Towards a standardized, ground-based network of hyperspectral measurements: Combining time series from autonomous field spectrometers with Sentinel-2
Notice bibliographique
Résumé
Sentinel-2 satellite data enables multispectral monitoring of the earth at a high temporal revisit rate. Combining this information with a network of optical ground measurements enables a more detailed and a more complete understanding of terrestrial ecosystems . However, independent optical ground measurements often lack consistency, especially when comparing different sites in geographically remote locations. Using the very high temporal and spectral resolution offered by the automated field spectrometer systems FloX and RoX (Fluorescence Box and Reflectance Box, respectively, JB-Hyperspectral Devices GmbH, Duesseldorf, Germany), we investigated continuous time series ranging over three years and in ten different locations across Europe, Africa, America and Asia . The continuous records of ground-measured reflectance were first validated against Sentinel-2 top of canopy (TOC) reflectance to evaluate the consistency of the in-situ network. Our results suggest a good agreement of ground-measured reflectance with Sentinel-2 TOC reflectance in vegetation and snow with R 2 around 0.79 in the 833 nm band and R 2 up to 0.94 in the bands around 559 nm and 492 nm, demonstrating good consistency across the network. Spatial misalignment of Sentinel-2 pixel-sizes with respect to the different footprint sizes of the ten automated spectrometers on the ground, atmospheric uncertainties, sub-optimal instrument setup and spatial-temporal variable landscape heterogeneity were identified as the most relevant sources of uncertainties in the network. Comparing the Normalized Difference Vegetation Index (NDVI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI) and Enhanced Vegetation Index (EVI) between ground and satellite revealed a decreasing agreement with increasing complexity of index formulation. The best agreement between satellite and ground was exhibited by NDVI with R 2 around 0.96 and relative error of 4.3% investigating vegetation and snow across all ten sites. Furthermore, we identified a seasonal pattern in residuals of NDVI between ground and satellite in an alpine ecosystem in northern Italy, which was associated with increased spatial heterogeneity due to the effects of diverse vegetation phenology and snowfall. In contrast, a random distribution of residuals was recognized in a rather uniform oak forest canopy in southern France. Clustering Sentinel-2 pixels with respect to their temporal patterns in NDVI identified similar areas seen as homogenous in the canopy of Torgnon, Italy, and Observatoire de Haute-Provence (OHP), France, each. The very high temporal resolution of NDVI measured on the ground confirmed overlap with matched homogenous areas, but must consider seasonal landscape heterogeneity. Using well-standardized and globally homogenous Sentinel-2 TOC reflectance enabled the assessment of uncertainties in ten field spectrometer sites around the world. The standardization of the automated field spectrometers, their data products and data annotation were essential prerequisites that enabled joint validation against Sentinel-2. Harmonizing optical ground measurements with respect to a satellite is promising for future research to ensure the valid intercomparison and transfer of data products across different sites in a network worldwide.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».