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.

Vegetation net primary productivity (VNPP) is the main factor in ecosystem carbon sink function and regulation of environmental processes. However, NPP data products have data missing in some areas, which affects the availability and overall accuracy level of data. Therefore, we adopted the Factor Analysis Backpropagation neural network model (FA-BP model) to acquire a high-accuracy and high-reliability NPP result without missing or empty areas by using a series of easily accessible datasets, such as meteorological data and remote sensing data. We selected the western Sichuan region as the study area and carried out a VNPP time series estimation from 2000 to 2016. Comparative simulations also verify the accuracy of the time series estimation results: The Pearson correlation r of VNPP prediction results ranged from 0.807 to 0.917, the mean absolute error ranged from 29.1 to 38.9, the root mean square error was between 37.3 and 51.8, and the mean relative error varies from 0.10 to 0.14. Further analysis shows that the spatial pattern of estimated VNPP during the past 17 years in western Sichuan shows a decreasing trend from southeast to northwest. Besides, the VNPP time series is generally on an upward trend in this period. The increasing and decreasing areas of VNPP values in the study area accounted for 81.42% and 18.58%, respectively. Moreover, we find that temperature dominates the change of VNPP in the whole western Sichuan region. The data processing method and experimental results presented in this paper can provide a reference for accurately acquiring VNPP and related studies on natural resources and climate change.

An emerging microwave remote sensing technology, global navigation satellite system interferometry reflectometry (GNSS-IR) shows the excellent application potential in vegetation remote sensing due to its all-weather and high temporal resolution characteristics. Previous studies for GNSS-IR have mainly concentrated on the global positioning system (GPS) signal-to-noise ratio (SNR) data. Because of the similarity between GPS and Beidou Navigation Satellite System (BDS), the signal of BDS can also be impacted by vegetation, which, however, has not been comprehensively researched. Therefore, this letter acquires the normalized amplitude of the BDS SNR data based on triple-frequency SNR observations collected by two stations with different vegetation types. To reveal the impact of different vegetation growth conditions on the BDS multifrequency signals, we conduct a comparative analysis with normalized difference vegetation index (NDVI) data obtained by Sentinel-2 and Moderate-Resolution Imaging Spectroradiometer (MODIS) imagery. The outcomes demonstrate that the normalized amplitude of the BDS signal exhibits a strong association with Sentinel-2 NDVI, with correlation coefficients varying from 0.69 to 0.83 and 0.78 to 0.84 at P041 and P105, respectively. In addition, it is the first time discovered that harvesting vegetation around the stations leads to a cliff-like decrease in normalized amplitude. Compared with optical remote sensing vegetation monitoring methods, BDS-IR shows significant temporal resolution and sensitivity advantages. This finding may promote vegetation monitoring, vegetation protection, and other related research fields.