Soil moisture estimation using Simulated NISAR Dual Polarimetric GRD Product over croplands
Bibliographic record
Abstract
Synthetic Aperture Radar (SAR) has immense potential in estimating soil moisture with high-resolution imaging capability and cloud independent acquisition ability. Nevertheless, estimation of soil moisture under vegetation cover is a challenging task. Notably, existing literature used ancillary data sources, such as optical data, to segregate the vegetation contribution in the backscatter. In this study, we propose a new Ground Range Detected (GRD) radar vegetation index for dual-pol data, DpRVIcthat overcomes the typical shortcomings (such as cloud cover, asynchronous observations and saturation effect for denser canopies) associated with different optical data derived indices. This proposed descriptor jointly utilizes the copol purity of the wave and normalized co-pol intensity parameter. We then use this index in the Water Cloud Model to estimate soil moisture over croplands. Furthermore, the performance of DpRVIcis compared with Normalized Difference Vegetation Index (NDVI) by utilizing the simulated NISAR L-band dual-pol data (VV-VH, HH-HV) over a Canadian test site. The proposed method has proven to be a potential alternative to synergetic approaches with Root Mean Square Error (RMSE) ranging from 5.6% to 6.0% with DpRVIcas a vegetation descriptor. Thus, the proposed vegetation descriptor provides new insights to quantify the vegetation using dual-pol GRD SAR data. Further, the adapted soil moisture technique has opened up a new avenue for soil moisture estimation using dual-pol GRD SAR data in the presence of vegetation.
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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 | 0.000 | 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 | 0.000 |
| 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 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".