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Urban areas contribute to over 80% of carbon dioxide emissions, and considerable efforts are being undertaken to characterize spatiotemporal variations of CO2 (carbon dioxide) at a city, regional, and national level, aiming at providing pipelines for carbon mission reduction. The complex underlying surface composition of urban areas makes process-based and physiology-based models inadequate for simulating carbon flux in this context. In this study, long short-term memory (LSTM), support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were employed to develop and investigate their viability in estimating carbon flux at the ecosystem level. All the data used in our study were derived from the long-term chronosequence observations collected from the flux towers within urban complex underlying surface, along with meteorological reanalysis datasets. To assess the generalization ability of these models, the following statistical metrics were utilized: coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Our analysis revealed that the RF model performed the best in simulating carbon flux over long time series, with the highest R2 values reaching up to 0.852, and exhibiting the smallest RMSE and MAE values at 0.293 μmol·m−2·s−1 and 0.157 μmol·m−2·s−1. As a result, the RF model was chosen for simulating carbon flux at spatial scale and assessing the impact of urban impervious surfaces in the simulation. The results showed that the RF model performs well in simulating carbon flux at the spatial scale. The input of impervious surface area index can improve the performance of the RF model in simulating carbon flux, with R2 values of 84.46% (with the impervious surface area index in) and 83.74% (without the impervious surface area index in). Furthermore, the carbon flux in Fengxian District, Shanghai, exhibited significant spatial heterogeneity: the CO2 flux in the western part of Fengxian District was less than in the eastern part, and the CO2 flux gradually increased from the west to the east. In addition, we creatively introduced the diurnal impervious surface area index based on the Kljun model, and clarified the influence of impervious surface on the spatiotemporal simulation of CO2 flux over the complex urban underlying surface. Based on these findings, we conclude that the RF models can be effectively applied for estimating carbon flux on the complex underlying urban surface. The results of our study reduce the uncertainty in modeling carbon cycling in terrestrial ecosystems, and make the variety of models for the carbon cycling of terrestrial ecosystems more diverse. [ABSTRACT FROM AUTHOR]

• We collected and analyzed 111,814 social media reviews from 50 green spaces in Shanghai. • Recreational activity and social interaction received the most recognition by residents. • Based on the public's perception of ecosystem cultural services, urban green spaces can be divided into four types. • Sentiments toward natural landscapes and supporting facilities substantially influence landscape perceptions. Urban green spaces (UGSs) facilitate the interaction of residents with blue and green infrastructure. Various cultural ecosystem services (CESs) generated by UGSs are reflected in social media data, and continuous efforts are needed to consistently characterize citizens' perceptions of CESs by mining increasingly available social media data. For 50 UGS sites in Shanghai, we established a perception lexicon to cluster CESs, and we analyzed the impacts of landscape elements on citizens' sentiments via text analytics. Nine types of landscape elements and five types of CESs were identified. Among the five CES types, recreational activities and social interaction were perceived the most frequently, while aesthetic appreciation was perceived the least frequently. Furthermore, the UGS sites were classified into social interaction-oriented, outdoor workout-oriented, history and culture-oriented, or multi-functional spaces. Multivariate regression analysis on the sentiments expressed by reviews revealed the significant impacts of natural landscape elements (plants, animals, and water bodies) and supporting facilities on the perceived sentiments. This study demonstrates the utility of social media data for identifying the CESs experienced by citizens, and the information generated through this approach is potentially useful for urban planners, landscape architects, and policymakers. [ABSTRACT FROM AUTHOR]