On the consistency and stability of vegetation biophysical variables retrievals from Landsat-8/9 and Sentinel-2
Bibliographic record
Abstract
Systematic decametric resolution global mapping of vegetation biophysical variables, including fraction of absorbed photosynthetically active radiation (fAPAR), fraction of vegetation cover (fCOVER), and leaf area index (LAI), is required to support various activities, including climate adaptation, crop management, biodiversity monitoring, and ecosystem assessments. The Canada Centre for Remote Sensing (CCRS) version of the Simplified Level 2 Prototype Processor (SL2P-CCRS) enables global mapping of these variables using freely available medium resolution multispectral satellite data from Sentinel-2 (S2) and Landsat-8/9 (LS) data. In this study, fiducial reference measurements (RMs) from the National Ecological Observatory Network (NEON) supplemented with regional measurements from CCRS were used to evaluate the consistency between SL2P-CCRS estimates of fAPAR, fCOVER and LAI from LS and S2 data and to quantify their temporal stability. SL2P-CCRS estimates of fCOVER (Accuracy (A) ∼ 0.03, Uncertainty (U) ∼ 0.13) and fAPAR (A ∼ −0.03, U ∼ 0.13) from LS and S2 were unbiased, and generally similar between sensors, based on 6569 LS-RMs and 4932 S2-RMs matchups. However, LAI estimates, especially for woody wetlands, deciduous forest, and mixed forest, were underestimated, with better estimates obtained using S2 (A ∼ −0.33, U ∼ 0.98) than LS (A ∼ −0.43, U ∼ 1.13). For all variables, SL2P-CCRS LS estimates were highly correlated to S2 estimates overall (R2 0.80 to 0.82) but up to 35 % lower for LAI over broadleaf and mixed forests and between lower 10 % and 20 % otherwise. The inter-annual stability of SL2P-CCRS estimates from both LS and S2 fell within the Global Climate Observing System (GCOS) requirements with the mean (standard deviation) values of −0.01 yr −1 (0.06 yr −1 ) for LS LAI, 0.02 yr −1 (0.09 yr −1 ) for S2 LAI, and 0 yr −1 (0.01 yr −1 ) for fCOVER and fAPAR from both LS and S2. The stability of both S2 and LS vegetation biophysical products indicate that are well suited for quantify the physical response of vegetation to climate variability, disturbances and regeneration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".