In the context of global climate governance, the study of land-use carbon emissions in the Yellow River Basin is crucial to China’s “dual-carbon” goal in addition to ecological conservation and the high-quality developments. This paper computed the land-use carbon emissions of 95 cities in the Yellow River Basin from 2000 to 2020 and examined its characteristics with respect to spatio-temporal evolution and driving mechanisms. The findings are as follows: (1) The overall net land-use carbon emissions in the Yellow River Basin rose sharply from 2000 to 2020. (2) From a spatial perspective, the Yellow River Basin’s land-use carbon emissions are high in the middle-east and low in the northwest, which is directly tied to the urban development model and function orientation. (3) A strong spatial link exists in the land-use carbon emissions in the Yellow River Basin. The degree of spatial agglomeration among the comparable cities first rose and then fell. “Low–Low” was largely constant and concentrated in the upper reaches, whereas “High–High” was concentrated in the middle and lower reaches with an east-ward migratory trend. (4) The rates of economic development and technological advancement have a major positive driving effect. Moreover, the other components’ driving effects fluctuate with time, and significant geographical variance exists. Thus, this study not only provides a rationale for reducing carbon emissions in the Yellow River Basin but also serves as a guide for other Chinese cities with comparable climates in improving their climate governance.

Rice height, as the fundamental biophysical attribute, is a controlling factor in crop phenology estimation and yield estimation. The aim of this study was to use time series Sentinel-1A images to estimate the spatio-temporal distribution of rice height. In this study, a particle filter (PF) was applied for the real-time estimation of rice height compared with a simplified water cloud model (SWCM) on the basis of rice mapping and transplanting date. It was found that the VH backscatter ($\sigma$${}_{vh}$${}^o$) can potentially be applied to accurately estimate rice height compared with VV backscatter ($\sigma$${}_{vv}$${}^o$), the $\sigma$${}_{vh}$${}^o$/$\sigma$${}_{vv}$${}^0$ ratio, and the Radar Vegetation Index (RVI, 4* $\sigma$${}_{vh}$${}^o$/($\sigma$${}_{vh}$${}^o$+$\sigma$${}_{vv}$${}^o$)). The results show that the rice height estimation by PF generated a better result with a root-mean-square error (RMSE) equal to 7.36 cm and a determination factor (R${}^2$) of 0.95 compared with SWCM (RMSE = 12.59 cm and R${}^2$ = 0.86). Moreover, rice height in the south and east of the study area was higher than in the north and west. The reason for this is that the south and east are near to the South China Sea, and there are higher temperatures and earlier transplanting. Altogether, our results demonstrate the potential of PF and $\sigma$${}_{vh}$${}^o$ to study the spatio-temporal distribution of crop height estimation. As a result, the PF method can contribute greatly to improvements in crop monitoring.

Urban spatial structure, shaped by human intervention in the natural environment, impacts the functional space and socioeconomic efficiency of cities. Against this backdrop, this study investigates the linkage between urban spatial structure (USS) and carbon emission efficiency (CEE) in China from 2012 to 2020. This paper employs a nine-year dataset and spatial econometric models involving 279 prefectural cities in China. The study innovatively employs the Herfindahl index and the super-efficiency SBM model to evaluate the level of polycentric urban structure and CEE in these cities. It further explores the trends in spatial evolution and relevant characteristics of these factors. Additionally, the study applies the SDM to explore the spillover effect of urban polycentric structure on CEE. The study makes innovative use of the Herfindahl index and the super-efficiency SBM model to evaluate the level of polycentric urban structure and CEE in these cities, further exploring the trends in spatial evolution and relevant characteristics of these factors. Additionally, it applies the SDM to explore the spillover effect of urban polycentric structure on CEE. Robustness tests are carried out to confirm the findings. The results confirm that a polycentric structure significantly enhances carbon emission efficiency within a city. However, it adversely affects neighboring cities by reducing their carbon emission efficiency through negative spatial spillover effects. The results verify that a polycentric structure significantly enhances carbon emission efficiency within a city. However, it has an adverse impact on neighboring cities by reducing their carbon emission efficiency through negative spatial spillover effects.

