Assessment and Validation of AirMOSS P-Band Root-Zone Soil Moisture Products
Bibliographic record
Abstract
The Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) P-band synthetic aperture radar (SAR) was flown more than 1200 h from August 2012 to September 2015, covering regions of 2500 km2spread over nine major biomes in North America. The flights, as a part of the NASA AirMOSS Earth Venture Suborbital 1 (EVS-1) mission, collected radar data used to map root-zone soil moisture (RZSM) at 3-arcsec resolution. We previously reported the baseline retrieval algorithm and demonstrated its performance for a semiarid shrubland (Walnut Gulch, AZ, USA); we represented the RZSM profile as a continuous quadratic function and solved a radar scattering nonlinear optimization problem to obtain the unknown polynomial coefficients. In this article, we expand the retrievals to other AirMOSS sites that, in addition to the semiarid shrubland, include grassland and crops (MOISST, OK, USA), woody savanna (Tonzi Ranch, CA, USA), temperate conifer forest (Metolius, OR, USA), and boreal forest (Saskatchewan, Canada). Due to a wide range of land covers, soil types, and soil moisture regimes, we parameterize the forward model and constrain the inverse algorithm for each site separately. We present the full set of retrievals for these sites, validating the results against in situ observations. Error sources and strategies to minimize their effects are discussed. The concept of sensing depth is introduced. We find that the retrieval errors are smallest for the top 25 cm of soil with a root-mean-square error (RMSE) of less than 0.05 m3/m3. The RMSE remains around 0.06 m3/m3even for depths reaching 45 cm, which is the typical sensing depth for the sites considered. These AirMOSS RZSM products (known as Level-2/3 RZSM, or L2/3-RZSM, products) are the first of their kind in that it is the first time RZSM has been retrieved directly from remote sensing observation.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".