This study explores a new scheme to retrieve the global aerosol optical depth (AOD) over land for the advanced Medium Resolution Spectral Imager (MERSI-II) aboard the Fengyun-3D (FY-3D) satellite based on the dark target (DT) algorithm. The main improvement is that the global surface reflectance (SR) model nonlinearly varies with the solar zenith angle and normalized difference vegetation index (NDVIswir) is made, which is more complex relative to that of Moderate-Resolution Imaging Spectro-Radiometer (MODIS) operational algorithm. Our AOD retrievals are compared with an aerosol robotic network (AERONET) AOD and cross evaluated with Aqua/MODIS, respectively. Overall, the MERSI-II retrieved results over the global scale have good consistency with the AERONET observations; on the same condition, the percentage of matchups within the expected error (EE: ±0.05 ± 0.15AOD) is 67.06%, which is slightly lower than the percentage of MODIS (79.84%). On a spatial scale, the coverage of MERSI-II retrievals at one granule is significantly higher than that of MODIS, which is related to successful inversion of haze pixels and has retrieval ability in urban, grassland, and other surface types. The monthly mean AOD values retrieved by MERSI-II are close to those of MODIS, indicating that MERS-II has similar quantitative capability and application potential as its international counterparts.

The Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun 4A (FY-4A) satellite has high spatiotemporal resolution and provides useful spectral information that can be used to monitor aerosols and air pollution. The objective of this study is to propose the Land General Aerosol (LaGA) algorithm for retrieving aerosol information using AGRI data in the Asia region. First, the sensitivity analysis indicated that the AGRI blue band is more suitable for aerosol retrieval, and its red band is sensitive under high aerosol loading. Then, a real-time surface reflectance (SR) database was established using the atmosphere-corrected technique based on the background AOD library and regional aerosol model parameters. By comparing the AGRI observed reflectance with that calculated using a lookup table, the AGRI aerosol optical depth (AOD) with a 1-h resolution was obtained. The validation results indicated that the AGRI AOD, both at all moments (data volume: 12,102) and the daily mean (data volume: 1,766), exhibit a good agreement with AERONET AOD (R > 0.830). Its performance was comparable to that of the MOdIs dark target (DT) AOD (expected error (EE), ± (0.05 + 20%τAERONET): AGRI = 0.673 vs. DT = 0.666) and Himawari-8 (H8) AOD (EE: AGRI = 0.698 vs. H8 = 0.658). The pixel-by-pixel comparison demonstrated that the R between the AGRI and MODIS AODs was >0.6, and the mean bias between them was within ±0.05 in most of the study area. These results suggest the robustness of the proposed algorithm, and it has great potential for application in the follow-up Fengyun 4 series satellites.

The Directional Polarimetric Camera (DPC) is a polarization sensor mounted on Chinese Gaofen-5 satellite. It has the capability of observing multispectral, multiangular, and polarized light, and can be used to monitor global aerosols and clouds. This work developed an adaptive algorithm to retrieve aerosol properties based on DPC measurements. It performs initial surface property estimations, aerosol parameter retrievals, surface parameter adjustments, and result assessments. In the algorithm, it allows global aerosol optical depths (AODs) and Angstrom exponents (AEs) to be retrieved. The AOD values show good consistency with that from AErosol RObotic NETwork (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS). The regression line between AODs from DPC and AERONET is$y = 0.895x + 0.056$, with a correlation coefficient of 0.894, and 49% of the DPC-retrieved AODs are within the expected error range of ±(15%AOD +0.05). For the AEs, the regression line is$y = 0.728x + 0.395$, the correlation coefficient is 0.763, and 41.4% of the AEs are within the error range of (AE ± 0.4). We also investigated the possible influence of AOD errors retrieved from the DPC measurements over three typical areas. It was found that though the DPC-retrieved dust aerosols did not show so good consistency with the MODIS measurements as the smoke aerosols, the retrievals still matched well on the whole. It demonstrates the potential of DPC and the adaptive algorithm for aerosol remote sensing.

