An Apparent Thermal Inertia Based Trapezoid Model for Downscaling ESA CCI Soil Moisture Products
Bibliographic record
Abstract
Existing long-term Soil Moisture (SM) products are relatively coarse in spatial resolution, limiting their applications in heterogeneous scales. Various spectral information derived from optical satellite data, such as the Land Surface Temperature-Vegetation Parameter (LST-VP), have been widely employed to detect spatiotemporal variability of SM under different regional hydrological scales. In this study, inspired by the concept of LST-VI space, an ATI-VP (Apparent Thermal Inertia-Vegetation Parameter) was proposed and assessed for downscaling the ESA CCI SM product from 25 km to 1 km. Different vegetation indices (including NDVI, EVI, NIRv, and MSAVI) and biophysical variables (LAI and fPAR) derived from MODIS satellites were first assessed as inputs of the ATI-VP space to estimate AVDI (Apparent Thermal Inertia/Vegetation Drought Index). The AVDI was then applied to the weight decomposition model for SM downscaling. Overall, LAI for the ATI-VP space achieved the best AVDI performance. The accuracy of SM estimation was validated using in situ SM collected from the Murrumbidgee Soil Moisture Monitoring Network (MSMMN). The results showed that the accuracy of the downscaled 1km SM (R = 0.637, bias = 0.038 m3/m3) was close to that of the CCI SM (R = 0.661, bias = 0.030 m3/m3). However, the downscaled SM data exhibited enhanced spatial detail compared to CCI SM data. Further analysis based on the time series SM indicated that both the CCI SM and the downscaled SM are in good agreement in terms of temporal evolution. The downscaling method shows high potential for application in SM mapping across semi-arid regions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".