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 NASA–ISRO Synthetic Aperture Radar (NISAR) mission provides L-band soil moisture at 200 m, but validating its early products depends on retrieval flags, overpass collocation and sensor depth. We evaluated the beta Level-3 product against seven U.S. in situ networks (October 2025–January 2026) and the provisional product at 21 National Ecological Observatory Network sites (June–July 2026). Beta retrievals with flag 0, which averages all three candidate algorithms, were matched to in situ data within 60 min, merged by orbit and averaged over 0–10 cm sensors, yielding 261 site overpasses from 117 sites. Beta NISAR had a preliminary bias of 0.003 m³ m⁻³, a root mean square error (RMSE) of 0.081 m³ m⁻³ (95% interval 0.066–0.098) and $R=0.578$. Accepting all flag classes increased coverage ninefold and RMSE to 0.096 m³ m⁻³. On common site overpasses, deeper sensors did not have significantly higher RMSE than 5 cm sensors. The exploratory provisional assessment found a wet bias of 0.110 m³ m⁻³, concentrated at very dry reference sites, with a pooled R of −0.038 but an anomaly R within sites of 0.259. These estimates rest on few sites and short records; validation of early NISAR products should report flag classes, overpass construction and matched depths and separate pooled from temporal agreement.

Cyanobacterial harmful algal blooms (CyanoHABs) pose serious threats to inland water ecology and environmental security. Despite advances, accurately capturing their long-term dynamics for effective governance remains challenging due to limited datasets and segmentation model accuracy. This study addresses these gaps by developing a CyanoHABs dataset and a new model, Multi-scale Spatial Attention Network (MSA-Net), to automatically extract cyanobacterial blooms from Landsat imagery using deep learning. With enhanced multi-scale feature extraction and a Hybrid Attention Mechanism in an encoder–decoder framework, MSA-Net outperforms other models, providing robust methods and data support for remote sensing-based CyanoHAB monitoring in complex environments. The proposed MSA-Net achieved an F1-score of 88.34% and a Precision of 88.74%, outperforming all baseline methods. Ablation experiments further verified the effectiveness of the proposed Spatial Feature Enhancement Mechanism (SFEM) and Hybrid Attention Mechanism (HAM) in enhancing segmentation performance. The results highlight MSA-Net’s effectiveness in accurately identifying CyanoHABs, particularly in complex, multi-scale environments. This study provides a transferable deep learning framework and long-term remote sensing dataset for CyanoHAB monitoring, offering valuable insights for the large-scale assessment and management of eutrophic inland waters worldwide.