Urban nitrogen dioxide nitric oxide (NO2) emission is a major source of total NO2 emission, yet brings large uncertainty of emission estimates because of its complicated internal environment. Current top-down methods, for example, exponentially modified Gaussian (EMG) have strict theoretical assumptions of atmospheric dispersion condition and simplify the whole urban region as an isolated point source, which differs from actual emission situation, resulting in poor temporal representativeness and large uncertainty. This article constructs a remote sensing estimation method for urban NO2 emissions based on the cross-sectional flux method which is insensitive to meteorological assumptions, and improved it with considerations of NO2 lifetime. Compared with ground-based observation, the mean absolute percentage error (MAPE) of this work decreases by 42.58% compared with EMG’s MAPE. On the total scale of annual stocktake, the MAPE of this work is reduced by 39.79% compared with the EMG method. This method weakens the impact of meteorology condition and provides higher temporal representativeness. The retrieved NO2 emission based on this method of New York City, Las Vegas, Chicago, Wuhan, Xi’an, and Paris shows a$61.55~\pm ~28.25$kt/yr differences compared with Emissions Database for Global Atmospheric Research (EDGAR) inventory results, with overestimation up to 135.39 kt/yr (Xi’an). For all study regions, a clearly temporal pattern of NO2 emission is found, with the monthly emission during ozone season increases 5.76 kt/month compared with nonozone season. The cross-sectional flux method, once improved, demonstrates greater accuracy in estimating urban NO2 emissions and is expecting to be applied to different gaseous emission to provide more reliable results.

Measurements of column-averaged dry air mole fraction of CO2 (XCO2) from satellite contain systematic errors and regional scale biases are often induced by the limitations of the retrieval algorithm. Although the global linear bias correction (BC) model has successfully reduced some of these systematic errors, the unified correction formula and filter have proven too restrictive for certain regions, resulting in high remaining systematic errors. We propose a BC method through Bayesian optimal estimation considering spatiotemporal bias characteristics (ST-BCs) to address this issue. The prior distribution is provided by the global flux model, and the corrected samples are calculated by: 1) training a nonlinear model of satellite correction parameters and truth proxy based on XGBoost machine learning; 2) adjusting spatial characteristics bias based on spatial correlation of systematic errors; and 3) using the total carbon column observation network (TCCON) long-term measurement trend to correct temporal characteristics bias. The posterior correction value is iteratively calculated through Bayesian optimal estimation. We conducted experiments using OCO-2 V10 retrieval in the study areas in Europe, Asia, and North America, where TCCON coverage is extensive. The results demonstrated that the method effectively reduces regional bias and also significantly diminishes the correlation between state parameters and bias. The application of ST-BC enhances the correction accuracy of ACOS V10 products from 1.07 to 0.88 ppm. Particularly, the method reduces bias by 0.78 ppm for complex summer measurements. This study improves the accuracy of satellite retrieval XCO2 and will advance our understanding of the global and regional carbon sources and sinks.

Fine particulate matter, specifically PM2.5, has raised increasing public and governmental concerns over the past decade for its threats to the environment and public health. For large-scale PM2.5 estimation, spatial neighborhood information is frequently ignored when modeling the spatiotemporal heterogeneity of the PM2.5-aerosol optical depth (AOD) relationship. In this regard, applying convolutional neural networks (CNNs) to extract the spatial neighborhood characteristic has great potential; therefore, this article establishes a spatial neighborhood deep neural network (SNDNN) model to predict PM2.5 concentrations across China. In addition to the backward propagation neural network (BPNN) model for extracting spatiotemporal features, the model integrates a CNN model to achieve spatial neighborhood data mining within a$3 \times 3$km2 window. The cross-validation (CV) results show that the daily model achieves high accuracy and stability from 2016 to 2020. The coefficient of determination ($R^{2}$) value reached 0.92 in 2018, with a root mean square error (RMSE) of$9.39 \mu \text{g}/\text{m}^{3}$and a mean absolute error (MAE) of$6.09 \mu \text{g}/\text{m}^{3}$. Further, a monthly SNDNN model is established to predict seasonal and annual PM2.5 concentrations with greater accuracy and wider spatial coverage. The study results demonstrate the superiority of introducing spatial neighborhood information to PM2.5 estimation and indicate that the SNDNN can provide a practical reference for spatial neighborhood feature extraction.

