Aerosol is one of the important factors leading to atmospheric pollution, but there are still relatively few studies on the variation of AOD at high resolution, which brings some influence on the study of the variation of small-scale aerosol such as cities. Based on Sentinel-2 Multi-Spectral Instrument (MSI) data, aerosol inversion is carried out by using structure function method and 6SV model, and the AOD inversion results with 500m resolution in Beijing urban area are obtained. The results show that the pixel interval has an obvious impact on aerosol inversion. Using the structure function method and the distance value of 15, the AOD accuracy of B07 band is the best, but the accuracy of other bands is poor, so the more data need to be collected later to improve and verify the algorithm.

The Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km aerosol product based on the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has great potential in understanding the interaction between human activities and the atmospheric environment. In this paper, the MODIS 1 km aerosol product over China during the Coronavirus Disease 2019 (COVID-19) pandemic was validated against with the ground measured data collected from the Aerosol Robotic Network (AERONET). The result shows a good agreement between the two datasets. The spatiotemporal analyses of three selected regions, which are Beijing-Tianjin-Hebei, Hubei and Guangdong-Hong Kong-Macao, indicate that the COVID-19 pandemic has a significant impact on human activities and aerosol loadings.

The Coronavirus Disease 2019 (COVID-19) pandemic, which has lasted for more than two years, has had a huge impact on human health and the global economy, as well as the ecological environment. In this study, the variations of atmospheric environment over China from 2019 to 2020 were calculated and analyzed based on the measured total columns of ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2) and aerosol optical depth (AOD) from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura satellite. The study shows the impact of the epidemic prevention and control measures and the resumption of work and production on atmospheric environment, and demonstrates that satellite remote sensing can play an important role in the monitoring of the COVID-19 pandemic, especially its impact on atmospheric environment.

Satellite remote sensing has advantages in monitoring environmental changes during the global pandemics such as the Severe Acute Respiratory Syndrome Coronavirus (SARS) and the Corona Virus Disease 2019 (COVID-19). In this paper, the variations of atmospheric environment during SARS and COVID-19 pandemics were calculated and analyzed based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Atmosphere Monthly Global Product. Preliminary results show that: (1) aerosol optical depth is most affected by the pandemics, especially the duration and prevention and control measures; (2) the correlations between the variables of aerosol optical depth, cloud fraction, total column ozone and precipitable water vapor were not very strong during the two pandemics.

The long-term satellite products including MODIS AOD, GOME-2 NO2and OMI O3were collected and processed respectively, and were verified by AErosol RObotic NETwork (AERONET) ground measurements. Then the time series data in eastern China were plotted and analyzed. The results show that MODIS AOD and OMI O3have high correlation with AERONET measurements, the correlated coefficients are 0.81 and 0.94 respectively. The GOME-2 NO2inversion algorithm has lower accuracy which the correlated coefficient (R) is 0.71. All the datasets have seasonal cycles, the maximum values of AOD and trop O3appear in summer, while the NO2appears in winter. After 2013, the value of all satellite products is in a downward trend, which may be closely related to the implementation of the “Ten Atmospheric Regulations” in 2013, and human activities and economic development level will affect the distribution of AOD and trop NO2.

Aerosols have been proven to be associated with the Coronavirus Disease (COVID-19). Satellite-derived aerosol optical depth (AOD) at a medium or high resolution e.g. 20 m may provide a new perspective for public health study, especially controlling the spread of COVID-19. Based on the Sentinel-2 MSI data, this paper uses the structure function method and the 6SV model to perform aerosol retrieval, and obtains the AOD retrieval result with a resolution of 20m in the study area. The results show that the distribution of surface features has important influence and correlation on the retrieval results, and the pixel interval, aerosol model and atmospheric model have obvious influence on aerosol retrieval. Moreover, the AOD inversion value is relatively higher and the error is larger, so the more data need to be collected later to improve and verify the algorithm.

