Combination of Optical and SAR Sensors for Monitoring Biomass Over Corn Fields
Bibliographic record
Abstract
In this study, a cross-calibration approach was applied to combine RADARSAT-2 and RapidEye sensors for biomass monitoring over corn fields. First, RapidEye and RADARSAT-2 sensors were compared in terms of biomass estimation. Then the estimated biomass from RADARSAT-2 was cross-calibrated with respect to the biomass estimated from RapidEye. Combination of the optical and cross-calibrated Synthetic Aperture Radar (SAR) derived biomass was proposed to have higher temporal resolution biomass maps. Vegetation indices including normalized difference vegetation index (NDVI), red-edge triangular vegetation index (RTVI), simple ratio (SR) and red-edge simple ratio (SRre) were used for modeling of biomass estimation from RapidEye. Water Cloud Model (WCM) was also used for biomass estimation from RADARSAT-2. Data collected during SMAP Validation Experiment 2012 (SMAPVEX12) field campaign was used for validation. The results demonstrate that the accuracies of biomass estimations from RapidEye and RADARSAT-2 are close. For RapidEye, the highest accuracies derived from RTVI index with correlation coefficient (R) of 0.92 and Root Mean Square of (RMSE) of 118.18 gr/m2. The R values derived from RADARSAT-2 is 0.83 and its RMSE is 171.93 gr/m2. After cross-calibration of the biomass derived from RADARSAT-2 versus those derived from RapidEye, the RMSE of estimates dropped by 18.86 gr/m2.
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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 | 0.000 |
| 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 | 0.000 | 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".