Under global climate change, fragmented urban vegetation is more susceptible to the external environment, and changes in vegetation phenology are one of the most apparent responses. In this study, phenological camera (phenocamera) photo data, Klosterman curve fitting, and a Gu model were employed to explore the phenological characteristics of an urban forest at different levels within different species. Differences between species and groups regarding the upturn date (UD), the stabilization date (SD), the downturn date (DD), the recession date (RD), and the length of the growing season (LOS) are displayed in detail. We found that the UD of Cinnamomum camphora groups began in late April (day of year 108th), the SD appeared in early May (121st), and the DD started in early October (283rd) and ended in late October (293rd), with an average LOS of 185 days. The phenological characteristics of the Cinnamomum camphora and Bischofia polycarpa groups differed significantly. The average LOS of Bischofia polycarpa was 47 days longer than that of Cinnamomum camphora. Between Cinnamomum camphora individuals and group levels, differences in the UD and the SD were not obvious, while differences in the DD, the RD, and the LOS were large (LOS > RD > DD). The LOS of Cinnamomum camphora was longer on the individual scale (209 days), while the average LOS on the group scale was 185 days. In conclusion, our results reflect the more refined quantitative results of urban vegetation phenology and will help to elucidate urban vegetation phenological changes, which has important theoretical and practical significance for future urban forest management practices.

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.

Azimuthal temperature biases in multi-level tower-mounted sensors have long been reported, yet their underlying physical causes remain unclear. In particular, three candidate mechanisms—flow blockage, structural-geometry effects, and instrument-to-instrument differences in radiative sensitivity—have rarely been disentangled within a single observational experiment. Here, we address this gap by using a 100 m lattice tower at the Shouxian National Climate Observatory (32.5° N, 116.8° E), which has base and top widths of 10.0 m and 2.0 m, respectively. East–west symmetric dual temperature sensors and independent bilateral anemometers were installed at 34, 50, 65, and 80 m, with an additional reference anemometer at 95 m. On 12 May 2025, the complete probe-and-shield assembly at 50 m was reversed east–west, whereas the other three levels remained unchanged as negative controls, thereby establishing a difference-in-differences (DID) framework. Using the top-mounted reference wind direction, the bilateral wind-speed difference shows no statistically significant shadow sector at any level, allowing us to reject the flow-blockage (tower-shadow) hypothesis. The structural baseline bias S(z) of the three un-swapped decreases monotonically in magnitude with height (from −0.92 °C at 34 m to −0.28 °C at 80 m) and tracks the height-dependent tower geometry, proxied by the face width W(z), linearly, with stable day–night behavior—pointing to a geometric/orientational origin. After the swap, the 50 m bias amplifies to 3–18 times the baseline and reverses seasonally; the inter-instrument difference in daytime shortwave response is significant (Δα = +46.27 × 10⁻³ °C/(MJ m⁻² d⁻¹), 95% CI [+5.71, +91.30]), whereas the nighttime net-longwave difference is directionally consistent but not significant (Δβ = +38.33 × 10⁻³ °C/(W m⁻²), 95% CI [−1.32, +70.78]). We conclude that instrument-swap-induced homogeneity breaks are more likely caused by inter-instrument differences in radiative-response sensitivity than by a constant offset. This finding has important implications for climate-network homogenization and for azimuthal quality control of tower-mounted sensors.

Understanding of the crop carbon balance across different time scales and corresponding responses to abiotic and biotic factors is crucial for improving carbon cycle models in the context of future climate change and management practices. In this study, we employed the Random Forest (RF) algorithm, Kolmogorov-Zurbenko filtering method and structural equation modeling (SEM) to quantify the effects of abiotic and biotic factors on CO2 fluxes at various time scales based on 7-years measurements. Our results revealed that O3 primarily manifested indirect effects on NEE and GPP via altering LAI on the daily and monthly scale, and that overall regulatory effect on CO2 fluxes developed greater as the time scale increased. Net radiation (Rn) was the most critical abiotic factor altering net ecosystem exchange (NEE) and gross primary productivity (GPP) at the half-hourly, daily, and monthly scales, with the exception of photosynthetically active radiation (PAR) controlling daily NEE and GPP in the rice system. It was innovatively found that LAI had little control on detrended daily CO2 fluxes, which was much lower than the monthly CO2 fluxes. Air temperature (Ta) was the most important abiotic factor for ecosystem respiration (Reco) at half-hourly and daily scale. For NEE, Reco, and GPP, the maximum explanation of SEM models was 70.10%, 79.60% and 76.20%, respectively. The SEM results indicated that at multiple time scales, Rn exerted significant direct and indirect effects on both NEE and GPP. LAI only showed a strong direct leading effect on NEE and GPP on the monthly scale. The findings we reported have the potential to further develop carbon cycle models of cropland ecosystems under climate change by clarifying the influence path of O3 on CO2 fluxes and highlighting the factors that dominate CO2 fluxes on various time scales.