A Spatial Downscaling Method for Remote Sensing Soil Moisture Using Adaptive Weighted Stacking Strategy
Bibliographic record
Abstract
Soil moisture (SM) derived from remote sensing plays a crucial role in understanding land-atmosphere interactions between water and carbon cycles. However, existing remotely sensed surface SM products (e.g., ESA CCI SM) have relatively coarse spatial resolutions (25 – 40 km), limiting their suitability for precision agriculture and ecological management. To address this limitation, this study proposes an adaptive weighted stacking strategy for soil moisture downscaling. A stacking framework integrating Random Forest (RF), Gradient Boosted Regression Trees (GBRT), and XGBoost was developed to downscale 25 km resolution ESA CCI SM data to a high-resolution 1km product. Key predictors, including surface albedo, apparent thermal inertia, clay content, and leaf area index, were identified through SHAP (SHapley Additive exPlanations) feature importance analysis. An adaptive weight strategy was then introduced to dynamically optimize the contributions of each base model. The downscaled SM was validated using in-situ SM measurements from the Murrumbidgee River Basin. Results indicate that both GBRT (R = 0.916, RMSE = 0.046 m³/m³) and XGBoost (R = 0.915, RMSE = 0.047 m³/m³) models significantly outperformed the RF model (R = 0.847, RMSE = 0.066 m³/m³). Notably, the stacking strategy method that combines a linear regression meta model with adaptive weighting achieved the best performance (R = 0.931, RMSE = 0.041 m³/m³). The downscaled SM exhibits finer spatial details of within-field variability compared to the original CCI SM. Further spatiotemporal analysis confirmed the downloaded SM effectively captures precipitation response and seasonal variations, particularly providing more detailed representation in farmland and pastureland regions. This study provides an effective method for high-resolution soil moisture monitoring in semi-arid areas, with significant applications in agricultural irrigation, water resource management, and climate change research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".