Peixin Zheng

Synthesizing bare soil pictures in regions with complex vegetation is challenging, which hinders the accuracy of predicting soil organic carbon (SOC) in specific areas. An SOC prediction model was developed in this study by integrating the convolutional neural network and long and short-term memory network (CNN-LSTM) algorithms, taking into consideration soil-forming factors such as climate, vegetation, and topography in Hainan. Compared with common algorithmic models (random forest, CNN, LSTM), the SOC prediction model based on the CNN-LSTM algorithm achieved high accuracy (R² = 0.69, RMSE = 6.06 g kg⁻¹, RPIQ = 1.96). The model predicted that the SOC content ranged from 5.49 to 36.68 g kg⁻¹, with Hainan in the central and southern parts of the region with high SOC values and the surrounding areas with low SOC values, and that the SOC was roughly distributed as follows: high in the mountainous areas and low in the flat areas. Among the four models, CNN-LSTM outperformed LSTM, CNN, and random forest models in terms of R² accuracy by 11.3%, 23.2%, and 53.3%, respectively. The CNN-LSTM model demonstrates its applicability in predicting SOC content and shows great potential in complex areas where obtaining sample data is challenging and where SOC is influenced by multiple interacting factors. Furthermore, it shows significant potential for advancing the broader field of digital soil mapping.

The urban heat island (UHI) effect caused by urbanization negatively impacts the ecological environment and human health. It is crucial for urban planning and social development to monitor the urban heat island effect and study its mechanism. Due to spatial and temporal resolution limitations, existing land surface temperature (LST) data obtained from remote sensing data is challenging to meet the long-term fine-scale surface temperature mapping requirement. Given the above situation, this paper introduced the ResNet-based surface temperature downscaling method to make up for the data deficiency and applied it to the study of thermal environment change in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2000 to 2020. The results showed (1) the ResNet-based surface temperature downscaling method achieves high accuracy (R² above 0.85) and is suitable for generating 30 m-resolution surface temperature data from 1 km data; (2) the area of severe heat islands in the GBA continued to increase, increasing by 7.13 times within 20 years; and (3) except for Hong Kong and Macau, the heat island intensity of most cities showed an apparent upward trend, especially the cities with rapid urban expansion such as Guangzhou, Zhongshan, and Foshan. In general, the evolution of the heat island in the GBA diverges from the central urban area to the surrounding areas, with a phenomenon of local aggregation and the area of the intense heat island in the Guangzhou-Foshan metropolitan area is the largest. This study can enrich the downscaling research methods of surface temperature products in complex areas with surface heterogeneity and provide a reference for urban spatial planning in the GBA.