Due to cloud/snow contamination and retrieval method limitation at night, satellite aerosol optical depth (AOD) products often have many missing gaps not only at night but also during the day. In contrast, hourly seamless AOD data with coarse resolution from numerical models can usually be used to fill in the missing gaps in satellite products. However, current studies on seamless optimization of satellite AOD data only focus on fusion results during satellite overpass time and ignore the spatiotemporal complementarity of multiple satellite products. In this study, we propose a model for fusing multiple AOD data sets, which for the first time combines three satellite AOD products and two aerosol numerical model products to produces hourly seamless 1-km AOD products throughout day and night in the Beijing-Tianjin-Hebei urban agglomeration region. Compared with ground AERONET AOD data, the validated results not only achieve a promising accuracy, e.g. R2=0.91 [RMSE=0.09], in the region containing three satellite AOD retrievals, but also obtain a reasonable result, e.g. R2=0.83 [RMSE=0.21], in the region without satellite AOD retrievals. The spatial information entropy evaluation results also indicate that the generated AOD data can capture more spatial details than the numerical model data. Our results demonstrate the proposed model can generate reliable hourly seamless fine AOD data, having significant meanings in the aerosol related fields.

Satellite video with high temporal resolution can provide a broad view for surface dynamic monitoring, especially traffic monitoring. However, the limited vehicle features and complex background information make it difficult to detect vehicles simply from video frame features such as spectrum, shape, and texture. In this letter, a new satellite video dynamic vehicle detection method for traffic monitoring is proposed, which considers the relation between vehicles and the background in a time series. Road buffers are obtained according to the various road levels in order to focus on dynamic vehicles, and then, a multiframe brightness gradient (MBG) is proposed to detect potential dynamic vehicles on the buffers, and the detection results are optimized by the cumulative trajectory of motion. This approach can effectively suppress false detection caused by parallax motion and illumination variation in satellite videos. The experimental results of three satellite videos show that the proposed method can effectively achieve dynamic vehicle detection with a high positive detection rate and low false alarm rate.

The ground PM2.5 mapping method based on aerosol optical depth (AOD) is universal. However, since the modeling samples for hourly PM2.5 mapping are usually very large, and it also lacks an efficient spatiotemporal model to describe the strong spatiotemporal correlation that existed in the large hourly PM2.5 modeling samples, most of the existing studies focus on the daily, monthly, and annual average scales. In this study, a model coupled with a spatiotemporal Kalman filter and linear mixed effect (LMESTKF) is proposed. Based on the hourly seamless 1-km AOD data generated by fusing multiple satellite products and numerical model data products, we realize efficient and accurate hourly seamless 1-km PM2.5 mapping in the Beijing-Tianjin-Hebei (BTH) urban agglomeration region. Results show that the goodness of fit ($R^{2}$), root mean square error (RMSE), bias, and mean absolute percentage error (MAPE) metrics based on sample- and site-based tenfold cross-validation method are (0.91,$14.37 \mu \text{g}/\text{m}^{3}$,$- 0.41 \mu \text{g}/\text{m}^{3}$, 28.91%) and (0.87,$16.98 \mu \text{g}/\text{m}^{3}$,$- 0.32 \mu \text{g}/\text{m}^{3}$, 34.62%), respectively; the accuracy is higher than those of the existed PM2.5 mapping models in the BTH urban agglomeration region. In addition, the time and memory consumption required to construct the LMESTKF model for hourly PM2.5 mapping at the monthly scale is only around 10 min and 4 GB, respectively, indicating that the LMESTKF model proposed in this study is not only high in accuracy but also fast in efficiency. These results prove that the proposed LMESTKF model can efficiently generate accurate hourly PM2.5 maps, which can further promote the prevention and control of PM2.5 pollution in China.

Underwater ultraviolet radiation (UVR), which plays a significant role in photobiological and photochemical processes, is one of the key factors in marine ecosystems. A new algorithm KpcaUV, based on kernel principal component analysis (KPCA) and multiple linear regression (MLR), was proposed in this study for the retrieval of the UVR diffuse attenuation coefficient Kd(λ) from remote sensing reflectance Rrs(λ) in the global ocean. KPCA can be applied in all areas that principal components analysis (PCA) can be used. More importantly, KPCA can help mapping data into high dimensions and reducing the nonlinearity between inputs and outputs, which will improve the performance and robustness of algorithms when deriving large dynamic ranges parameters. Compared with SeaUVc, which is one of the most successful Kd(λ) retrieval algorithms in UVR, the results showed that KpcaUV (with R2: 0.970 and RMSE: 14.0%) performed similar to SeaUVc (withR2: 0.963 and RMSE: 15.6%) when implemented with high-quality data. Nevertheless, KpcaUV was more robust and consistent than SeaUVc when implemented on the satellite images with different levels of quality control. The RMSD of SeaUVc had a significant reduction from 26.8% (QA ≥ 0.6) to 12.7% (QA = 1.0), and the RMSD of KpcaUV varied less than SeaUVc from 14.6% (QA ≥ 0.6) to 10.1% (QA = 1). Hence, considering its good nonlinear-problem-solving ability and robustness when applied to multiple satellites, KpcaUV proposed by this study can be used to obtain Kd(380) for the continuous observation of the large area.

The Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite was launched on September 7, 2018, and has been providing global multispectral Earth observation data as a new spaceborne sensor for ocean color detection since September 10, 2018. In this study, an atmospheric correction algorithm using a composite of the algorithms developed by Wang and Gordon (1994) and Heet al.(2004) and a chlorophyll-a concentration retrieval method that is a composite of the OC4 and color index (CI) algorithms are used to derive the global chlorophyll-a concentration from COCTS. The retrieval products are validated againstin situdata measured in the East China Sea and the South China Sea during the in-orbit testing activity for the HY-1C satellite with an unbiased percentage difference (UPD) of 39%, and the data are also validated against the Aerosol Robotic Network-Ocean Color (AERONET-OC) data with a UPD of 39%. The daily global chlorophyll-a concentration from COCTS with a gridded resolution of 9.2 km and a time period from September 10, 2018 to February 29, 2020 is compared with the same products from MODIS and VIIRS. The UPDs of COCTS against MODIS onboard Terra are approximately 20%, and the mean biases (in logarithm form) are approximately zero. Examples of chlorophyll-a concentration retrieval results in specific cases along the eastern coast of China and the Kuroshio Current are also presented to show the performance of the algorithms used in this study. The retrieval and evaluation results show that COCTS onboard HY-1C demonstrates satisfactory performance for global chlorophyll-a concentration observations.

In this work, the cyclostationary empirical orthogonal function analysis and the empirical mode decomposition (EMD) method is used to compute the low frequency parts (T >~5 years) in tide gauge, satellite altimetry and reconstructed sea level data. High spatio-temporal correlations are observed between tide gauge measurements North of Cape Hatteras (NCH) and other datasets in the subpolar and tropical regions in the North Atlantic Ocean. The altimeter and tide gauge data show significant different spatial pattern of correlation over north with that South of CH. In the last two decades, the weakening of Atlantic Meridional Overturning Circulation (AMOC) might be related to the phase reversal of the correlations in the NCH as well as the strengthening of positive correlations in the tropical regions. Moreover, higher correlations observed near the tide gauges on the coasts of the NCH are presented by comparing the correlations in the time span of 2003-2012 with that in 1993-2002. Both the North Atlantic Oscillation, Atlantic Multidecadal Oscillation and ocean heat content variations, which could affect AMOC and Gulf Stream variations, are linked to the variations of the correlations.

To analyze the wind power and develop tools for the off-shore wind farm siting in the coastal region of Guangdong with water depth 30~50m, wind data from satellites, the CCMP analysis, Lidars, wind towers, buoys, a high-resolution numerical model are used to calculate wind resources and wind energy reserves. Overall, the northeastern wind with speed above 8m/s is prevailing in Autumn and Winter and the southern wind with low speed is prevailing in Spring and Summer. The eastern part of the off-shore region of Guangdong province is better for wind farm siting than the western region.

GF-1 is the first satellite of this Chinese civilian remote sensing satellites series. This paper presents some typical applications on marine environmental monitoring with high resolution and wide swath satellite data, especial on the marine disaster monitoring such as oil spill, sea ice, and red tide. With these data, we can get the detailed information about different disaster.

SAR data and its texture features are used to estimate the ice thickness over Liaodong Bay with ice model thickness. Sea ice and open water discrimination works well for dual-polarized data using a simple linear model. For ice thickness estimation the number of data points is too limited, but relatively good estimates can be extracted using the leave-one-out approach. The leave-one-out ice thickness estimation (N=31) accuracy is below: mean error is 5.8cm and RMSE is 7.1 cm.

GIS-based proximity models are one of the key tools for the assessment of exposure to air pollution when the density of spatial monitoring stations is sparse. Central to exposure assessment that utilizes proximity models is the ‘exposure intensity–distance’ hypothesis. A major weakness in the application of this hypothesis is that it does not account for the Gaussian processes that are at the core of the physical mechanisms inherent in the dispersion of air pollutants.Building upon the utility of spatial proximity models and the theoretical reliability of Gaussian dispersion processes of air pollutants, this study puts forward a novel Gaussian weighting function-aided proximity model (GWFPM). The study area and data set for this work consisted of transport-related emission sources of PM2.5 in the Houston-Baytown-Sugar Land metropolitan area. Performance of the GWFPM was validated by comparing on-site observed PM2.5 concentrations with results from classical ordinary kriging (OK) interpolation and a robust emission-weighted proximity model (EWPM). Results show that the fitting R2 between possible exposure intensity calculated by GWFPM and observed PM2.5 concentrations was 0.67. A variety of statistical evidence (i.e., bias, root mean square error [RMSE], mean absolute error [MAE], and correlation coefficient) confirmed that GWFPM outperformed OK and EWPM in estimating annual PM2.5 concentrations for all monitoring sites. These results indicate that a GWFPM utilizing the physical dispersing mechanisms integrated may more effectively characterize annual-scale exposure than traditional models. Using GWFPM, receptors’ exposure to air pollution can be assessed with sufficient accuracy, even in those areas with a low density of monitoring sites. These results may be of use to public health and planning officials in a more accurate assessment of the annual exposure risk to a population, especially in areas where monitoring sites are sparse.

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.

Gravity-based spatial access models have been widely used to estimate spatial access to healthcare services in an attempt to capture the interaction of various factors. However, these models are inadequate in informing health resource allocation work due to their inappropriate assumption of healthcare demand. For the purpose of effective healthcare resource planning, this article proposes a three-step floating catchment area (3SFCA) method to minimize the healthcare-demand overestimation problem. Specifically, a spatial impedance-based competition scheme is incorporated into the enhanced two-step floating catchment area (E2SFCA) method to account for a reasonable model of healthcare supply and demand. A case study of spatial access to primary care physicians along the Austin–San Antonio corridor area in central Texas showed that the proposed method effectively minimizes the overestimation of healthcare demand and reflects a more balanced geographic pattern of spatial access than E2SFCA. In addition, by using an adjusted spatial access index, the 3SFCA method indicates strong potential for identifying health professional shortage areas. The study concludes that 3SFCA is a promising method to provide health professionals and decision makers with useful healthcare accessibility information.