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The linking of ecosystem service flows (ESFs) with urban land management is still in its nascent stage. The spatial process modeling of ESFs plays a crucial role in establishing connections between urban land sustainability and human benefits. However, the spatial processes and driving mechanisms associated with urban cooling services (UCS) remain ambiguous. In this study, we selected the area within the 6th Ring Road of Beijing as the study area, where the population is highly concentrated and the urban greenery is relatively developed. We modeled the spatial processes of cooling service flow (UCSF) in this area and elucidated the contribution of landscape patterns to UCSF. Firstly, the cooling capacity, referred to as UCS, of the urban blue–green landscape, was estimated using the InVEST tool. Subsequently, the UCSF spatial process was simulated by employing a two-dimensional Gaussian function at the pixel level. In order to characterize the landscape features in the study area, eight landscape indices were selected, and Fragstats v4.2 was employed for their calculation. Finally, GeoDetector was utilized to explore the driving mechanisms of landscape patterns on UCSF. The predominant area for both UCS and UCSF lies between the 5th and 6th Ring Road in Beijing, exhibiting a declining trend from the 6th Ring Road toward the city center. The UCSF coverage area, which represents the beneficiary area, accounted for approximately 87.78% of the study area, with the largest increase occurring within the 2nd Ring Road. The Landscape Shape Index demonstrated the strongest individual contribution to UCSF, while its combined bivariate contribution was significant. Geometry exerted a greater influence on UCSF compared to landscape scale and spatial configuration. This study presents novel insights for assessing the omnidirectional flow of ESFs through the modeling of flow functions. The findings of this study can serve as a valuable reference for sustainable urban landscape management and planning.

Soil is an important natural resource. The excessive amount of heavy metals in soil can harm and threaten human health. Therefore, monitoring of soil heavy metal content is urgent. Monitoring soil heavy metals by traditional methods requires many human and material resources. Remote sensing has shown advantages in the field of monitoring heavy metals. Based on 971 heavy metal samples and Sentinel-2 multi-spectral images in Tai Lake, China, we analyzed the correlation between six heavy metals (Cd, Hg, As, Pb, Cu, Zn) and spectral factors, and selected As and Hg as the input factors of inversion model. The correlation coefficient of the best model of As was 0.53 (p < 0.01), and of Hg was 0.318 (p < 0.01). We used the methods of partial least squares regression (PLSR) and back propagation neural network (BPNN) to establish inversion models with different combinations of spectral factors by using 649 measured samples. In addition, 322 measured samples were used for accuracy evaluation. Compared with the PLSR model, the BP neural network builds the model with higher accuracy, and B1-B4 combined with LnB1-LnB4 builds the model with the highest accuracy. The accuracy of the best model was verified, with an average error of 19% for As and 45% for Hg. Analyzing the spatial distribution of heavy metals by using the interpolation method of Kriging and IDW. The overall distribution trend of the two interpolations is similar. The concentration of As elements tends to increase from north to south, and the relatively high value of Hg elements is distributed in the east and west of the study area. The factories in the study area are distributed along rivers and lakes, which is consistent with the spatial distribution of heavy metal enrichment areas. The relatively high-value areas of heavy metal elements are related to the distribution of metal products factories, refractory porcelain factories, tile factories, factories and mining enterprises, etc., indicating that factory pollution is the main reason for the enrichment of heavy metals.