Improved soil moisture retrieval during crop growing season using passive microwave data at L-band
Bibliographic record
Abstract
Soil moisture (SM) is a critical driver of crop growth and a key indicator of agriculture monitoring. Efficient and accurate SM monitoring is essential for advancing sustainable agricultural practices. The zero-order radiative transfer model (τ-ω), widely used for SM estimation from passive microwave satellites, simplifies the characterization of vegetation-covered surfaces by neglecting multiple scattering effects. However, its canopy transmissivity calculations within this framework remain limiting. To address this gap, this study proposes a ratio-based approach to quantify vegetation transmissivity, capitalizing on unique features of passive microwave radiative transfer over vegetated surfaces. The method is then integrated into the Single Channel Algorithm (SCA) and the L-band Microwave Emission of the Biosphere (L-MEB) model, respectively, to improve the accuracy of SM retrieval. To mitigate the weather-related constraints of optical data, the study is further to investigate the integration of the Radar Vegetation Index (RVI) into the enhanced models. Experimental results demonstrate that the ratio-based method effectively refines vegetation transmissivity estimations, thereby enhancing SM retrieval accuracy: For L-MEB model, R increased from 0.63 to 0.72 and RMSE decreased from 0.119 m 3 /m 3 to 0.061 m 3 /m 3 ; for SCA-H and SCA-V, R increased from 0.48 and 0.54 to 0.71 and 0.63, and RMSE decreased from 0.150 m 3 /m 3 and 0.102 m 3 /m 3 to 0.053 m 3 /m 3 and 0.067 m 3 /m 3 , respectively. This study presents a novel approach for SM retrieval. This work underscores the potential of the ratio-based method to improve SM assessments across diverse environmental and climatic conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".