It is difficult to form a quantitative remote-sensing model to describe the reflectance of shadowed urban surfaces. However, it is probably feasible to describe the spectral response of surface polarized reflectance (SPR). In this letter, we clarify the role of shadow in the spectral response of urban SPR from in situ and airborne measurements, which were obtained in Hefei and Binhai New Area, China, respectively. The in situ SPR of concrete and asphalt surfaces, as two largest coverage types of urban surface, shows a decreasing linear trend with the shadow proportion. The ratios between SPR at different wavelengths are around 1. The airborne SPRs are determined using bright, mixed (containing bright areas and shadows), and total (bright and mixed) pixels, which show similar spectral responses: the regression lines of 555 versus 865 nm and 670 versus 865 nm are close to the y = x line, their correlation coefficients are around 0.9, and coefficients of variation are less than 0.3. These results proved that the shadow has little effect on spectral responses of urban SPR, which can be used in quantitative remote sensing of atmosphere over urban areas.

Satellite observation is an effective way of obtaining global aerosol information. The study focuses on developing a new scheme to apply the traditional dark target (DT) method to the advanced Medium Resolution Spectral Imager (MERSI II), which is a part of the Chinese Fengyun-3-D satellite. Compared with the Moderate Resolution Imaging Spectroradiometer (MODIS), MERSI II shows higher ratios between red (0.65$\mu \text{m}$) and near-infrared ($2.13~\mu \text{m}$) bands in surface reflectance estimation and the green band ($0.55~\mu \text{m}$) that is more sensitive to cloud screening. Aerosol optical depth (AOD) is retrieved from earlier MERSI II observations by following the adapted DT method over land in Asia in 2018. Overall, AOD from MERSI II has a good performance compared with ground-based measurements with an expected error (EE%) of 66.38% and$R^{2}$of 0.834, which is close to the MODIS EE% of 70.59% and$R^{2}$of 0.829. Both sensors slightly overestimate the AOD over heavy aerosol loading regions, but MERSI-II has larger retrieval area covering a wider swath than MODIS in heavy hazy areas. On a spatial scale, the MERSI II effectively reflects the AOD distribution pattern but tends to overestimate and underestimate AOD at low and high latitudes, respectively, when compared with MODIS. The MERSI II sensor shows good aerosol detection potential, and the DT algorithm can be applied. MERSI II will provide important observation data on climate change and atmospheric pollution for the investigations in the future.

Himawari-8 (H8), as a geostationary satellite, observing the full disk image of the earth per 10 min, is a very powerful tool to investigate atmospheric aerosol temporal variations. The advanced himawari imager (AHI) onboard H8 has several spectral bands similar with those of Moderate Resolution Imaging Spectroradiometer (MODIS). Aerosol optical depth (AOD) is thus possible to be retrieved from AHI data using the dark target (DT) method of MODIS. Based on a statistics study of infrared bands of these two satellite instruments, we found that the difference of shortwave infrared bands between AHI (2.3 μm) and MODIS (2.12 μm) in DT area can be ignored for our purpose. Meanwhile, for AHI sensor, the normalized difference vegetation index (NDVI) calculated from 0.86 μm (instead of traditional 1.24 μm) and 2.3 μm can also be a good indicator of surface spectral reflectance. Therefore, first, a new NDVI of AHI sensor is proposed in this paper to improve the estimation of surface reflectance based on DT retrieval method. Then, AOD over DT area was retrieved using a lookup table strategy and tested based on AHI observation over China region from August 2015 to July 2016. All retrieved results are validated against groundbased measurements of the aerosol robotic network and the sun- sky radiometer observation network. The coincident AOD results between AHI retrieval- and ground-based network measurements show good agreements with R2of 0.81. The intercomparison between AHI and MODIS DT AOD products also shows a good agreement with R2of 0.92.

In this letter, an improved algorithm for aerosol retrieval is presented by employing the non-Lambertian forward model (forward model) (NL_FM) in the Moderate Resolution Imaging Spectroradiometer (MODIS) dark target (DT) algorithm to reduce the uncertainties induced when using the Lambertian FM (L_FM). This new algorithm was applied to MODIS measurements of the whole year of 2008 over Eastern China. By comparing the results with that of AERONET, we found that the accuracy of the aerosol optical depth (AOD) retrieval was improved with the regression plots concentrating around the 1 : 1 line and two-thirds falling within the expected error (EE) envelope EE = ±0.05±0.1τ (from 53.6% with L_FM to 68.7% with NL_FM at band 0.55 μm). Surprisingly, more accurate retrieval of the AOD demonstrated significantly improved the Ångstrom exponent (AE) retrieval, which is related to particle size parameters. The regression plots tended to concentrate around the 1 : 1 line, and many more fell within the EE = ±0.4 from 53.6% with L_FM to 80.9% with NL_FM. These results demonstrate that including the NL_FM in the MODIS DT algorithm has the potential to significantly improve both AOD and AE retrievals with respect to AERONET in comparison to the L_FM used in the current MODIS operational retrievals.