With the rapid development of tourism, professional tourism villages emerge one after another, which has become the focus of the tourism industry. At present, there are some problems in tourism professional villages, such as imperfect management and inaccurate prediction of future development, which affect the rational allocation of tourism resources. In order to improve the distribution of tourism resources and better predict the development of tourism professional villages, it is necessary to make comprehensive judgment and analysis, especially the analysis of indicators such as the prediction and development judgment of tourism professional villages. This paper discusses the optimization analysis of the agglomeration of tourism specialized villages by backpropagation (BP) neural network and system dynamics model, analyzes the system structure of the agglomeration factors of tourism specialized villages, and promotes the intelligent integration of the agglomeration factors. The development of clusters of professional villages promotes data integration among resources, economy, society, and other elements and presents the characteristics of big data. As the level of concentration of professional villages increases, the complexity of the associated factors also increases, which increases the difficulty and effectiveness of tourism analysis. In view of this situation, taking mountain tourism as the research object, this paper proposes an improved system dynamics model based on BP, extracts features from cross factor (resource, economic, and social) data, and optimizes the relationship between professional village agglomeration and various factors. The MATLAB simulation results show that based on the improved system dynamics analysis, the simplification rate of (resources, economy, and society) data can be controlled at more than 24%, the degree of agglomeration is more than 95%, and the construction time of the relationship map of related factors is less than 40 s. Therefore, the analysis method proposed in this paper is suitable for the calculation of the agglomeration of tourism professional villages in the mountain area and can meet the needs of the development of tourism professional villages in the mountain area.

In this article, the principles of the gray model and BP neural network model are analyzed, and the characteristics of land-use change and spatial and temporal distribution are studied in-depth, and at the same time, to explore the influence of land-use change on ESV, the relationship between the two is analyzed using gray correlation degree, and a mathematical model is constructed to maximize the benefits of the regional system, coupling economic and ecological benefits, combined with Geo SOS-FLUS model to achieve the optimization of land use. This article constructs a combined prediction model of a gray neural network. The gray differential equation parameters correspond to the weights and thresholds of the neural network, and the optimized parameters are determined by training the neural network to make it stable. Then the training results of the BP neural network are fitted with the results obtained from the gray GM (1.1) model. Finally, the prediction results of the three models, gray GM (1.1), BP God Meridian, and gray neural network model, are compared and analyzed. The global spatial autocorrelation and local spatial aggregation patterns of regional soil erosion and its erosion factors are analyzed using the Exploratory Spatial Data Analysis (ESDA) method in spatial measurement theory.

With the promotion of high-quality agricultural development and ecological protection awareness, studying the spatiotemporal dynamics and influencing factors of green transformation in arable land use (GTALU) is crucial for breaking resource constraints and achieving sustainable agriculture. However, how to quantitatively analyze the spatiotemporal interactive evolution patterns and the comprehensive effect paths of main factors in GTALU remains challenging. Therefore, this research uses exploratory spatiotemporal data analysis (ESTDA) methods to investigate the spatiotemporal evolution pattern of GTALU in the Yellow River Basin (YRB) from 2005 to 2022. In addition, a ‘individual-interaction-comprehensive’ analytical framework was constructed by combining the optimal parameter geographical detector (OPGD) and geographically weighted regression-improved structural equation model (GWR-SEM) models to identify the main influencing factors of GTALU and to interpret its comprehensive spatial non-stationarity characteristics. The research results show that: (1) from 2005 to 2022, GTALU in the YRB exhibited an ‘N’-shaped characteristic. (2) GTALU in the YRB presented a feature of ‘dynamic fluctuations under stable aggregation’, with strong path dependence and lock-in effects. (3) DEM, SP, ALR, PD, and II significantly influenced GTALU, with II playing a dominant role. (4) The combined effect of main factors showed significant spatial non-stationarity characteristics. Among them, II and SP have significant positive impacts in Shandong province and the southwest of the research area; DEM, ALR, and PD have significant negative impacts in the middle reaches of the YRB, the “Hohhot-Baotou-Ordos-Ulanqab” urban agglomeration, and the three cities of Jiuquan-Jiayuguan-Zhangye, respectively. This research provides scientific support for formulating differentiated GTALU development strategies according to local conditions.