Modeling gross primary production of deciduous forest using remotely sensed radiation and ecosystem variables
Bibliographic record
Abstract
We explored the potential application of two remotely sensed (RS) variables, the Global Vegetation Moisture Index (GVMI) and the near‐infrared albedo (AlbedoNIR), in modeling the gross primary production (GPP) of three deciduous forests. For the Harvard Forest (deciduous) of Massachusetts, it was found that GPP is strongly correlated with GVMI (coefficient of determination, R2 = 0.60) during the growing season, and with AlbedoNIR (R2 = 0.82) throughout the year. Subsequently, a statistical model called the Remotely Sensed GPP (R‐GPP) model was developed to estimate GPP using remotely sensed radiation (land surface temperature (LST), AlbedoNIR) and ecosystem variables (enhanced vegetation index (EVI) and GVMI). The R‐GPP model, calibrated and validated against the GPP estimates derived from the eddy covariance flux tower of the Harvard Forest, could explain 95% and 92% of the observed GPP variability for the study site during the calibration (2000–2003) and the validation (2004–2005) periods, respectively. It outperformed the primary RS‐based GPP algorithm of Moderate Resolution Imaging Spectroradiometer (MODIS), which explained 80% and 77% of the GPP variability during 2000–2003 and 2004–2005, respectively. The calibrated R‐GPP model also explained 93% and 94% of the observed GPP variation for two other independent validation sites, the Morgan Monroe State Forest and the University of Michigan Biological Station, respectively, which demonstrates its transferability to other deciduous ecoregions of northeastern United States.
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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) | 0.001 | 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 | 0.001 | 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".