Characterizing satellite-derived freeze/thaw regimes through spatial and temporal clustering for the identification of growing season constraints on vegetation productivity
Notice bibliographique
Résumé
Vegetation growth and productivity in Canada's boreal are governed by a characteristically short growing season, which is largely driven by the Freeze/Thaw(F/T) cycles that constrain the supply of water and nutrients through seasonally frozen soils. Since much of the vegetation in the Canadian boreal consists of evergreen species which do not experience large seasonal cycles in photosynthetic biomass, monitoring this growing season through the use of visible and near-infrared wavelengths via spectral indices such as the Normalized Difference Vegetation Index (NDVI) has proven difficult. To adequately capture growing season constraints in these northern environments, microwave remote sensing offers potential. L-band passive microwave observations are sensitive to near-surface soil moisture conditions and can monitor F/T states effectively due to the high contrast in permittivity between frozen and thawed soils. We characterize F/T information using products from both the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions. Daily F/T retrievals are classified into distinct growing season phases based on a 7-day moving window approach and are used to generate a suite of 12 temporal metrics over the 2017–2019 period including growing season length, timing of the fall freeze transition, ephemeral F/T events, and the total number of state transitions. Several key metrics are also generated from in-situ soil temperature datasets for comparison with the SMOS and SMAP datasets. Uncorrelated F/T metrics were then leveraged to delineate unique regions of F/T-derived growing season characteristics using a K-means clustering approach. Regions derived from SMOS and SMAP F/T retrievals were assessed for their ability to capture unique spatial constraints on vegetation productivity with reference to modelled Gross Primary Productivity (GPP) obtained from SMAP and a MODIS/Fluxnet synergy product. Our results indicate that both SMOS and SMAP-derived F/T metrics correspond with unique spatial patterns in vegetation productivity, illustrating the F/T cycle constraints on the seasonal availability of soil moisture, nutrients and suitable soil temperatures required for vegetation productivity across the Canadian boreal. In addition, the relationship between the SMAP and SMOS F/T-derived growing season length metrics and reference GPP yielded rates of change at 5.30 and 5.64 gC m−2 yr−1 per 1-day increase in growing season length. These estimated rates of change are similar to those identified by studies using complex process-based ecosystem models and in-situ eddy covariance data from flux towers. These similarities highlight the potential of this simple and robust remotely sensed approach for capturing climatic drivers of land cover and vegetation productivity not currently represented in common Canadian ecological regions.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».