This letter reports uncertainties in the Aqua-Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 dark target (DT), deep blue (DB), and multiangle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD) during the COVID-19 lockdown period (February–May 2020) compared to the pre-COVID-19 period (February–May 2019). Validation of AOD retrievals was conducted against AErosol RObotic NETwork (AERONET) Version 3 Level 1.5 AOD data obtained from three sites located in urban (Beijing_CAMS and Beijing_RADI) and suburban (XiangHe) areas of China. The results show the poor performance of the DT and DB algorithms compared to the MAIAC algorithm, which performed better during the lockdown period. Overall, all MODIS algorithms overestimated the AOD and showed higher positive bias under high aerosol loading conditions during lockdown than during prelockdown. This is mainly attributed to the overestimation of the aerosol single-scattering albedo (SSA), which was found higher during lockdown than during the same period in 2019.

Landsat images have played an important role in the field of Earth observation and geoinformatics. However, optical Landsat images are frequently contaminated by cloud cover, especially in tropical and subtropical regions, which limits the utilization of these images. To improve the utilization of Landsat images, in this study, we propose a novel spatiotemporal neural network with four modules: a cloud detection module, a spatial–temporal learning module, a spatial–temporal feature fusion module, and a reconstruction module. The results of the experiments demonstrate that the proposed method is quantitatively effective (root mean square error < 0.0179) and can achieve a better result for reconstructing Landsat images than some of the widely used existing deep learning methods and multitemporal methods. The proposed neural network method provides an effective tool for the removal of contiguous, thick clouds from satellite images, so as to improve the quality of subsequent remote sensing mapping and geoinformation extraction.

In this work, we extracted the near-surface CO2 concentration from the Greenhous gases Observing SATellite (GOSAT) and the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker model datasets for a temporal period of 8 years from 2010 to 2017 to study the spatiotemporal distribution of near-surface CO2 and the factors affecting it over five regions of Asia including Central Asia, East Asia, South Asia, Southeast Asia, and West Asia. The near-surface CO2 datasets from both satellite and model were first validated against the ground-based CO2 observations obtained from the World Data Center for Greenhouse Gases (WDCGG) stations located in Asia to confirm their applicability and the results showed a good agreement between the datasets with significant correlations. The results from the time-series analyses showed a gradual increase in the near-surface CO2 with significant monthly and seasonal variations over all the regions. To study the factors affecting the spatial distribution of near-surface CO2, we investigated the relationship of near-surface CO2 with the anthropogenic CO2 emissions, terrestrial ecosystem, and winds. The results showed that over Asia, the anthropogenic CO2 emissions and winds primarily controlled the spatial distribution of near-surface CO2. However, in the areas where anthropogenic emissions were lower, the terrestrial ecosystem and winds affected the near- surface CO2 distribution. To study the factors controlling the temporal distribution of near-surface CO2, the relationship of near-surface CO2 with vegetation, precipitation, and relative humidity was investigated. The results showed an inverse relationship between near-surface CO2 and NDVI, precipitation, and relative humidity over monsoon-influenced regions, i.e., East Asia, South Asia, and Southeast Asia. However, a positive relation of near-surface CO2 was observed with precipitation and relative humidity over arid and semi-arid regions, i.e., Central Asia and West Asia. The results were also verified by determining the correlations among these variables.

Atmospheric correction is an essential process to correct atmospheric effects in Optical Remote Sensing Data (ORSD) and to provide surface reflectance. The surface reflectance is the most basic and important parameter in optical remote sensing to perform vegetation analysis as well as urban growth assessment. In this study, two atmospheric correction methods, i.e. SREM (Simplified and Robust Surface Reflectance Estimation Method) and 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum, Vector version), were compared over diverse land surfaces using Landsat-8 data. The results showed that the SREM atmospheric correction method performed equally as 6SV over diverse land surfaces. However, the performance of SREM was better than 6SV over hilly terrain as 6SV provides negative values of the surface reflectance due to overcorrection.

Satellite aerosol optical depth (AOD) is a quantitative parameter frequently used to estimate ground-level fine particulate matters (PM2.5)at regional to global scales. In this article, Himawari-8 apparent reflectance (top-of-atmosphere reflectance) data were used to estimate the hourly ground-level PM2.5concentrations (Ref-PM2.5) using deep neural networks (DNNs), and comparison was conducted with the AOD-based PM2.5estimation method (AOD-PM2.5). In high-density site areas, the Ref-PM2.5method was closer to the actual situation and more capable of PM2.5estimation compared with the AOD-PM2.5method. The PM2.5samples used in the AOD-PM2.5method were less than one-half of the Ref-PM2.5method due to unavailability of AOD observations, which might be due to strict surface assumptions, cloud detection, and error in the aerosol scheme used in the AOD inversion method. This led to many missing values of AOD-derived PM2.5in the spatial distribution map of a single day. Moreover, similar hourly variations in PM2.5were observed for both the methods, and the highest concentration of PM2.5appeared at the junction of Jiangsu, Anhui, and Shandong at 08:00, 09:00, and 10:00 in local time, which gradually decreased at 11:00 and reached to a minimum value at 16:00 and 18:00.

