the four-dimensional variational data assimilation method is used daily to assimilate selected rainfall products in the WRF model during the entire month of August 2015. Results suggest that assimilation of satellite rainfall improved the WRF model analyses and subsequent temperature and moisture forecasts. Moreover, rainfall prediction is also improved with the maximum positive impact from TRMM rainfall assimilation followed by IMERG rainfall assimilation. Similar nature of improvements is also seen in rainfall prediction when INSAT-3D retrieved rainfall products (HEM and IMSRA) are used for assimilation.     

面向减灾应用的光学成像静止卫星能力分析

静止卫星凭借其宽覆盖、高时效、机动灵活的特点,在国家减灾救灾业务中有着独特的应用优势。在回顾国内外静止卫星发展的基础上,总结其光学成像传感器在灾害监测预警中的应用现状和存在的问题。基于静止卫星及其载荷现有研制基础,从灾害管理各阶段应用需求出发,分析光学成像静止卫星在减灾中的应用潜力。根据减灾救灾实际业务和应用需求,分别从卫星工作模式、观测频次以及光学图像的几何性能、光谱范围、辐射性能等5个方面提出对静止卫星的技术指标要求,为推动我国的防灾减灾光学成像静止卫星建设提供借鉴。     

基于GOCI卫星的大气细颗粒物PM2.5的遥感反演及其时空分布规律研究

随着我国经济的快速发展和城市化进程的加快,大气细颗粒物PM_2.5已经成为影响我国大气环境污染的主要因素之一。利用静止卫星数据可以获取大范围的面状PM_2.5信息,为我国大气环境的监测、治理、预测等提供了不可替代的数据源。以江苏省为研究区,利用静止卫星GOCI数据,在反演逐时气溶胶光学厚度（AOD）的基础上,结合气象因子,利用多元统计分析进行了研究区PM_2.5的遥感反演研究。结果表明：基于AOD的多元统计模型,在估计的PM_2.5浓度和观测值之间表现出良好的一致性,拟合度R ²为0.665 2。在对AOD进行湿度订正后得到的dry AOD进行多元统计建模,预测的PM_2.5浓度与观测值之间的拟合度R ²达到了0.702 6,证明了经过湿度订正后的“干”AOD与PM_2.5之间建立的关系更加可靠。使用GOCI反演的AOD计算PM_2.5浓度,在空间分辨率和时间分辨率上充分体现了GOCI作为静止卫星监测PM_2.5的优势。在空间分分辨率上,基于GOCI卫星获取AOD的空间分辨率为500 m,优于MODIS 10 km的AOD产品;时间分辨率上,基于GOCI获取AOD实现每日自9:00~16:00逐小时监测,优于MODIS每日两次的AOD产品。     

Remote Sensing Retrieval of Atmospheric Fine Particle PM2.5 based on GOCI Satellite and Its Temporal and Spatial Distribution

With the rapid development of China's economy and the acceleration of urbanization, PM_2.5 has become one of the major factors affecting atmospheric environmental pollution in China. The use of geostationary satellite data can obtain a wide range of regional PM_2.5information, providing irreplaceable data sources for China's atmospheric environment monitoring, control, and forecasting. This paper uses the geostationary satellite GOCI data, based on Aerosol Optical Depth (AOD) retrieveal, combined with meteorological factors, and uses multivariate statistical analysis to study the remote sensing retrieval of PM_2.5 in the study area. The results show that the multivariate statistical model based on AOD shows a good agreement between the estimated PM_2.5 concentration and the observed values, and the fitting degreeR ² is 0.665 2. After multivariate statistical modeling of dry AOD obtained after moisture correction of AOD, the fitting degree R² between the predicted concentration of PM_2.5 and the observed value reached 0.702 6, which proved the relationship established betweenthe “dry” AOD after the humidity correction and PM_2.5 is more reliable.The use of GOCI-retrieved AOD to calculate PM_2.5 concentration fully reflects the advantages of GOCI as a geostationary satellite in spatial resolution and temporal resolution. In terms of spatial resolution, the spatial resolution of AOD based on GOCI satellite reachs to 500 meters, which is better than MODIS 10 km AOD product.In terms of temporal resolution,hourly AOD monitoring from 9:00 to 16:00 based on GOCI can be obtained,which is better than MODIS twice daily AOD products.