Global sensitivity analysis of the radiative transfer model
Bibliographic record
Abstract
Abstract With the recently launched Soil Moisture Active Passive (SMAP) mission, it is very important to have a complete understanding of the radiative transfer model for better soil moisture retrievals and to direct future research and field campaigns in areas of necessity. Because natural systems show great variability and complexity with respect to soil, land cover, topography, precipitation, there exist large uncertainties and heterogeneities in model input factors. In this paper, we explore the possibility of using global sensitivity analysis (GSA) technique to study the influence of heterogeneity and uncertainties in model inputs on zero order radiative transfer (ZRT) model and to quantify interactions between parameters. GSA technique is based on decomposition of variance and can handle nonlinear and nonmonotonic functions. We direct our analyses toward growing agricultural fields of corn and soybean in two different regions, Iowa, USA (SMEX02) and Winnipeg, Canada (SMAPVEX12). We noticed that, there exists a spatio‐temporal variation in parameter interactions under different soil moisture and vegetation conditions. Radiative Transfer Model (RTM) behaves more non‐linearly in SMEX02 and linearly in SMAPVEX12, with average parameter interactions of 14% in SMEX02 and 5% in SMAPVEX12. Also, parameter interactions increased with vegetation water content (VWC) and roughness conditions. Interestingly, soil moisture shows an exponentially decreasing sensitivity function whereas parameters such as root mean square height (RMS height) and vegetation water content show increasing sensitivity with 0.05 v/v increase in soil moisture range. Overall, considering the SMAPVEX12 fields to be water rich environment (due to higher observed SM) and SMEX02 fields to be energy rich environment (due to lower SM and wide ranges of TSURF), our results indicate that first order as well as interactions between the parameters change with water and energy rich environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".