Fusing multiple satellite aerosol optical depth (AOD) products to produce high-quality aerosol records is necessary for climate-related research. The Bayesian maximum entropy (BME) approach has unique advantages in this regard. However, the large accuracy differences and redundancy characteristics between aerosol products are not considered by BME resulting in limited fusion quality and large computational consumption. Therefore, in this study, we try to explicitly introduce a priori knowledge about the multisource AOD product accuracy, called the accuracy ranking matrix, into the BME fusion process to test whether the above issues can be alleviated. Eight publicly released aerosol products over China are used in the fusion experiments, and the fusion results are validated with ground-based measurements. Results suggest that, compared with fusion directly, the fusion accuracy and efficiency are significantly improved by introducing the accuracy ranking matrix; the proportion of satisfying the Global Climate Observing System (GCOS) accuracy requirements increases by 12.2%, and the consumption time decreases by 74.6%. Specifically, the accuracy improvement in vegetated and clean air conditions outperforms that in arid and polluted conditions, with the most significant improvement in clean conditions, where the GCOS fraction increased by 22.9%. Moreover, the spatial distribution of fusion results in typical regions indicates that the introduction of the accuracy ranking matrix makes the AOD spatial variation smoother. These results demonstrate that introducing the accuracy ranking matrix can generate higher quality AOD distributions with improved efficiency, which is expected to guide the fusion of AOD products or other remote sensing products.

Single scattering albedo (SSA) is a key variable to describe the aerosol absorption for solar radiation and also a critical metric for the climate impact. However, a great challenge is noticed from most spaceborne algorithms since satellite measured reflectances result from a convolved effect of aerosol loading and absorption, which is hardly differentiated and SSA can only be simply approximated depend on a few aerosol model candidates. This study is intended to propose an algorithm to obtain aerosol SSA indirectly from visible infrared imaging radiometer suite (VIIRS), developing a strategy to characterize the aerosol absorption of polluted plume over East Asia. A new parameterization scheme for aerosol models is proposed by optimizing the independent SSA values to the mixed combination of three basic components. Notable sensitivities mean that the updated aerosol assumptions work efficiently in the lookup table-based algorithm. The algorithm is less affected by the uncertainties of surface definitions and achieves reasonable SSA estimations under higher aerosol loadings conditions (AOD > 0.5). The comparison is encouraging that new SSA results have an expected correlation with those retrieved independently of ground-based sun photometers, especially for high aerosol loading SSA, with better correlations (0.603, 0.571, and 0.473 of$R^{2}$at 440, 550, and 675 nm, respectively), lower mean biases (0.026, 0.030, and 0.042 at 440, 550, and 675 nm, respectively), and 85% of the retrievals fall within the 5% expected error (EE) envelope. The flexible SSA products will thus be more useful for characterizing aerosol absorption in pollution.

In this study, a temporal shape–based fusion method using a spatially and temporally moving window is proposed to incorporate time lag of fine and coarse resolution observations, and to fully utilize target fine resolution pixel and similar coarse resolution pixels in the process. This method provides high accuracy fused images with Pearson’s r of ~0.95, root mean square error of ~0.04, and bias of ~0.01 for commonly used fine spatial resolution satellites, including Landsat 7 and 8, Sentinel 2, and Gaofen 1, over different heterogeneous regions, such as urban, mountain, forest, and savanna regions. The fused fine resolution Enhanced Vegetation Index (EVI) time series using different fine spatial resolution satellites data as input are all highly correlated with the PhenoCam monitored green chromatic coordinate, with no temporal lag. Compared with commonly used data fusion method, this method provides equivalent and slightly higher accuracy because both neighboring similar pixels and the annual temporal variation are fully considered. This temporal shape–based fusion method does not require each input fine resolution image to be cloud-free; therefore, it can be used at a large spatial scale without further preprocessing and generates continuous datasets over a long-time range with only one input preparation process. The factors that could affect the method accuracy are the cloud detection accuracy of fine resolution data and the temporal continuity of the coarse resolution data. The method may also be used to produce spatially and temporally continuous surface reflectance and other surface reflectance derived indices.

