Monitoring Wheat and Rapeseed by Using Synchronous Optical and Radar Satellite Data—From Temporal Signatures to Crop Parameters Estimation
Bibliographic record
Abstract
This paper investigates the sensitivity of multi-temporal SAR data acquired at different frequencies (X-, C-and L-bands), polarizations (HH, VV, VH and HV) and incidence angles (from 24° to 53°) during the growing season of two winter crops (rapeseed and wheat). This study was part of a multi-sensor crop-monitoring experiment that was performed from February to November 2010 (MCM’10). During the experiment, dense series of satellite data were acquired in microwave, optical and thermal domains (more than 150 images were provided by TerraSAR-X, Radarsat-2 Alos, Formosat-2, Spot-4/5 and Landsat-5/7) were synchronous with ground measurements over an agricultural area located in southwestern France, near Toulouse. An angular normalization of radar signals is first performed for each crop type at X-and C-bands by using a dense temporal satellite series and the complementarity provided by microwave and optical data. The results show that the angular sensitivity of radar backscatter decreases with the increase of the vegetation index (from 0.4 dB.°ˉ1 over bare soils to 0.05 dB.°ˉ1 for fully vegetated fields). Lower angular sensitivity is observed at X-band (compared to C-band), and for the cross-polarized signal. Analyses of the temporal signatures of the radar backscatter show a well-marked signal dynamic at X-, C-and L-bands, depending on the crops and theirs associated phenological stages. During the stems elongation of wheat while the NDVI increases of 0.2, a dynamic of 10 dB is observed at X-band and at C-band with VV polarization. Interesting behaviors are also observed during the crop senescence with an increase of several dB (depending on the sensor configuration), while the NDVI decreases of 0.5. Over rapeseed, cross-polarized backscatters offer promising dynamic of 6 dB during the seed development, while the NDVI saturates at maximum values. The use of radar signals, in complement of optical, for crop parameters monitoring is achieved in terms of leaf area index and crop height estimations. Over rapeseed, best correlations between crop parameters and radar signals are obtained at C-band, by combining co-and cross-polarized backscatters (R2 > 0.61). Over wheat, best results are achieved by using X-band data (R2 > 0.64).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".