Previous wildfire risk assessments have problems such as subjectivity of weight allocation and the linearization of statistical models, resulting in generally low robustness and low generalization ability of fire risk assessment models. Therefore, in this paper, we explored the potential of integration machine learning algorithms to build wildfire risk assessment models. Based on analyzing fire data’s spatial and temporal distribution, we selected 10 triggering factors of topography, meteorology, vegetation, and human activities, using frequency ratio (FR) to provide uniform data representation of triggering factors. Next, we used the Bayesian optimization (BO) algorithm to perform hyperparametric optimization solutions for various machine learning models: support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). Finally, we constructed an integration machine learning algorithm to acquire a fire risk grading map and the importance evaluation corresponding to each triggering factor. For validation purposes, we selected Liangshan Prefecture in Sichuan Province as the specific study area and obtained MCD64A1 burned area product to extract the extent of burned areas in Liangshan Prefecture from 2011 to 2020. The accuracy, kappa coefficient, and area under curve (AUC) were then applied to assess the predictive power and consistency of the fire risk classification maps. The experimental analysis showed that among the three models, FR-BO-XGBoost had the best performance in wildfire risk assessment in the Liangshan region (AUC = 0.887), followed by FR-BO-RF (AUC = 0.876) and FR-BO-SVM (AUC = 0.820). The feature importance result indicated that the study area’s most significant effects on wildfires were precipitation, NDVI, land cover, and maximum temperature. The proposed method avoided the subjective weighting and model linearization problems. Compared with the previous methods, it automatically acquired the importance of the triggering factors to the wildfire, which had certain advantages in wildfire risk assessment, and was worthy of further promotion.

Being built on the reclamation area, Shanghai Pudong International Airport (SPIA) has been undergoing uneven subsidence since the beginning of its operation in 1999. In order to explore the evolution characteristics of ground deformation in the SPIA reclamation area and further provide assurance for the airport’s safe operation, 141 Sentinel-1A images from October 2016 to September 2021 were selected to acquire time-series ground deformation observations by the StaMPS PSI processing procedure. We subsequently built a ground deformation prediction model using the Long Short Term Memory (LSTM) neural network for the short-term prediction of the SPIA deformation severity area. On this basis, the spatial-temporal evolution trends of SPIA ground deformation in the reclamation area were revealed concerning the influence and mode of action of geological conditions and environmental factors. Finally, we proposed targeted recommendations and strategies for the comprehensive ground deformation prevention and control needs of SPIA. The results indicated that the SPIA exhibits overall subsidence in the eastern part, with the maximum deformation rate reaching −57.29 mm/a. Meanwhile, the central and western part has a local uplift with the maximum deformation rate reaching 32.76 mm/a. The proposed LSTM ground deformation prediction model demonstrated excellent robustness in the region of uneven deformation, and the prediction results were in high agreement with the StaMPS PSI monitoring results. The time-series observations and prediction results are expected to provide references for the expansion project of SPIA and help the research of ground deformation and prevention in related fields.

Compared with previous snow depth monitoring methods, global navigation satellite system-interferometric reflectometry (GNSS-IR) technology has the advantage of obtaining continuous daily observation data, and has great application potential. However, since GNSS satellites are in motion, their position in the sky is constantly varying, and the Fresnel reflection areas about the receiver in different periods alter accordingly. As a result, the retrieving results obtained from different GNSS satellites, and data sets collected in different periods, fluctuate considerably, making the traditional single-satellite-based GNSS-IR retrieving method have limitations in accuracy and reliability. Therefore, this paper proposed a novel GNSS-IR signal-to-noise ratio (SNR) retrieving snow depth method for fusing the available GNSS-IR observations to obtain an accurate and reliable result. We established the retrieval model based on the backpropagation algorithm, which makes full use of the back propagation (BP) neural network’s self-learning and self-adaptive capability to exploit the degree of contribution of different satellites to the final results. Then, the SNR observations of the global positioning system (GPS) L1 carrier from the Plate Boundary Observation (PBO) site P351 were collected to experiment for validation purposes. For all available GPS L1 carrier data, the snow depth values retrieved for each satellite were first obtained by the existing single-satellite-based GNSS-IR retrieval method. Then, four groups of comparison results were acquired, based on the multiple linear regression model, random forest model, mean fusion model, and the proposed BP neural network model, respectively. Taking the snow depth in-situ data provided by snow telemetry (SNOTEL) as a reference, the root mean squared error (RMSE) and mean absolute error (MAE) of the proposed solution are 0.0297 m and 0.0219 m, respectively. Furthermore, the retrieving results are highly consistent with the measured data, and the correlation coefficient is 0.9407.

