A Soil Moisture-Informed Seismic Landslide Model Using SMAP Satellite Data
Abstract
1. Introduction
2. Materials and Methods
2.1. Landslide Inventories
2.2. Landslide and Non-Landslide Data Sampling
2.3. Key Variables
- (1)
- Prior-event Normalized Soil Moisture: Averages of Root Zone Soil Moisture (RSM), Level 4 Surface Soil Moisture (SSM L4), and Level 3 Surface Soil Moisture (SSM L3) were computed over multiple pre-event windows (1 month, 2 weeks, 1 week, and 3 days). Due to temporal resolution limitations, a 3-day average was not computed for Level 3. In addition to antecedent conditions, RSM and SSM L4 values were also extracted for the day of the earthquake to represent modeled soil moisture at the time of shaking. For SMAP L3, the nearest-available value was used when event-day data were unavailable due to its coarser temporal sampling schedule. Each soil moisture value, whether from antecedent or event-day windows, was normalized using the annual minimum and maximum soil moisture values for the corresponding earthquake year, following:
- (2)
- Short-Term Soil Moisture Change: These variables quantify the percentage change in soil moisture over short-term periods leading up to the earthquake, specifically for Root Zone Soil Moisture (RSM) and Level 4 Surface Soil Moisture (SSM L4). The change is calculated between the 3-day average prior to the event and longer-term antecedent averages (1 month and 2 weeks), as well as between the event-day value and the 1-week average. These variables assess whether soils were experiencing anomalous wetting or drying prior to failure and destabilizing conditions such as post-rainfall infiltration, offering insight into potential triggering mechanisms.
- (3)
- Lagged Soil Moisture Change: Percent differences in Root Zone Soil Moisture (RSM) and Level 4 Surface Soil Moisture (SSM L4) were computed between event-day values and those recorded at lagged time steps (e.g., 3, 7, 10, and 14 days before the earthquake). These changes capture the short-term evolution of soil moisture, allowing detection of rapid wetting or drying trends that may not be evident in longer-term averages. Such variations can help identify transient hydrologic conditions conducive to slope failure under seismic loading.
2.4. Independent Variable Selection and Multicollinearity Test
2.5. Model Development
- : Overall proportion of correctly classified instances.
- : Proportion of predicted landslides that are true landslides.
- Sensitivity): Proportion of actual landslides correctly identified.
- : Harmonic mean of precision and recall, balancing both metrics.
- AUC (Area Under the ROC Curve): The ROC (Receiver Operating Characteristic) curve plots the true positive rate (TPR)—the proportion of landslide cases correctly classified as landslides by the model—against the false positive rate (FPR)—the proportion of non-landslide cases incorrectly classified as landslides—across various classification thresholds. The AUC summarizes this curve into a single value, representing the model’s ability to distinguish between classes independently of any fixed threshold.
2.6. Assessment of Model Uncertainty and Reliability
3. Results
3.1. Data Exploration: Relationships Between Variables and Landslide Occurrence
3.2. Landslide Model
4. Discussion
5. Conclusions
- Soil moisture improves model performance: Incorporating SMAP-derived surface and root-zone soil moisture indicators enhanced landslide prediction. Short-term indicators such as SSM L4 1w Avg and Δ% RSM (Pre − Day–14) ranked among the top predictors, reflecting the importance of hydrologic preconditioning in co-seismic slope failures.
- High predictive accuracy achieved: The leave-one-earthquake-out cross-validation yielded strong performance (average AUC = 0.86, F1-score = 0.78, and accuracy = 0.83), consistently outperforming the United States Geological Survey (USGS) landslide model and another well-established global Random Forest model that did not consider soil moisture.
- Dynamic variables outperform static proxies: The use of time-varying satellite-based soil moisture and rainfall (e.g., GPM) provided better insights than static proxies like TWI. This supports the transition from static to dynamic modeling in future hazard frameworks.
