Space-based spectrodirectional measurements for the improved estimation of ecosystem variables
Bibliographic record
Abstract
n this paper, four unique information sources of the Compact High Resolution Imaging Spectrometer (CHRIS) onboard the Project for On-Board Autonomy (PROBA-1) are exploited, namely, the spectral, directional, spatial, and temporal dimensions. Based on the results of three case studies in Switzerland, the use of multi-angular CHRIS-PROBA data for monitoring complex and dynamic vegetation canopies of forests and agricultural crops is demonstrated. We conclude that simultaneous exploitation of the spectrodirectional and temporal behaviours of various vegetation canopies allows for assessing the biochemical and biophysical properties on the one hand and provides additional information on canopy structure via the directional component on the other hand. The study cases focus on various aspects of combining these information dimensions for improved retrieval of vegetation characteristics, namely, (i) the vegetation heterogeneity measurements that use the Minnaert function parameter k, (ii) an improved assessment of foliar water content (CW) and nitrogen concentration (CN) based on multi-angular data, and (iii) continuous leaf area index (LAI) time-profiles lead to more accurate estimates of ecosystem processes and inventorying studies. The first study’s assessment of canopy structure and heterogeneity from multi-angular data using Minnaert’s k successfully demonstrates the distinction between closed and medium-density canopies. The second case study shows that the assessment of plant biochemistry from remotely sensed data profits from the information gained from multi-angular datasets. A synergistic approach that integrates multiple sources of information for the estimation of LAI over the season produces promising results for crop growth monitoring in the third case study. CHRIS-PROBA’s multi-angular observations at the regional scale, while having a comparable spatial resolution of Landsat satellites, can significantly contribute to a better understanding of regional surface anisotropy. This strengthens the link between field observations and canopy scale applications. The results of the three case studies clearly demonstrate the potential and value of spectrodirectional Earth observations at regional scales for ecological monitoring and modeling studies.
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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 | 0.000 |
| 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".