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Aerosol types have crucial and distinguished impacts on climate effects and cause the difference of air pollution. However, direct retrieval of aerosol type by satellite remote sensing is difficult, and the satellite products of aerosol type are seriously lacking. At present, the identification of aerosol type is mainly based on ground-based observation data, but the coverage area of ground-based stations is limited, and large-scale spatial continuous monitoring cannot be carried out. The existing aerosol type identification methods based on satellite data need to combine multiple satellites data (e.g. Moderate-resolution Imaging Spectroradiometer (MODIS), Ozone Monitoring Instrument (OMI), etc.) which leads to a low temporal resolution. The research about aerosol type identification only based on geostationary satellite data is lacking. Therefore, aerosol type identification based on the multi-parameter threshold method and the neural network method are proposed and compared in this paper. The neural network jointly using spectral and temporal information was trained and tested by the measurements from 26 sites of Aerosol Robotic Network (AERONET) and Sun-Sky Radiometer Observation Network (SONET). For the overall validation, the aerosol type identification based on the neural network is better than that based on the multi-parameter threshold method, and the accuracy reaches 71.44%. Atmospheric environment monitoring and quantitative remote sensing can be supported by this study. • Aerosol type, neural network, geostationary satellite [ABSTRACT FROM AUTHOR]

• An ecological risk assessment model of Jiaozhou bay in China is established. • The model is developed based on multi-source satellite and in-situ sample datasets. • The effects of multiple risk sources threatening the ecology are considered. • The risk sources for multiple years are detected using remote sensing images. • The ecological risk situation of Jiaozhou Bay for multiple years is assessed. Healthy coastal area have high ecological service value. However, because of the uncontrolled utilization of marine resources and the discharge of pollutants, the ecological environments of many coastal areas are faced with ecological risks, such as oil spills and Enteromorpha blooms. Therefore, it is of great significance to monitor and assess the ecological risks in coastal areas. The remote sensing technique can be used to obtain frequent and wide-coverage images of the areas of interest, and has become an effective tool to monitor the ecological risks occurring in coastal areas. In this study, taking Jiaozhou Bay in China as the study area, an innovative ecological risk assessment framework was established using multi-source remote sensing images and in-situ sample data, which fully considers the potential harm of oil spills and Enteromorpha blooms on the ecology of the bay. Specifically, the oil spill risk for Jiaozhou Bay from 2017 to 2019 was identified by a deep learning based method from synthetic aperture radar (SAR) images, and the Enteromorpha bloom risk for the corresponding years was identified by a floating algae index (FAI) threshold method from multi-source optical images. Then, using habitat information and ground observation data about the marine species in the bay, environmental vulnerability maps were generated. Finally, a disaster-causing factors and disaster-bearing factors weighted risk assessment model was developed. The risk source monitoring results show that the occurrence frequency of oil spills in Jiaozhou Bay decreased from 2017 to 2019, while the outbreak intensity of Enteromorpha blooms generally became higher. The ecological risk assessment results reveal that the high risk areas of Jiaozhou Bay are mainly around the transportation regions and the ports, with a higher risk degree in the eastern and southern parts. To support our work, the well-trained oil spill detection network model and some information about the oil spill events that have occurred in Jiaozhou Bay are provided in this paper. [ABSTRACT FROM AUTHOR]

[Display omitted] • Eco-environment assessment of 3 756 urban residential quarters in Xi'an, China. • Factors affecting the ecological environment of residential quarters have been detected. • Specific reference construction values for the eco-environment of residential quarters have been proposed. Residential quarters are a critical component of urban areas, which significantly affects people's health and quality of life. In recent decades, the high-density and crowded residential quarters have become the mainstream organizational model of construction in China, because of the rapid and massive urbanization in many cities. This results in the contradiction between the eco-environment needs and the accommodation needs. Therefore, a systematic evaluation of the eco-environment in residential quarters is urgently needed to address the drawbacks of current residential quarters' planning and design. This paper aims to explore ways to improve the eco-environment with residential quarters from the perspective of China. We first used the Remote Sensing Ecological Index (RSEI) to evaluate the eco-environmental quality of 3, 756 residential quarters in the main urban areas of Xi 'an, China. Then, we quantitatively analyzed the landscape patterns of the impervious surfaces in these residential quarters. Finally, we characterized the impacts of residential quarters layout on the ecological environment by using the vegetation coverage (FVC), the normalized difference bare soil index (NDBSI), building density (BD), the residential density (RD), and the population density (PD) as main factors. The results are as follows: (1) Only 20.5 % of residential quarters with good eco-environments were distributed between the Second and Third Ring Road, indicating the need for improvement in creating livable eco-environments. (2) The shape of the impervious surface of excellent ecological quarters is complex and broken, the average patch area is smaller, and the degree of aggregation and combination of patches is lower compared to residential quarters with poor ecological quality. Therefore, it is recommended to reduce the connectivity of the impervious surface to improve the eco-environment of residential quarters. (3) Among the factors affecting RSEI of residential quarters, FVC, and NDBSI contribute more than other factors. For the construction of excellent eco-environments in residential quarters, we suggest that vegetation coverage should be no less than 0.4, the impervious surface coverage should be no more than 0.53, the building density should be between 0.28 ∼ 0.35, residential density should be no more than 0.22, and the population density of residential quarter should be under 0.29. Overall, for open and low-density residential quarters, the living environment can be improved by enriching vegetation and reducing impervious surfaces, while compact, high-density residential quarters can create a livable eco-environment by optimizing the impervious surface landscape patterns. [ABSTRACT FROM AUTHOR]