Based on Himawari-8 hourly AOD product, meteorological variables, auxiliary data and PM2.5 observations, this study established a random forest (RF) model to estimate the PM2.5 concentration in China in 2018. The results showed that the estimated PM2.5 has high consistency with the ground observations. Ten-fold cross-validation (CV) result showed that the model has high accuracy, with R2 of 0.801, RMSE of$15.677\mu\mathrm{g}\cdot\mathrm{m}^{-3}$In areas with sparse sites, there are fewer ground observation data, resulting in fewer training samples and poor model accuracy. During the day, the RF model performed best around noon due to the meteorological observation conditions. Seasonally, the RF model has the highest fit goodness in autumn and the lowest in summer. In addition, the model has the lowest RMSE in summer. Result of the model prediction showed that the spatial distribution of PM2.5 prediction and PM2.5 observation are relatively consistent. The high values in PM2.5 are mainly concentrated in Jiangsu, Shandong, Henan and Anhui, southeast of Beijing-Tianjin-Hebei and east of Sichuan Province.

In this study, ChinaHighPM2.5 data sets were used for trend analysis of PM2.5 concentration in China and its five typical regions after the issue of “Air Pollution Prevention and Control Action Plan”. The result shows that the average PM2.5 in the five selected regions from 2014 to 2019 was higher than that of China. China's PM2.5 showed a significant downward trend from 2014 to 2019, with a year on year growth rate of -43.57%. Beijing-Tianjin-Hebei and Chengdu-Chongqing City Group had the most significant decline in PM2.5 concentration, with that of -46.63% and - 53.05% respectively, both higher than the national average. Except for Taklimakan Desert Region, the PM2.5 in other regions showed a clear downward trend and was significant negatively correlated with the year. In addition, the regional difference in PM2.5 concentration decreased with the year, as well as concentration interval.

Due to the Coronavirus Disease (COVID-19) pandemic, the human activities in China and even in the world were reduced in 2020, which also caused the variation of the atmospheric environment, especially atmospheric aerosol emissions. In this paper, the MODIS level-3 gridded atmosphere monthly global joint product in 2019 and 2020 were collected and processed. After preliminary analysis, we found that MODIS annual aerosol optical depth (AOD) over China in 2020 is generally lower than in 2019. In some regions such as Beijing-Tianjin-Hebei and Yangtze River Delta, AOD values dropped the most in February. However, in some months and regions, AOD in 2020 is even higher than in 2019. More studies are still ongoing.

Affected by the Coronavirus Disease (COVID-19) pandemic, almost all students in China have to study online at home from February to June, 2020. In this paper, we discussed the forms of online courses and took Jiangsu Normal University as an example to introduce the online courses of remote sensing in China. The results of the satisfaction survey show that more than 90% of the respondents agree with online courses and believe that online courses can at least meet basic learning needs in the age of COVID-19, and more than 60% of respondents claimed that they had met or exceeded their learning expectations. The major advantages of online course include reducing the gathering of people and thus the risk of infection. However, there are still some problems with online courses, and we hope that these problems can be solved well in the future.

In this paper, an algorithm of the land surface bidirectional reflectance distribution function (BRDF) and albedo parameters inversion is proposed based on a simplified atmospheric radiative transfer model coupling with the BRDF model. The algorithm is applied to the Sentinel-2 Multi-Spectral Instrument (MSI) data. To validate the inversion, the BRDF/ albedo parameters derived from the MODerate Resolution Imaging Spectroradiometer (MODIS) were collected and resampled to match the MSI data. The preliminary validation shows a good consistency between the two datasets with the correlative coefficient (R) greater than 0.8 and the root-mean-square error (RMSE) less than 0.05 for the red and near infrared bands. The advantage of this algorithm is that the spatial resolution of the satellite-derived BRDF/albedo parameters can be improved as high as 20 m, which has great potential in quantitative remote sensing applications.

In this study, the ground measured Aerosol Optical Depth (AOD) at 500nm at ten sites of the AErosol RObotic NETwork (AERONET) in China were collected to evaluate the AOD retrieved from the observations by the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite. The results show the AHI aerosol products have great uniformity with ground measurements with correlated coefficient (R) of the two datasets is 0.808, and the Mean Absolute Error (MAE), Root-Mean-Square Error (RMSE) and Relative Mean Bias (RMB) values are 0.152, 0.045 and 1.107 respectively. From a spatial perspective, the AOD retrievals are overestimated at 50% of the sites selected in this study, especially in northwest and north China. In terms of time, summer has the best performance (correlation coefficient) followed by autumn winter, and spring. March, October and November has the relatively poor performance in a 12-month period, while the best performance appeared in June.

