Soil moisture is an important component of the hydrologic cycle and ecosystem functioning, and it has a significant impact on agricultural production, climate change and natural disasters. Despite the availability of machine-learning techniques for estimating soil moisture from high-resolution remote-sensing imagery, including synthetic aperture radar (SAR) data and optical remote sensing, comprehensive comparative studies of these techniques remain limited. This paper addresses this gap by systematically comparing the performance of four tree-based ensemble-learning models (random forest (RF), extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM), and category boosting (CatBoost)) and three deep-learning models (deep neural network (DNN), convolutional neural network (CNN), and gated recurrent unit (GRU)) in terms of soil moisture estimation. Additionally, we introduce and evaluate the effectiveness of four different stacking methods for model fusion, an approach that is relatively novel in this context. Moreover, Sentinel-1 C-band dual-polarization SAR and Sentinel-2 multispectral data, as well as NASADEM and geographical code and temporal code features, are used as input variables to retrieve the soil moisture in the ShanDian River Basin in China. Our findings reveal that the tree-based ensemble-learning models outperform the deep-learning models, with LightGBM being the best individual model, while the stacking approach can further enhance the accuracy and robustness of soil moisture estimation. Moreover, the stacking all boosting classes ensemble-learning model (SABM), which integrates only boosting-type models, demonstrates superior accuracy and robustness in soil moisture estimation. The SHAP value analysis reveals that ensemble learning can utilize more complex features than deep learning. This study provides an effective method for retrieving soil moisture using machine-learning and high-resolution remote-sensing data, demonstrating the application value of SAR data and high-resolution optical remote-sensing data in soil moisture monitoring.

In considering regional sustainable development, optimizing the distribution of land use and land cover (LULC) and improving terrestrial ecosystem carbon storage (CS) have emerged as major concerns. In this study, considering the synergistic effect between LULC and CS, a coupling model (named MPI) that integrates Multi-objective Optimization (MOP) model, the Patch-generating Land Use Simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, was proposed to simulate the 2030 CS and explore its spatial-temporal characteristics in a Beijing-Tianjin-Hebei urban agglomeration (BTH). The MPI model, which combines the advantages of the above three models, can optimize the LULC structure, simulate the LULC distribution, and efficiently extract CS variation. The results indicated that: (1) LULC changes in BTH were mostly represented in transfers between cropland, forest, and grassland; (2) three different scenarios were simulated using the MPI model, named BAU (Business as usual), EDP (Ecological development priority), and EEB (Ecological and economic balanced). The simulation results of the three scenarios are in line with their respective goals, and the results are quite different; (3) cropland, water, and bare land, will be reduced, and the constant shrinking of water is a pressing issue that must be addressed; and (4) the EEB scenario balanced ecological services and economic rewards, increased the ecosystem carbon sink function, and is an efficient way to investigate “carbon neutrality”. The application of the MPI model is of reference value for exploring the optimal configuration of land resources.

As an important power facility for transmission corridors, automatic three-dimensional (3D) reconstruction of the pylon plays an important role in the development of smart grid. In this study, a novel three-dimensional reconstruction method using airborne LiDAR (Light Detection And Ranging) point cloud is developed and tested. First, a principal component analysis (PCA) algorithm is performed for pylon redirection based on the structural feature of the upper part of a pylon. Then, based on the structural similarity of a pylon, a pylon is divided into three parts that are inverted triangular pyramid lower structures, quadrangular frustum pyramid middle structures, and complex upper or lateral structures. The reconstruction of the inverted triangular pyramid structures and quadrangular frustum pyramid structures is based on prior knowledge and a data-driven strategy, where the 2D alpha shape algorithm is used to obtain contour points and 2D linear fitting is carried out based on the random sample consensus (RANSAC) method. Complex structures’ reconstruction is based on the priori abstract template structure and a data-driven strategy, where the abstract template structure is used to determine the topological relationship among corner points and the image processing method is used to extract corner points of the abstract template structure. The main advantages in the proposed method include: (1) Improving the accuracy of the pylon decomposition method through introducing a new feature to identify segmentation positions; (2) performing the internal structure of quadrangular frustum pyramids reconstruction; (3) establishing the abstract template structure and using image processing methods to improve computational efficiency of pylon reconstruction. Eight types of pylons are tested in this study, and the average error of pylon reconstruction is 0.32 m and the average of computational time is 0.8 s. These results provide evidence that the pylon reconstruction method developed in this study has high accuracy, efficiency, and applicability.

