On the use of temporal vegetation indices in support of eligibility controls for EU aids in agriculture
Bibliographic record
Abstract
The use of remote sensing in the context of the Common Agricultural Policy (CAP) has progressively become an official method to support European (EU) Member States in carrying out controls about declarations of farmers requiring EU subsidies in agriculture.Reliable automatic or semi-automatic methodologies aiming at crop identification are still being developed and the only technique, which is officially accepted in the CAP context, remains photo interpretation of high/very high (satellite or aerial) orthoimages. To verify past situations, only orthophotos can be used but, unfortunately, they are not always available. In these cases, the use of satellite sensors with adequate spatial, spectral, and temporal resolutions, together with a reliable data analysis technique, could support or even substitute orthophoto interpretation.In this study, we propose a multi-temporal, multispectral algorithm exploiting the Thematic Mapper/Enhanced Thematic Mapper Plus data on Landsat platforms to identify different land covers in the context of CAP. Here it is presented to discriminate arable from non-arable lands. Assessment of the methodology was carried out using Corine 2012 and more than 1500 validation points over Basilicata region (Southern Italy). A general good agreement was found (74%), which increases to 82% in the specific case of arable land identification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".