In China, for many general colleges and universities, the postgraduate programs related to remote sensing are the subdisciplines named Photogrammetry and Remote Sensing, and Geographical Environment Remote Sensing. Totally, there are 125 and 67 colleges and universities which offer the related programs with the master's and doctoral degrees, respectively. In this paper, we introduce the objective and requirement, as well as the training mode of the related postgraudate programs. After years of development, the remote sensing education in China has ranked among the best in the world. In the future, encouraged by a series of policies and measures of the Ministry of Education of China, the remote sensing education will develop faster and better, and make greater contributions to China and the world.

In order to control the air pollution in China, the Chinese government issued an air pollution control action plan in Sep. 2013. The goal of the first phase of the plan is by 2017, the air quality was to improve significantly compared to 2012. In this study, the satellite aerosol product derived from the MODerate-resolution Imaging Spectroradiometer (MODIS) over East China were collected and processed to evaluate the effectiveness of the plan. The results show that the mean aerosol optical depth (AOD) over East China in 2017 has reduced about 23.17% compared to 2012, and the trends of the MODIS AOD during the period from 2010 to 2019 over the domain is -0.012 per year, which indicates that the air pollution plan has been well implemented to achieve the expected goals.

In this study, the ground measured nitrogen dioxide (NO2) column at six sites of the AErosol RObotic NETwork (AERONET) in China were collected to evaluate the Global Ozone Monitoring Experiment-2 (GOME-2) tropospheric NO2product. The results show the correlated coefficient of the two datasets is 0.851. The long temporal variations and spatial distributions of the GOME-2 tropospheric (trop) NO2in the heating season (Nov, Dec, Jan and Feb) and the non-heating season over China were plotted. The results indicate that in the heating season, the trop NO2column values in the North and East China are much higher in the non-heating seasons. Although the trends of the trop NO2column in the non-heating season from 2013 to 2017 are significantly decreased, the variations in the heating are not significant. It proved that the coal heating can have a great impact on trop NO2column variations in China.

In China, Remote Sensing Science and Technology (RSST) is a young undergraduate program offered by the colleges and universities since 2001. As of 2018, there are as many as 45 colleges and universities authorized to offer the programs and to train the undergraduate students in the field of remote sensing and related applications. To ensure the smooth implementation of the undergraduate education, the Ministry of Education of the People's Republic of China has issued a series of policies and measures, including the program objective, requirements, and main curriculum structure. In Jiangsu Normal University, following the relevant policies and regulations, the students in the RSST program are trained and classified as three modules: innovative, compound and international talents, to meet the kinds of interests of the students and the different needs of employers.

In this study, the Sentinel-2A Bottom Of Atmosphere (BOA) reflectance imageries over Beijing area from Dec 2016 to Nov 2017 were generated by the Sen2Cor processor and evaluated using the AERONET measurements and the 6SV code. The result shows a poor correlation between the Sen2Cor retrieved AOT and the ground data with R = 0.289 possibly because of the limitation of the AOT algorithm. The errors of Sen2Cor retrieved AOT and water vapor pressure have a great effect on the BOA reflectance and spectral indices such as NDVI. The highest relative uncertainty of the Sen2Cor BOA reflectance is 37.76% for B01 and the lowest correlation is 0.859 for B09. The result also shows that a high-accuracy atmospheric correction is still crucial and urgent for the Sentinel-2A data.

The long term satellite-derived datasets including AOD, tropospheric NO2and tropospheric 03 from MODIS, GOME-2 and OMI/MLS, respectively were collected and processed for variation and correlation analysis of major air pollution components over East China. The time series data of three urban regions were plotted and analyzed. The result shows that the AOD and trop NO2values over the urban regions are much higher than other areas. The trends of AOD and trop 03 over Pearl River Delta, -0.0076/year and 0.2454 DU/year, respectively, are significant at 0.05 level. There is a high and significant negative correlation between trop NO2and trop 03, and a significant positive correlation between trop 03 and AOD. More study is still ongoing.

In this paper, the 1 km Mixed Pixel Clumping Index (MPCI) was calculated using the 30 m HJ-IA11B CCD data in the arid and semi-arid region, Western China. To validate the result, an indirect validation method was proposed. In this method, the 1 km effective LAI was retrieved from the satellite data using the PROSAIL model first, and then corrected to the true LAI with the MPCI data. The comparison between the retrieved true LAI and the MODIS product shows a significant improvement relative to the effective LAI. The correlation R2rise from 0.52 to 0.70 and the RMSE falls from 0.58 to 0.42. It indicates that the MPCI calculation is reasonable and valid for LAI retrieval from the satellite data.