- Transferability demonstrated: The model showed generalizability across five diverse earthquake events in different climatic and geological settings, indicating its potential for near-real-time application in global earthquake impacts assessments.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Landslide Inventory | Magnitude | Area Exposed to Landslides (km2) | Number of Landslide Cases | Number of Non-Landslide Cases |
---|---|---|---|---|
Nippes, Haiti 2021 | 7.2 | 4000 | 4893 | 4893 |
Palu, Indonesia 2018 | 7.5 | 4000 | 7063 | 7063 |
Tari, Papua New Guinea 2018 | 7.5 | 24,000 | 11,610 | 11,610 |
Kaikoura, New Zealand 2016 | 7.8 | 10,000 | 14,412 | 14,412 |
Gorkha, Nepal 2015 | 7.8 | 30,000 | 24,843 | 24,843 |
Category | Variable Name (s) | Source |
---|---|---|
Topographic and Geological | Elevation, Slope, TRI, TPI | Digital Elevation Model (STRM) |
VS30 | [131] | |
Land Cover | Esri Sentinel-2 | |
Ground Shaking Intensity | PGA, GV | USGS |
Wetness Proxies | TWI, STI | Digital Elevation Model (STRM) |
Historical Precip | World Clim database | |
GPM precipitation | GPM Rain 1yr, 3mo, 1mo, 2w, 1w | Giovanni |
SMAP—Prior-event Normalized Soil Moisture | RSM 1mo Avg, 2w Avg, 1w Avg, 3d Avg SSM L4 1mo Avg, 2w Avg, 1w Avg, 3d Avg SSM L3 1mo Avg, 2w Avg, 1w Avg RSM Pre, SSM L4 Pre, SSM L3 Pre | National Snow and Ice Data Center |
SMAP—Short-Term Change Ratio | %Δ RSM (Pre − 1w Avg), RSM (3d Ave − 2w Avg), %Δ RSM (3d Ave − 1mo Avg) %Δ SSM L4 (Pre − 1w Avg), %Δ SSM L4 (3d Ave − 2w Avg), %Δ SSM L4 (3d − 1mo) | |
SMAP—Lag-Based Changes | %Δ SSM L4 (Pre − Day−3/7/10/14), %Δ RSM (Pre − Day−3/7/10/14) |
Variable | VIF |
---|---|
PGV | 8.2 |
Slope | 7.1 |
Elevation | 3.4 |
LC | 2.9 |
TWI | 1.8 |
Δ% RSM (Pre – Day–14) | 2.9 |
SSM L4 1w Avg | 7.8 |
GPM Rain 1w | 4.1 |
Left-Out Landslide Inventory | AUC | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|---|
Nippes, Haiti 2021 | 0.84 | 0.75 | 0.72 | 0.83 | 0.77 |
Palu, Indonesia 2018 | 0.83 | 0.77 | 0.78 | 0.76 | 0.77 |
Tari, Papua New Guinea 2018 | 0.89 | 0.81 | 0.83 | 0.76 | 0.80 |
Kaikoura, New Zealand 2016 | 0.89 | 0.80 | 0.84 | 0.75 | 0.79 |
Gorkha, Nepal 2015 | 0.84 | 0.70 | 0.63 | 0.95 | 0.76 |
Average in this study | 0.86 | 0.76 | 0.76 | 0.81 | 0.78 |
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Farahani, A.; Ghayoomi, M. A Soil Moisture-Informed Seismic Landslide Model Using SMAP Satellite Data. Remote Sens. 2025, 17, 2671. https://doi.org/10.3390/rs17152671
Farahani A, Ghayoomi M. A Soil Moisture-Informed Seismic Landslide Model Using SMAP Satellite Data. Remote Sensing. 2025; 17(15):2671. https://doi.org/10.3390/rs17152671
Chicago/Turabian StyleFarahani, Ali, and Majid Ghayoomi. 2025. "A Soil Moisture-Informed Seismic Landslide Model Using SMAP Satellite Data" Remote Sensing 17, no. 15: 2671. https://doi.org/10.3390/rs17152671
APA StyleFarahani, A., & Ghayoomi, M. (2025). A Soil Moisture-Informed Seismic Landslide Model Using SMAP Satellite Data. Remote Sensing, 17(15), 2671. https://doi.org/10.3390/rs17152671