Spatial Gap-Filling of SMAP Soil Moisture Pixels Over Tibetan Plateau via Machine Learning Versus Geostatistics
Bibliographic record
Abstract
The Soil Moisture Active Passive (SMAP) satellite provides global soil moisture products with reliable accuracy since 2015. However, significant gaps of SMAP soil moisture appeared over Tibetan Plateau. To address this issue, we proposed two methods, machine learning and geostatistics technique to fill the spatial gaps of SMAP L3 soil moisture. For the machine learning technique, we train a Random Forest algorithm which aims to match the output of available SMAP L3 soil moisture using a series of input variables such as SMAP brightness temperature (TBH, TBV) in ascending orbits, surface temperature, MODIS NDVI, land cover, DEM and other auxiliary data. Then, the established RF estimators were applied to the SMAP brightness temperature from descending orbits to reconstruct complete soil moisture data over the Tibetan Plateau. For the geostatistics technique, the Ordinary Kriging was applied to the available SMAP L3 soil moisture pixels to interpolate complete soil moisture data. To cross-validate the performances of the algorithms, we assume certain areas with available SMAP SM values as missing, and then compared the gap-filling results with the actual ones. The cross-validations show that the gap-filling results from two algorithms were highly correlated to the SMAP official SM products with high coefficients of determination (R2_RF = 0.97, R2_OK = 0.85) and low RMSE (RMSE_RF = 0.015 cm3/cm3, RMSE_OK = 0.036 cm3/cm3). Furthermore, the gap-filling soil moisture data present a better correlation with the SMOS soil moisture data (R = 0.55 ~ 0.7) than the GLDAS simulations (R = 0.18 ~ 0.62).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".