The mountainous area of southwest China is characterized by significant topography and complex geological conditions, which pose great challenges to the airport’s site selection, construction, and safe operation. Suining Anju Airport, one of the key projects under construction in southwest China, is essential in alleviating and dredging the air passenger flow in Sichuan Province. Because the overlying quaternary strata’s physical and mechanical properties, thickness, and distribution range are fairly different in the longitudinal and transverse directions, the Anju Airport’s foundation in the hilly area has typical inhomogeneity. Large-scale excavation and filling pose a challenge to the ground stability of the airport. To comprehensively monitor Anju Airport’s uneven ground subsidence during the construction period, this paper selected SAR image data collected by the Sentinel-1A satellite from May 2018 to June 2021 to extract time-series ground subsidence measurements based on the SBAS-InSAR method. Furthermore, based on the simulation of roadbed filling in the airport’s parallel slide fill area, the dynamic evolution analysis of soil stress field and internal subsidence caused by roadbed filling activities was carried out to further reveal the occurrence mechanism of ground subsidence. The monitoring results show that the subsidence centers of Anju Airport are mainly distributed in the filling areas, and the average annual subsidence is −20~−75 mm/yr from May 2018 to June 2021. Comparative analysis with in situ data indicates that the RMSE of InSAR monitoring results was ±6.12 mm. The numerical simulation shows that the subsidence of the airport parallel slide is mainly caused by a load of subgrade filling body and the compression of its weight. The results of this study can provide reference methodology and data support for the construction and future safe operation of Suining Anju Airport.

Time-series monitoring of relative surface soil moisture (RSSM) with remote sensing observation is crucial for guiding agricultural irrigation management and monitoring global climate change. However, the existing synthetic aperture radar (SAR) soil moisture retrieval algorithms suffer from insufficient decoupling of surface scattering characteristics and poor RSSM time-series monitoring capabilities. Therefore, this article proposes an integrated time-series relative soil moisture monitoring method based on a SAR backscattering model (SBM). Initially, the SBM is introduced, categorizing land cover into built-up areas, water bodies, vegetation-covered areas, and soil. Addressing the inconsistency in spatiotemporal resolution between optical vegetation indices and SAR data, we establish a unique SAR water cloud model (SWCM) in conjunction with the dual-polarization SAR vegetation index. By employing the SWCM to eliminate vegetation's influence, a high-quality soil backscatter coefficient is obtained. Ultimately, the dry and wet reference values of soil backscatter are calculated to retrieve the relative RSSM time series. Based on Sentinel-1 data, we select three representative experimental areas, namely the Qarhan Salt Lake in dry regions, the Tibetan Plateau Naqu in high-cold regions, and Inner Mongolia Xilinhot in grassland regions, conducting RSSM spatiotemporal monitoring for three years. The experimental results demonstrate that the RSSM exhibits seasonal variations in these three regions. The correlation coefficient between the RSSM monitoring results and the in situ data exceed 0.64, with a maximum of 0.84. Consequently, the proposed method underscores the advantages of simplicity in parameters, high estimation precision, and robust adaptability, thereby augmenting the potential for large-scale global monitoring applications.