Assessing SMAP Soil Moisture Scaling and Retrieval in the Carman (Canada) Study Site
Bibliographic record
Abstract
Core Ideas Upscaling methods compared in situ measures with soil moisture from the SMAP satellite. The accuracy of SMAP soil moisture products in annual cropland was assessed. The spatial representativeness of sparse in situ networks was determined. In 2015, NASA launched the Soil Moisture Active Passive (SMAP) satellite. Data from this satellite are being exploited to improve forecasting of extreme weather events and delivery of disaster response. International core validation sites (CVSs) have been contributing in situ soil moisture data to validate and calibrate SMAP soil moisture products. Overall the soil moisture retrieval errors have exceeded SMAP's mission requirement (errors below 0.04 m 3 m −3 ), with the exception of some sites of annual cropland as present at the Carman (Canada) CVS. In 2016, a SMAP validation experiment was conducted at the Canadian site in Manitoba (SMAPVEX16‐MB) in an attempt to understand the differences between the SMAP soil moisture retrievals and the permanent in situ network observations. The research presented here analyzed the performance of this network in representing soil moisture within a SMAP pixel and tested five upscaling approaches. Comparisons between the permanent network and SMAPVEX16‐MB measurements (from temporary stations and field measures) confirmed agreement among these three sources of soil moisture measures. The SMAP soil moisture values were compared with in situ soil moisture upscaled from the four tested approaches as well as soil moisture estimated by the NOAH Land Surface Model (LSM). There were similar discrepancies when analyzing all methods (RMSE 0.072–0.074 m 3 m −3 for the four upscaling methods; 0.076 m 3 m −3 for the LSM approach), yielding no reduction in the soil moisture RMSE for this site. The SMAP team will continue to investigate other factors that may be contributing to errors above 0.04 m 3 m −3 at these annually cropped CVSs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".