Wetland cities were proposed by the International Wetland Convention Organization for the protection of urban wetlands. Few studies have performed land cover classifications for internationally recognized wetland cities or explored what contribution the classification results can make to the establishment of additional wetland cities to date. Based on Sentinel-1 and 2 data, this study used a 10-fold random forest method to classify the land cover of the first six wetland cities recognized in China. A land cover dataset, which had a resolution of 10 m and included four wetland types, was obtained and the wetland area and protected wetland areas of the six cities were calculated. The results showed that (1) the classification accuracy of six cities was good, the overall accuracy was above 90%, and the Kappa coefficient was above 0.88. (2) Cropland or forested areas were the most common non-wetland land coverage type in wetland cities and accounted for more than 20% or 40% of the land coverages, while water was the most common wetland type and accounted for more than 2% of the land coverages. From 2015 to 2020, the built area in most cities increased, while cropland and forest decreased significantly. (3) The wetland rate was 6.68–37.56% and the wetland protection rate was 49.48–73.74% in the six wetland cities. From 2015 to 2020, the wetland rate of the six cities were relatively stable, and the wetland protection rate of inland cities (Yinchuan, Changde, Harbin and Changshu) increased significantly, while those of coastal cities (Haikou and Dongying) decreased, which might be related to the change in coastline. Therefore, we found that the wetlands in these cities were well protected. Land cover classification for wetland cities can provide a reference for using remote sensing techniques used to monitor internationally wetland cities while also supporting the creation of additional wetland cities.

As one of the most open and dynamic regions in China, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has been urbanizing rapidly in recent decades. The surface water in the GBA also has been suffering from urbanization and intensified human activities. The study aimed to characterize the spatiotemporal patterns and assess the losses and gains of surface water caused by urbanization in the GBA via long time-series remote sensing data, which could support the progress towards sustainable development goals (SDGs) set by the United Nations, especially for measuring SDG 6.6.1 indicator. Firstly, utilizing 4750 continuous Landsat TM/ETM+/OLI images during 1986–2020 and the Google Earth Engine cloud platform, the multiple index water detection rule (MIWDR) was performed to extract surface water extent in the GBA. Secondly, we achieved surface water dynamic type classification based on annual water inundation frequency time-series in the GBA. Finally, the spatial distribution and temporal variation of urbanization-induced water losses and gains were analyzed through a land cover transfer matrix. Results showed that (1) the average minimal and maximal surface water extents of the GBA during 1986–2020 were 2017.62 km² and 6129.55 km², respectively. The maximal surface water extent fell rapidly from 7897.96 km² in 2001 to 5087.46 km² in 2020, with a loss speed of 155.41 km² per year (R² = 0.86). (2) The surface water areas of permanent and dynamic types were 1529.02 km² and 2064.99 km² during 2000–2020, accounting for 42.54% and 57.46% of all water-related areas, respectively. (3) The surface water extent occupied by impervious land surfaces showed a significant linear downward trend (R² = 0.98, slope = 36.41 km² per year), while the surface water restored from impervious land surfaces denoted a slight growing trend (R² = 0.86, slope = 0.99 km² per year). Our study monitored the long-term changes in the surface water of the GBA, which can provide valuable information for the sustainable development of the GBA urban agglomeration. In addition, the proposed framework can easily be implemented in other similar regions worldwide.

Sustainable Development Goals (SDGs) target 11.a is a good vision for the coordinated development of the economy, society and environment in urban agglomerations. However, there was an extreme lack of indicators, data or case studies for SDG target 11.a, since it is a vague “process target”, which is not conducive to the implementation of SDG target 11.a. It is important to propose a quantitative, convenient, and local policies relevant method to promote the realization or to test the implementation effects of SDG target 11.a. Combined with socio-economic data and land use data, this study uses the methods of comprehensive evaluation model, coupling and coordination degree, and comparative advantage degree methods to study the pattern evolution, coordination characteristics and advantageous areas of production–living–ecological (PLE) functions in the Guangxi Beibu Gulf Urban Agglomeration (GBG_UA) from 1995 to 2019. The results showed that, (1) considering the spatiotemporal distribution of PLE functions, the study area has a relatively stable ecological function as well as fluctuating production and living functions. Considering the coordination characteristics of PLE functions, high–high and low–low clustering effects were observed, and primary coordination maintained the highest proportion, accounting from 55.26% in 1995 to 71.05% in 2019, indicating the SDG target 11.a level in the GBG_UA was poor. Considering the advantageous areas for PLE functions, the region mostly comprises single-function advantageous areas and a few multifunction advantageous areas, including 20 single-function advantage counties (accounting for 52%), 15 dual-function advantage counties (accounting for 39%), and three multi-function advantage counties (accounting for 7.8%), which indicates the lack of diversified land use structures in this region. (2) Optimization suggestions for the coordinated development and realization of SDG target 11.a for the GBG_UA were provided. Suggestions were made based on the radiation and driving role of Nanning city to guide the coordinated development of surrounding counties (districts). Suggestions were also made to improve the design of the integrated transportation network as well as to optimize allocation according to the resource endowment of land and to realize an upgraded ecology as well as agricultural products and services. (3) The evaluation of PLE functions is a quantitative and convenient method that can optimize national and regional development planning and test the implementation effects of SDG target 11.a. This study offers foundational knowledge for the realization of SDG target 11.a in the GBG_UA and provides a reference for the research and implementation of SDG target 11.a in other regions around the world.