Urbanization and climate change cause the urban ecological environment to become increasingly dependent on water. However, open water areas and green spaces in cities are constantly decreasing, making water resources increasingly scarce. There is an urgent need for a method that aligns with the current urban status and can quickly assess the urban ecologicalenvironmental quality (UEEQ). Traditional UEEQ methods have abandoned the water factor, neglecting the influence of water on the ecological environment. In modern cities, water, which guarantees the operation and maintenance of the urban ecological environment, must be considered in the UEEQ system. Therefore, we propose a water benefit-based ecological index (WBEI). In the formulation of the WBEI, we integrate a water eco-factor, the thermal environment and the land cover type to represent the surface ecological environment. We first construct a surface potential water abundance index (SPWI) to describe the spatial distribution of water. The combination of the SPWI and the normalized difference latent heat index (NDLI) allows the WBEI to better evaluate the UEEQ around water areas. Then, we choose the land surface temperature (LST) to represent the thermal environment. To represent the land cover type, the ratio vegetation index (RVI) and the normalized difference soil index (NDSI) are adopted in the WBEI. Finally, we use an entropy-based fusion method to fuse these indicators and obtain the WBEI values. The performance of the WBEI is tested using eight datasets with a variety of environmental characteristics. The results show that 75% of the WBEI results are consistent with the EI values. The correlation coefficient between the WBEI and EI is 0.8883, which is significantly better than those of the other methods. The research shows that the UEEQ of the Qingdao West Coast Economic New Zone is declining continuously at a rate of 3.7% per year. From 2013 to 2017, the percentage of areas with good environments decreased by 21.46%, and the percentage of areas with poor environments increased by 12.76%. The UEEQ inside the city deteriorated radially outward along the main traffic route, the UEEQ in the suburbs did not change significantly, and the UEEQ in the water areas deteriorated significantly. These relevant research results can provide quantitative information for the green sustainable development of cities.

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Soil salinization leads to dehydration of plants, which seriously threatens ecologically sustainable development and food security guarantee. In the complex and diverse coastal wetland environment, the impervious surface and bare soil have similar spectral features with salinized soil, which make it difficult for traditional satellite data and algorithms to accurately and timely monitor the small surface features of salinization. This paper presents a Baseline-based Soil Salinity Index (BSSI) for soil salinization monitoring using medium-resolution data. In BSSI, we construct a virtual salinization baseline by connecting the nearinfrared (NIR) band and the short-wave infrared-2 (SWIR2) band to enhance the spectral feature of salinized soils which border on the impervious surface. In addition, we calculate the distance between the short-wave infrared-1 (SWIR1) band and the virtual salinization baseline as the BSSI, which can effectively improve the stability of salinity inversion for different soils. Through data comparison and model simulations, BSSI has shown advantages over a series of the traditional salinization spectral indices (SSIs). The results show that the saline soil extraction accuracy of BSSI exceeds 85% and the correlation coefficient of the BSSI and the degree of soil salinization exceeds 0.90. Since the related spectral bands, such as NIR, SWIR1, and SWIR2, are available on many existing satellite sensors such as Landsat TM/ETM+, OLI, and sentinel 2, the BSSI concept can be extended to establish long-term records for soil salinization monitoring.

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