A novel multisensor synergy method, using data from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua as well as Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership, is presented to retrieve optical atmosphere-surface properties. By adopting three-sensor observations’ synergy, the proposed method can grasp the multitemporal characteristics of aerosol optical depth (AOD) and the multidirectional characteristics of surface reflectance. In addition, the bidirectional reflectance distribution function (BRDF) can be derived at daily scale by adopting the novel shape function constrained BRDF retrieval (SFCBR) method. The 550-nm AOD retrieval result of the proposed method is validated by AErosol RObotic NETwork (AERONET) measurement at Beijing, XiangHe, Noto, and Gwangju_GIST sites with$R^{2}$equaling to 0.78, 0.75, 0.70, and 0.75, respectively. Compared with MODIS/VIIRS AOD official product, the proposed method shows higher coverage rate (especially in AERONET Beijing site with approximately 50% increase) with comparative accuracy. The expected error of the retrieved AOD from the proposed method is estimated as$\Delta \tau = \pm 0.05 \pm 0.24\tau $. The correlation coefficients of BRDF-derived albedo time series between the proposed method and MODIS BRDF/Albedo product can reach up to 0.85 with an obvious improvement in temporal resolution by adopting an SFCBR method. The average relative differences of BRDF shape function between the retrieval result and MODIS BRDF/Albedo product in all directions equal to 0.026, 0.036, 0.037, and 0.016 in AERONET Beijing, XiangHe, Noto, and Gwangju_GIST sites, respectively.

The high-spatial-resolution aerosol retrieval algorithm using Chinese High-Resolution Earth Observation Satellite I (GF-1) wide-field images is developed, which retrieves the aerosol optical depth (AOD) over China for studying the impact of aerosol on climatic and environmental change. The algorithm is based on the red/blue surface reflectance correlations and the lookup table method. To reduce the enormous relative error caused by the constant surface reflectance relationship in the retrieval algorithm, the correlation is parameterized as a function of low, medium, and high values of normalized difference vegetation index (NDVI). Three linear relationships are simulated using MODIS BRDF-adjusted reflectance products (MCD43A4), and MODIS NDVI products are used to ascertain the value of NDVI. By applying the present algorithm to GF-1 images, two different aerosol cases of clear and turbid are analyzed to test the algorithm. Compared with the 10-km MODIS aerosol properties productions, the GF-1 retrieved AOD by our algorithm revealed a significant correlation coefficient with MODIS Dark Target AOD (R = 0.912) and Deep Blue AOD (R = 0.895). Otherwise, the retrieved AOD results are found to be highly correlated with Aerosol Robotic Network (AERONET) sunphotometer observations (R = 0.931). Compared with the results relying on the MODIS surface reflectance model, preliminary validation is encouraging that the method based on our updated surface reflectance assumptions successfully improved the accuracy, particularly under the clear sky background and over bright surface.

This paper analyzes the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR) AOD products to study aerosol distribution and regional trends during 2002 to 2010. Firstly, we compared MODIS and MISR AOD with AERONET station AOD. MISR has been found the better correlation (R= 0.93), which dominate MISR perform better than MODIS over eastern China. Our study found that the spatial distribution of aerosol for both sensors is highly associated with human activities. The trend analysis shows that increasing trends are found over study areas and MISR has better applicable over eastern China is evidenced using method of trend evaluation.

Cirrus clouds are known to play a key role in the Earth's radiation budget and global climate change, the radiative effects of cirrus clouds depend critically on cloud properties such as optical thickness and particle shape and size. The studies of the optical, microphysical, and physical properties of cirrus have become the popular issue. This paper simulated the bidirectional reflectance distribution function (BRDF) and bidirectional polarization reflectance distribution function (BPDF) at 1380 nm in cirrus cloudy conditions on the basis of an adding-doubling radiative transfer program. Based on the sensitivity of 1380 nm spectral reflectance and polarization reflectance on cirrus optical thickness and aspect ratio, a conceptual approach has been developed to simultaneous retrieve the particle shape and optical thickness of cirrus clouds using the remote sensing data of multi-angular total and polarized at 1380 nm.

HJ-1C will be launched at the end of 2008, it is a component of HJ satellites which are developed in China. HJ-1C SAR has a frequency of S-band (3.2 GHz), VV single polarization, and incident angle range from 25deg-47deg. Soil moisture retrieval using L- and C-band radar has been widely studied, but little attention was paid to S-band data. For the application of HJ-1C SAR, in this paper, the available data of S-band radar data by PALS (Passive and Active L- and S-band sensor) from SGP99 and SMEX02 experiments was used to study the potential of soil moisture change retrieval using S-band single polarization radar.

This paper, we detect clouds in China regions from combination of POLarization and Directionality of the Earth's Reflectances(POLDER) data and Moderate Resolution Imaging Spectroradiometer(MODIS) data, based on the spectral, multi- angular, and polarized characteristics of cloud. Four tests are applied to the measurements. The first one is blue channel reflectance test. The second one is the test on polarization at 865 nm. The third one is the test on reflectance at 1380 nm. The fourth one is the test on reflectance at 645 nm and 1640 nm. At last, the performance of method is evaluated using a large dataset of surface face observations of cloud cover. The result demonstrate the methods of cloud detection are feasible and believable..