China’s urbanization has attracted many scholars’ attention due to its significant impact on socioeconomic sustainability. Many studies have explored the spatial pattern and effects of the factors influencing urban expansion. However, the spatiotemporal dynamics integrating spatial and temporal dimensions and the spatial scales of the influencing factors are always ignored. This study applied the framework of exploratory space–time data analysis (ESTDA) to investigate the spatiotemporal dynamics of urban expansion across 342 cities in China from 1990 to 2017 and, further, used multiscale geographically weighted regression (MGWR) to estimate the effects of influencing factors on urban expansion. We found that urban expansion had an obvious south–north division, and yet the effects of influencing factors usually showed an east–west division. We also found that the dynamic local spatial dependency of urban expansion was accompanied by a volatile coevolution process and inclined to transfer from heterogeneity to homogeneity, and homogeneity tended to be stable. The coevolution of urban expansion between cities and other neighboring ones became stronger with increases in time and regional integration. These findings support the use of customized urban planning for specific regions in different spatial dependence to improve land-use efficiency and coordinate regional development.

Dynamic predictions of surface subsidence are crucial for assessing ground damage and protecting surface buildings. Based on Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology, a method for making dynamic predictions of large-scale surface subsidence in mining areas can be established; however, the problem of phase coherence loss in InSAR data makes it impossible to predict the complete dynamic subsidence basin. In this study, a method combining the WeiBull time function and the improved probabilistic integral method (IPIM) model was established based on the PIM model, and a method for predicting the dynamic subsidence basin in the mining area was proposed by integrating the IPIM and the combined WeiBull time function. Time-series subsidence data, obtained using SBAS-InSAR, were used as fitting data, and the parameters of the combined WeiBull function were inverted, pixel by pixel, to predict the dynamic subsidence of the working face in the study area. Based on the predicted surface subsidence results of a certain moment in the working face, the parameters of the IPIM model were inverted to predict the subsidence value in the incoherent region. The subsidence predictions of the combined WeiBull time function and the IPIM model were fused using inverse distance weighting (IDW) interpolation to restore the complete subsidence basin in the mining area. This method was tested at the Wannian Mine in Hebei, and the obtained complete subsidence basin was compared with the measured data, with an absolute error range of 0 to 10 mm. The results show that the dynamic subsidence basin prediction method for the SBAS-InSAR mining area, involving the combination of the IPIM model and the combined WeiBull model, can not only accurately fit the time series of surface observation points affected by mining but also accurately restore the subsidence data in the incoherent region to obtain complete subsidence basin information in the mining area.

Exploring how human activity impacts land use/cover change (LUCC) is a hot research topic in the field of geography and sustainability management. Researchers have primarily used socioeconomic variables to measure human activity. However, the human activity indexes mainly based on socioeconomic variables have a spatial resolution that is coarser than traditional LUCC datasets, which hinders a deep and comprehensive analysis. In view of these problems, we selected China’s Lijiang River Basin as our study area and proposed the use of GPS trajectory data for analyzing the impact of human activity on LUCC from two perspectives: (1) Type of population: we used the kernel density estimation method to extract the spatial distribution of activity intensity of local residents and tourists, investigated their correlation with the LUCC result, and found these two populations have different impacts on each land cover; (2) Flow of population: we used the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a network analysis method to build a flow network of population from raw trajectories, conducted regression analysis with LUCC, and found that the flow of population is an important factor driving LUCC and is sometimes a more important factor than the static distribution of the population. Experimental results validated that the proposed method can be used to uncover the impact mechanism of human activity on LUCC at fine-grained scales and provide more accurate planning and instructions for sustainability management.