Aerosols, suspended particles in the atmosphere, play an important role in the formation of cloud nuclei, climate change, poor air quality and degradation of atmospheric visibility, and public health and diseases. For a full understanding of aerosols effects on urban air quality and public health, retrieval of aerosol optical properties such as aerosol optical depth (AOD) over bright urban surfaces is required. In this study, Landsat 8 Operational Land Imager (OLI) cloud-free images at 30 m spatial resolution were obtained from 2013 to 2017 for the blue (483 nm), green (560 nm) and red (660 nm) channels to retrieve AOD using the Simplified Aerosol Retrieval Algorithm (SARA). The Minimum Reflectance Technique (MRT) was applied on the composite of Landsat surface reflectance images to estimate surface reflectance for AOD retrievals. Validation of the high-resolution AOD retrievals was conducted against two AERONET (AErosol RObotic NETwork) sites located at bright urban surfaces of Beijing. The quality and error of the AOD retrievals were reported using the Expected Error$\mathrm{EE} =\pm(0.05+20\%))$, root mean square error (RMSE), and relative percent mean error (RPME). Results showed that the Landsat 8 AOD retrievals were well-correlated$\mathrm{R}=0.98$) with the AERONET AOD measurements for each channel with 75% of the retrievals were within the EE, RMSE of 0.11 and RPME of 7%. These results suggest that the SARA algorithm is robust and useful to retrieve high-resolution AOD observations and Landsat 8 can be used for aerosols monitoring over bright urban surfaces of Beijing, which is frequently affected by severe dust storms and haze pollution, to identify their effects on public health.

In this study, the MODIS Collection (C6) combined Dark Target (DT) and Deep Blue (DB) aerosol products (DTBC6) at 10 km spatial resolution was validated against AERONET AOD obtained from 68 sites at global scale from 2004-2014 over different vegetated surfaces, i.e., partially-vegetated (NDVIP ≤ 0.3), moderately-vegetated and densely-vegetated surfaces defined by monthly NDVI values obtained from the MOD13A3 product. For comparison purposes, AOD was obtained from the combined DTBSMSproduct which was developed based on the Simplified Merged Scheme (SMS), i.e., using an average of the DT and DB highest quality AOD retrievals or the available one. Results showed that the number of coincident observations of the DTBSMSincreased from 6121 (DTBC6) to 10055 for NDVIPsurfaces, from 13371 (DTBC6) to 21438 for NDVIMsurfaces, and from 9410 (DTBC6) to 14415 for NDVIDsurfaces. The percentage of retrievals within the Expected Error (EE=±(0.05+0.15×AOD) increased by 13-15% and RMB decreased by 31-58%. Overall, the DTBSMSis robust and performed much better than the DTBC6 in terms of spatiotemporal coverage and data quality, and can be used operationally for generation of the merged DT and DB AOD product at global scale.

The objective of this study is to validate the Aqua-Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target (DT) operational aerosol products at 3 km (DT3K) and 10 km (DT10K) spatial resolutions over the South China Sea. For validation of the DT3K and DT10K AOD retrievals, the ground-based Microtops II Sun photometer AOD measurements, obtained from the Marine Aerosol Network (MAN) which were collected from 10 different cruises over the South China Sea, were used. Results showed that 38% of the DT3K and 68% of the DT10K AOD retrievals were within the expected error (EE = {+(0.04+0.10×AODMAN), -(0.02+0.10× AODMAN) } with root mean square error (RMSE) of 0.128 and 0.141 and mean absolute error (MAE) of 0.084 and 0.094, respectively. Underestimation in the DT AOD retrievals were found at both the resolutions, and a negative relationship was also found between DT AOD and normalized water reflectance. These results indicated that regional meteorology errors of the surface reflectance may have a significant effect on the underestimation of the MODIS AOD retrievals.

Satellite-retrieved aerosol optical depth (AOD) has been increasingly utilized for the mapping of fine particulate matter (PM2.5) concentrations. An accurate estimation and mapping of PM2.5concentrations depends on the high-resolution AOD data and a robust mathematical model that takes into account the spatial nonstationary relationship between PM2.5and AOD. Take the core portion of the Beijing-Hebei-Tianjin (Jing-Jin-Ji) urban agglomeration as case study (the most seriously polluted region in China). Land use, population, meteorological variables, and simplified aerosol retrieval algorithm-retrieved AOD at 1-km resolution are employed as the predictors for the geographically weighted regression (GWR) and the ordinary least squares (OLS) model to map the spatial distribution of PM2.5concentrations. The GWR model shows significant spatial variations in PM2.5concentrations over the region than the traditional OLS model, which reveals relative homogeneous variations. Validation with ground-level PM2.5concentrations demonstrates that PM2.5concentrations predicted by the GWR model (R2= 0.75, RMSE = 10 μg/m3) correlate better than those by the OLS model (R2= 0.53, RMSE = 16 μg/m3). These results suggest that the GWR model offered a more reliable way for the prediction of spatial distribution of PM2.5concentrations over urban areas.