Tropospheric ozone plays an important role in Earth's atmospheric environment and radiative balance. To assess the temporal variation of tropospheric ozone over Asia, ozone data from the Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere (TOST) were collected, evaluated, processed, and analyzed. Comparing TOST with ozonesonde station data and satellite data, there are good agreements with a high correlation (r at maximum = 0.88), low bias (<;10 ppbv), and low root-mean-squared error (<;20 ppbv). The time series analysis shows increasing trends of the tropospheric ozone mixing ratio for both TOST and satellite data, with the Sen-Theil slopes of 0.4417 and 0.7158 ppbv/year, respectively.

In this study, the Bottom-Of-Atmosphere (BOA) reflectance over Egbert, Ontario, Canada on September 24, 2016, as obtained by the Sentinel-2A Multi-Spectral Imager and corrected by the Sentinel-2 atmospheric Correction (Sen2Cor) software, was evaluated based on the 6SV atmospherically corrected reflectance. The aerosol and water vapor parameters used in the 6SV model were obtained from the AERONET data. The evaluation results showed that for the visible bands, the Sen2Cor BOA reflectance was lower than the 6SV BOA reflectance, with a maximum relative bias of -36.5%, due to an overestimation of the aerosol optical depth retrieved by Sen2Cor. The bias of the BOA reflectance could also affect the vegetation index (VI) calculation. Four VIs were calculated and compared using the different BOA reflectance. The maximum relative bias was 18.2%. The study shows that the BOA reflectance corrected by Sen2Cor should be treated with some degree of caution, especially for the visible bands and VIs.

The Sentinel-2A (S2A) Multi-Spectral Imager (MSI) is a new remote sensor launched on 23 June 2015 that provides unprecedented Earth observation with high spatial, spectral and temporal resolutions. It has high potential for chlorophyll content estimation. Chlorophyll content plays a crucial role in plant photosynthesis affecting the terrestrial carbon cycle. In this research, a physical retrieval algorithm is proposed for leaf chlorophyll content from the S2A MSI data based on 4-Scale and PROSPECT models. Satellite and ground data were collected and processed in a mixed temperate forest near Borden, Ontario, Canada from May to October 2016. Preliminary validation shows an agreement between the inverted and ground measured leaf chlorophyll contents, with r = 0.77 and RMSE = 8.82 μg/cm2, which is an improvement over those generated by the Sentinel Application Platform (SNAP). Further research is ongoing, and the algorithm will be improved in the future.

In this paper, 3 km Moderate resolution Imaging Spectroradiometer (MODIS) aerosol products over Xuzhou and the surrounding area, including Jiangsu, Anhui, Henan and Shandong provinces in China, from July, 2002 to December, 2015 are collected and validated by the ground-based measured data from Xuzhou-CUMT site of Aerosol Robotic Network (AERONET). The validation result shows that the correlation coefficient of aerosol optical depth (AOD) between MODIS and AERONET data is 0.86 and the regression equation is y = 1.06× + 0.06. The monthly and seasonal average of 3 km MODIS AOD are calculated and charted. The results show that AOD in Xuzhou area is highest in summer and lowest in winter. The average of monthly AOD is about 0.82. After 2008 Beijing Olympic Games, AOD in Xuzhou increases again. Therefore, the sustained efforts should be made to improve air quality in Xuzhou area.

Clumping index is an important vegetation structure parameter to describe the foliage clumping in canopy quantitatively. It is defined as the ratio of the effective leaf area index to the true leaf area index. In previous studies, it is generally considerate that cluster of canopy and below canopy scale in pure pixel. However, the in-pixel spatial heterogeneity need be taken into account estimating clumping index in mixed pixel, which is different from clumping index of pure pixel. A new method to calculate clumping index of mixed pixel based on fine spatial resolution image is proposed in this paper. The sensitivity analysis has been processed and its results show that the pixel spatial heterogeneity and view zenith angles cannot be ignored for calculating the mixed-pixel clumping index. The method is capable of correcting the scale difference caused by the heterogeneity of the vegetation cover inside the mixed pixel the view zenith angle. The formula presented can estimate clumping index of the mixed pixel more accurately, which is significant for LAI inversion of coarse spatial resolution and the precision accuracy application of carbon cycle model.