Monitoring crop biomass accumulation using multi-temporal hyperspectral remote sensing data
Bibliographic record
Abstract
The estimation of the above-ground dry phytomass accumulation is important for monitoring crop growth, predicting potential yield, and estimating crop residues in the context of the carbon cycle. Hyperspectral remote sensing has been proven to be a very effective tool for the estimation of crop variables such as LAI, pigment and water content; therefore it is reasonable to expect that data from hyperspectral remote sensing can show great potential for monitoring crop biomass accumulation, either directly or indirectly through other variables. The objective of this study is to investigate the relationships between optical indices and either crop dry mass or height using multi-temporal, multi-field hyperspectral data. Using the Compact Airborne Spectrographic Imager (CASI), hyperspectral data were acquired in three deployments during the 2001 growing season over corn, soybean and wheat fields in the former Greenbelt Farm of Agriculture and Agri-Food Canada in Ottawa. High correlation was observed between the measured above-ground crop dry biomass and the other two parameters, crop height and leaf area index (LAI). The vegetation index MTVI2, calculated from hyperspectral images, was used to estimate the accumulated absorbed photo-synthetically active radiation (APAR) for the monitoring of crop biomass production. Both dry biomass and crop height were highly correlated with the accumulated APAR. For all the samples from the three dates, the coefficient of determination (R/sup 2/) between the estimated APAR and crop dry mass was 0.95, 0.99 and 0.76, and R/sup 2/ between the estimated APAR and crop height was 0.90, 0.89 and 0.70 for corn, soybean and wheat, respectively. However, further analysis shows that the correlation between biomass increment and the accumulated APAR during a short period of time is much lower for wheat. This demonstrates that apart from APAR, biomass accumulation is affected by other factors as well.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".