In recent years the satellite monitoring capabilities in particular to derive maps of aerosol optical depth (AOD) have increased tremendously. There are many aerosol retrieval algorithms for different satellites and sensors such as Dark-Target method (DT), Deep Blue, etc. In this paper, we used an improved approach called the Synergetic Retrieval of Aerosol Properties (SRAP) method to retrieve aerosol properties over land surfaces by using the MODIS data. The improvement of the SRAP method include the following respects: 1) Considering the importance of gas absorption correction, we use ancillary data acquired from National Center for Environmental Prediction (NCEP) analyses to correct the effect of gas absorption. 2) A new cloud mask based on a spatial variability test as well as the absolute value at the 0.47 µm and the 1.38 µm bands were implemented in the SRAP algorithm.

A new algorithm for Land Aerosol property and Bidirectional reflectance Inversion by Time Series technique (LABITS) is presented and applied to National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) data over China. Based on the assumptions that the surface bidirectional reflective property are not varying during one day and aerosol characteristics are constant in 0.1° × 0.1° window, we inverse the aerosol optical depth (AOD) and bidirectional reflectance distribution function (BRDF) parameters. Preliminary AOD validation with Aerosol Robotic Network (AERONET) data shows that the correlation coefficient, R2, is 0.79, the root-mean-square error, RMSE, is 0.13 and the uncertainty is Δτ= ±0.05 ± 0.20Δ. Comparing with MODIS AOD product, it is found that both the AOD results are consistent very well. The R2is 0.80 and RMSE is 0.10. The algorithm is flexible and appropriate for aerosol retrieval over both dark and bright land surface. It is potential to retrieve long term global AOD over land from NOAA AVHRR data since 1980s and to study aerosol climatology and global climate change well.

A new algorithm for Land Aerosol property and Bidirectional reflectance Inversion by Time Series technique (LABITS) is presented and applied to Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI) data. Based on the assumptions that the surface bidirectional reflective property are not varying during one day and aerosol characteristics are constant in 2 × 2 window, we inverse the aerosol optical depth (AOD) and bidirectional reflectance distribution function (BRDF) parameters. Preliminary validation shows good accuracy. The correlation coefficient R2is 0.84, the root-mean-square error is about 0.05, and the uncertainty is found to be Δτ= ± 0.05 ± 0.15τ. Comparing with MODIS products, our inversion are consistent very well. The algorithm is flexible and appropriate for aerosol retrieval over both dark and bright land surface. It is potential to retrieve AOD with a high-frequency over land and to monitor aerosol's local spatio-temporal variation from the geostationary satellite data.

Surface reflectance retrieval is an important step in the data processing chain for the extraction of quantitative information in many applications areas. The aim of this paper is to develop a new method for retrieving surface reflectance and aerosol optical depth simultaneously over both dark vegetated surfaces and bright land surfaces. After applying this new model to the Moderate Resolution Imaging Spectroradiometer (MODIS) data in the Heihe River Basin of China, aerosol optical depth and surface reflectance values of these regions are calculated. The retrieved surface reflectance from MODIS is consistent with measured reflectance from Analytical Spectral Device (ASD) Field Spec spectral radiometer, and the root mean square error (RMSE) are; Band 1 (0.66μm): 0.027; Band 3 (0.47μm): 0.015; Band 4 (0.55μm): 0.017. The R-squared (R2) value reveals a good agreement between MOD09 and retrieved surface reflectance at band 1. The RMSE of the reflectance value differences are quite small; Band 1: 0.031; Band 3: 0.026; Band 4: 0.029.

Since the year of 2011, PM2.5have become a heated topic in China. Particulate matter (PM), also known as aerosol, is one of the major pollutants that affect air quality. Exposure to particular matter with aerodynamic diameters less than 2.5 μm (PM2.5) can cause lung and respiratory diseases and even premature deaths. In this paper we use the aerosol optical depth (AOD) retrieved by the Synergetic Retrieval of Aerosol Properties (SRAP) method from MODIS data to calculate PM2.5., then estimate number of days with good air quality (PM2.5≤0.075 mg/m3) at each pixel over China in 2008. The result is applied to examine the air quality, From which we can see that there are about 200 days with good air quality in Beijing, in agreement with Official Reports. Throng analyzing the calculated days with good air quality in August from 2005 to 2008, we examined the temporal variations of PM2.5over China, These findings indicate a positive annual variation trend before Beijing 2008 Olympic Games, however the air qulity of Beijing still needs improving.

