Accurate landslide susceptibility mapping is vital for disaster forecasting and risk management. To address the problem of limited accuracy of individual classifiers and lack of model interpretability in machine learning-based models, a coupled multi-model framework for landslide susceptibility mapping is proposed. Using Jiuzhaigou County, Sichuan Province, as a case study, we developed an evaluation index system incorporating 14 factors. We employed three base models—logistic regression, support vector machine, and Gaussian Naive Bayes—assessed through four ensemble methods: Stacking, Voting, Bagging, and Boosting. The decision mechanisms of these models were explained via a SHAP (SHapley Additive exPlanations) analysis. Results demonstrate that integrating machine learning with ensemble learning and SHAP yields more reliable landslide susceptibility mapping and enhances model interpretability. This approach effectively addresses the challenges of unreliable landslide susceptibility mapping in complex environments.

The Gravity Recovery and Climate Experiment (GRACE) enables large-scale monitoring of terrestrial water storage changes, significantly contributing to hydrology and related fields. However, the coarse resolution of groundwater storage anomaly (GWSA) data limits local-scale research utilizing GRACE and GRACE-FO missions. In this study, we develop a regional downscaling model based on the linear regression relationship between GWSA and environmental variables, reducing the grid resolution of GWSA obtained from GRACE from approximately 25 km to 1 km. First, we estimate the missing values of monthly continuous terrestrial water storage anomaly (TWSA) for the period from 2003 to 2020 using interpolated multi-channel singular spectrum analysis (IMSSA). Next, we apply the water balance equation to separate GWSA from TWSA, which is provided jointly by the Global Land Data Assimilation System (GLDAS) and the distributed ecohydrological model ESSI-3. We then employ a partial least squares regression (PLSR) model to identify the most significant environmental variables related to GWSA. Precipitation (Prec), normalized difference vegetation index (NDVI), and actual evapotranspiration (AET), with variable importance in projection (VIP) values greater than 1.0, are recognized as effective variables for reconstructing long-term, high-resolution groundwater storage changes. Finally, we downscale and reconstruct the long-term (2003–2020), high-resolution (1 km × 1 km) monthly GWSA in the Songhua River Basin using fused and supplemented GRACE/GRACE-FO data, employing either geographically weighted regression (GWR) or random forest (RF) models. The results demonstrate superior performance of the GWR model (CC = 0.995, NSE = 0.989, RMSE = 2.505 mm) compared to the RF model in downscaling. The downscaled GWSA in the Songhua River Basin not only achieves high spatial resolution but also exhibits improved accuracy when compared to in situ groundwater observation records. This research enhances understanding of spatiotemporal variations in regional groundwater due to local agricultural and industrial water use, providing a scientific basis for regional water resource management.

Complex terrain, the sparse distribution of rain gauges, and the poor resolution and quality of satellite data in remote areas severely restrict the development of watershed hydrological modeling, meteorology, and ecological research. In this study, based on the relationship between cloud optical and physical properties and precipitation, a daily geographically weighted regression (GWR) precipitation downscaling model was constructed for the Three Rivers Source region, China, for the period from 2010 to 2014. The GWR precipitation downscaling model combined three different satellite precipitation datasets (CMORPH, IMERG, and ERA5) which were downscaled from a coarse resolution (0.25° and 0.1°) to a fine resolution (1 km). At the same time, the preliminary downscaling results were calibrated and verified by employing the geographic difference analysis (GDA) and geographic ratio analysis (GRA) methods combined with rainfall data. Finally, the analytical hierarchy process (AHP) and the entropy weight method (EW) were adopted to fuse the three downscaled and calibrated satellite precipitation datasets into the merged satellite precipitation dataset (MSP), which provides a higher quality of data (CC = 0.790, RMSE = 2.189 mm/day, and BIAS = 0.142 mm). In summary, the downscaling calibration and precipitation fusion scheme proposed in this study is suitable for obtaining high-resolution daily precipitation data in the Three Rivers Source region with a complex climate and topography.

Obtaining high-quality precipitation data with both high spatial and temporal resolution is imperative for hydrological and meteorological research. However, the coarse resolution and uncertain data quality of most satellite data, coupled with sparse rain gauge station (RGS), limit their direct applicability in scientific research. Downscaling satellite data, particularly in conjunction with RGS, proves to be an effective approach to overcome this challenge. In this study, we utilize the geographically weighted regression kriging (GWRK) model to downscale GPM IMERG monthly precipitation data from 2001 to 2020. Leveraging spatially heterogeneous relationships with DEM, slope, LST, and soil moisture in the Songhua River Basin in Northeast China, we enhance the spatial resolution from 0.1° to 1 km. Initially achieving a 1.4% increase in data accuracy, with a CC value of 0.966. Subsequently, employing the daily fraction method, the downscaled precipitation data are disaggregated to the daily scale and calibrated by merging RGS using the geographical difference analysis (GDA) method. The outcome is high-quality daily precipitation data with both high spatial resolution and accuracy (CC=0.818, RMSE=3.188, ME=0.086). An analysis of the annual variation of precipitation in the Songhua River Basin over the past two decades reveals an increasing trend. Spatially, the average annual precipitation variation rate in the basin increases from the middle to both ends, with the increasing trend gradually decreasing from south to north. The proposed approach provides a practical solution for enhancing the spatiotemporal scale of satellite data, improving data quality, and addressing the sparse distribution of RGS.

Domain transformation is playing an increasingly important role in heterogeneous change detection. Existent methods, however, cannot guarantee the consistency of reconstructed feature spaces. To solve this issue, we propose an unsupervised change detection method based on the unification of feature space (UFS). First, we construct a convolutional autoencoder based on adaptive instance normalization to unify feature space. Then, we use fuzzy local information c-means to reduce the over-reliance on reconstructed features. Finally, we design a dynamic superpixel-based label assignment (DSLA) rule to increase the number of reliable pseudo-labels used to learn a binary classifier to obtain the CD results. Experimental results on heterogeneous datasets demonstrate the effectiveness of UFS.