Enhancing Representativeness of Eddy Covariance Evapotranspiration With Remote Sensing and In Situ Data: A Case Study in the Brazilian Cerrado
Bibliographic record
Abstract
ABSTRACT The Cerrado sensu stricto , so‐called wooded Cerrado, is one of the many phytophysiognomies of the undisturbed Brazilian Cerrado ecoregion holding a biodiversity hotspot towards an extensive area. Thus, such land is under constant land use and cover changes mainly due to the demand for agriculture land, sector with the highest consumption of available freshwater in this ecoregion. This underscores this region's critical economic and environmental relevance. The evapotranspiration (ET) in the Brazilian Cerrado is a major player in the regional hydrological cycle, significantly influencing rainfall distribution in this ecoregion. Nonetheless, acquiring observed measurements of ET measurements using the eddy covariance (EC) technique is still challenging, especially where the flux footprint represents a heterogeneous canopy. Thus, how vegetation and spatiotemporal climate variability affect EC evapotranspiration were assessed in a preserved fragment of wooded Cerrado. Our goals were to (i) improve the water fluxes representativeness by coupling flux footprint with remote sensing products to account for spatiotemporal variability and (ii) assess how seasonal variability of vegetation and climate affect water fluxes in this study's target vegetation. First, we determined which integration approach with enhanced vegetation index (EVI) improved the representativeness of the study's site target vegetation—either a half‐hourly flux footprint integration or a fixed‐extent radius surrounding the flux tower. We further conducted a random forest analysis to identify the most relevant environmental and meteorological variables influencing the canopy conductance. We noted a significant gain in performance when EVI is integrated with the half‐hourly footprint, indicating an improvement in the representativeness between this remote sensing variable and the EC fluxes, evidenced by a better energy balance closure. And we found that the most relevant variables were the vapor pressure deficit and soil water content at a seasonal and annual basis, respectively. Our findings highlight that integrating a vegetation index with flux footprint can enhance spatiotemporal representativeness of the target vegetation, contributing to a better regional understanding of not only the wooded Cerrado but also other complex and heterogeneous land covers in terms of water and energy fluxes.
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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".