Enhancing CYGNSS Soil Moisture Retrieval Accuracy by Considering the Lag Effect of Sun-Induced Fluorescence
Bibliographic record
Abstract
The Cyclone Global Navigation Satellite System (CYGNSS) has emerged as a key focus in soil moisture (SM) retrieval research due to its high temporal resolution and quasi-global coverage. The Normalized Difference Vegetation Index (NDVI) is widely used for large-scale SM retrieval due to its ability to characterize vegetation's physiological status and growth. In comparison, Sun-Induced Fluorescence (SIF) demonstrates greater sensitivity to SM variations. However, there has been limited research exploring the application of SIF in SM retrieval based on CYGNSS, and existing studies have largely overlooked the potential lag effect of SIF on SM (LESOS). To enhance the accuracy of CYGNSS-based SM retrieval, this study proposed a novel SM retrieval scheme considering the LESOS. The results indicated that there was a lag effect in the response of SIF to SM, and this effect varied significantly across different regions. After accounting for LESOS, the mean root mean square error (RMSE) of the retrieved SM across the entire study area decreased from 0.0360 cm3$\cdot$cm-3to 0.0355 cm3$\cdot$cm-3, while the Pearson correlation coefficient (PCC) increased from 0.5217 to 0.5358. In regions where the lag effect was entirely positive, both metrics showed more substantial improvements: RMSE decreased by 5.7%, and PCC improved by 10.8%. In Hunan Province, where positive lag effects predominated, retrieval accuracy was further enhanced. Moreover, the retrieved SM in the positive delay region showed better consistency with in-situ SM. The proposed method offers a novel solution for high-precision SM retrieval using SIF based on CYGNSS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".