Downscaling SMAP soil moisture using radarsat constellation mission (RCM) compact polarimetric SAR data: A case study in southern Ontario
Bibliographic record
Abstract
This paper presents an improved algorithm for downscaling SMAP soil moisture using C-band SAR data. It is developed using multi-temporal C-band Radarsat Constellation Mission (RCM) compact polarimetric (CP) SAR data. In this improved algorithm, the effect of vegetation on soil moisture retrieval from SAR data is minimized by using a normalized scattering based empirical model, in which vegetation contribution is quantified using the volume scattering derived from RCM CP decomposition. The influence of soil surface roughness is eliminated by using multi-temporal data. The multi-temporal SMAP soil moisture and RCM CP data (simulated from Radarsat-2 QuadPol data) are the only inputs of this downscaling model. The model is tested in southern Ontario, Canada to downscale 36 km resolution SMAP soil moisture to 1 km. The downscaled results have good agreement with the in-situ soil moisture collected in June of 2017 with an unbiased root-mean-square-error (RMSE) of 0.047 m3/m3 and a coefficient of determination (R2) of 0.43. The results suggest that the improved algorithm can be applied for C-band RCM CP data to provide continuous soil moisture mapping over large area at higher resolutions because of RCM's high-revisit frequency and large areal coverage characteristics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".