Water quality monitoring of medium-sized inland water is important for water environment protection given the large number of small-to-medium size water bodies in China. A case study was conducted on Yuandang Lake in the Yangtze Delta region, with a surface area of 13 km². This study proposed utilising a multispectral uncrewed aerial vehicle (UAV) to collect large-scale data and retrieve multiple water quality parameters using machine learning algorithms. An alternate processing method is proposed to process large and repetitive lake surface images for mapping the water quality data to the image. Machine learning regression methods (Random Forest, Gradient Boosting, Backpropagation Neural Network, and Convolutional Neural Network) were used to construct separate water quality inversion models for ten water parameters. The results showed that several water quality parameters (COD_Mn, temperature, pH, DO, and NC) can be retrieved with reasonable accuracy (R² = 0.77, 0.75, 0.73, 0.67, and 0.64, respectively), although others (NH₃-N, BGA, TP, Turbidity, and Chl-a) have a determination coefficient (R²) less than 0.6. This work demonstrated the tremendous potential of employing multispectral data in conjunction with machine learning algorithms to retrieve multiple water quality parameters for monitoring medium-sized bodies of water.

This study explores the quality of data produced by Global Precipitation Measurement (GPM) and the potential of GPM for real-time short-term nowcasting using MATLAB and the Short-Term Ensemble Prediction System (STEPS). Precipitation data obtained by rain gauges during the period 2015 to 2017 were used in this comparative analysis. The results show that the quality of GPM precipitation has different degrees efficacies at the national scale, which were revealed at the performance analysis stage of the study. After data quality checking, five representative precipitation events were selected for nowcasting evaluation. The GPM estimated precipitation compared to a 30 min forecast using STEPS precipitation nowcast results, showing that the GPM precipitation data performed well in nowcasting between 0 to 120 min. However, the accuracy and quality of nowcasting precipitation significantly reduced with increased lead time. A major finding from the study is that the quality of precipitation data can be improved through blending processes such as kriging with external drift and the double-kernel smoothing method, which enhances the quality of nowcast over longer lead times.

This paper proposes a bespoke urban sustainability indicator framework in the context of China’s prevalent property-led urban development. Emphasising local characteristics and incorporating underlying institutions, it advocates a more nuanced, holistic and dynamic approach when addressing sustainability issues. Selection of indicators were based on extensive literature reviews and tested through an international expert survey comprising both China-based and overseas-based experts. The two groups of experts have shown divergent views, with the former prioritising economic and institutional aspects over environmental and social factors. It also provides transferable policy insights into developing countries more generally, given many similarities in broader development challenges. Discussion on recent literature and urban development reinforces the applicability of these tailor-made indicators to not only monitoring but also explaining and predicting urban changes. We argue it is necessary to recognise the centrality of property-led urban development in urban sustainable development, and the need for examining the complex relations between the property sector and urban sustainability via inclusion of institutional analysis and a multi-method approach combining quantitative and qualitative evaluations.