Comparison of different downscaling schemes for obtaining regional high-resolution soil moisture data
Bibliographic record
Abstract
• Two Soil Moisture (SM) downscaling schemes are compared over the Qinghai-Tibet Plateau (QTP). • Passive microwave brightness Temperature (TB) is downscaled to high resolution using machine learning methods. • High-resolution SM is retrieved from downscaled TB based on the radiative transfer model (RTM). • The “downscaling-then-retrieving” approach outperforms the “retrieving-then-downscaling” method over 70 % of the QTP. Obtaining regional fine-scale daily Soil Moisture (SM) data is crucial for better understanding carbon and water cycles. Currently, downscaling from passive microwave SM products has become the most commonly utilized approach for generating regional high-resolution SM data, while retrieving SM based on disaggregated brightness Temperature (TB) data gains litter attention. Besides, the relative potentials of these two downscaling approaches remains largely unknown. Therefore, this study comprehensively compared the relatively performances of the two downscaling schemes namely the “retrieving-then-downscaling” and “downscaling-then-retrieving” over the Qinghai-Tibet Plateau (QTP). Evaluation results showed that retrieving SM using disaggregated TB significantly outperformed the popular passive microwave SM downscaling method. The averaged R and ubRMSE metrics for downscale-first based SM were 0.62/0.74 and 0.051/0.038 m 3 /m 3 at station/network scales, and were 0.58/0.70 and 0.056/0.041 m 3 /m 3 for the retrieval-first based SM, respectively. Spatially, the downscale-first based SM had lower uncertainty than the retrieval-first based SM over nearly 70 % areas of the QTP. However, due to the strong vegetation scattering effect, it showed two times larger uncertainty than the retrieval-first based SM over densely vegetated regions in the east and southeast. In addition, satisfactory TB downscaling performance could be achieved by leveraging machine learning algorithms and multiple covariables, but need to further reduce additional errors. The superiority of “downscaling-then-retrieving” scheme was independent of models selected for downscaling. In conclusion, this study demonstrates the great potential of “downscaling-then-retrieving” method and provides a new insight for generating regional SM data at fine scale.
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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) | 0.000 | 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 | 0.000 |
| 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".