In this paper, a new algorithm, Land Aerosol property and Bidirectional reflectance Inversion by Time Series technique (LABITS), is presented and applied to Advanced Very High Resolution Radiometer (AVHRR) data in North China. In this algorithm, we couple the Ross Thick-Li Sparse Bidirectional Reflectance Distribution Function (BRDF) model and the atmospheric radiative transfer model. Assuming that the surface bidirectional reflective property is unchanged during a short period, usually 2-4 days and aerosol characteristics has a high temporal variation but is consistent spatially, then we can obtain AOD and BRDF parameters jointly by numerical iterative technique. The data used to test our algorithm is Global Area Coverage (GAC) 4KM Level 1B from AVHRR/3 on board NOAA-18 and NOAA-19 from 8 July to 9 July, 2011 in North China (110°E-130°E, 25°N-45°N). Synchronous Aerosol Robotic Network (AERONET) level 1.5 data and field measured data during the Ministry Of Science and Technology Aerosol Project (MOSTap) in Beijing-Tianjin-Tangshan region in 2011 was adopted to validate our retrieved result. The correlation coefficient R is about 0.72. Also, in the area both retrieved AOD and MODIS aerosol product have an effective value, the consistency between them is quite good.

In this paper, an analytical strategy is presented to retrieve jointly aerosol optical depth (AOD) and surface reflectance (R) from geostationary satellites. The new algorithm is based on a parameterization of the atmospheric radiative transfer model. Taking AOD and R as unknown parameters and based on some reasonable assumptions of AOD's spatial consistence and R's temporal invariance, both parameters of each pixel can be derived. Applying this algorithm to data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) observations on board Meteosat Second Generation (MSG), we obtain regional maps of AOD and R from two adjacent observations. Preliminary validation results by comparing our retrieved AOD with Aerosol Robotic Network (AERONET) data show good accuracy, and retrieved R is also reasonable. This method is potential to be applied in instantaneously monitoring aerosol spatio-temporal variation using geostationary satellite sensors with only one single visible channel and high-frequency observations.

Aerosol optical depth (AOD) is a key parameter reflecting aerosol properties, and it is inevitable to mosaic multi-orbit data while making AOD datasets in some situations due to the limitation in scanning width of various instruments. This article summarizes some conventional methods eliminating seams of mosaic images, and introduces the idea of combining the Gaussian dispersion model into mosaic process in the AOD retrieval case. In this article, we elaborate the principle of the Gauss plume model and put forward the improved method. Based on the AOD products from the Synergetic Retrieval of Aerosol Properties (SRAP) model from the Moderate Resolution Imaging Spectroradiometer (MODIS) data, we conduct experiments on improved methods, and evaluate the processing effect.

MODIS and MISR are two main satellites provide aerosol observations. However, AOD products generated from these two sensors by different retrieval algorithms are inconsistent. In this paper, a semi-empirical optical fusion method was proposed to produce consistent AOD with different derived AOD datasets form MODIS and MISR. Using the semi-empirical optical algorithm, new merged AOD data sets were generated over China for 2010. We used level 2 cloud screened quality assured AERONET measurements to evaluate the merged AOD results. Our results showed that the combination of MODIS and MISR with this method could produce a more consistent, reliable AOD data with great improvement in spatial AOD coverage.

The retrieval of aerosol properties over land is more complicated due to the relatively strong contribution of the land surface reflectance to the radiation measured at the top-of-atmosphere (TOA). Another problem caused by the anisotropic earth surface which is a second order effect, can create systematic biases in the aerosol retrieval. But the simple Lambertian surface assumption is still widely used in most aerosol retrieval algorithms for the single satellite view. In this paper, radiative transfer simulations with coupling surface-atmosphere are employed to assess how much uncertainties are introduced from the Lambertian surface assumption in satellite aerosol optical depth (AOD) retrieval. The result shows that it has great impacts on aerosol retrieval especially at lower aerosol loading. The uncertainties mainly depend on the anisotropy of the target. The more difference lies between the reflectance along observing direction and the average ' reflectance ρ̅t, ρ̅t, ρtof the whole BRDF surface, the larger error happens in aerosol retrieval.