Improving crop yield forecasts with satellite-based soil moisture estimates: An example for township level canola yield forecasts over the Canadian Prairies
Notice bibliographique
Résumé
Satellite-derived vegetation indices are widely utilized in yield forecasting models; however, they can be heavily impacted by atmospheric conditions due to their reliance on visible and near-infrared portions of the electromagnetic spectrum. Given the importance of soil moisture (SM) for crop development, the objective of this study was to investigate the use of passive microwave-derived estimates of surface SM obtained by the SM Ocean Salinity Mission (SMOS) satellite for forecasting canola yields across the Canadian Prairies within Agriculture and Agri-Food Canada’s (AAFC) Canadian Crop Yield Forecaster (CCYF) model. Weekly SMOS SM observations were combined with climate variables and normalized difference vegetation index (NDVI) data derived from the Advanced Very High Resolution Radiometer (AVHRR) platform and used as an input for forecasting canola yields at the township-scale across the Canadian Prairies from 2010 to 2016. Top predictors were identified, and regression models were built using a robust least angle regression (RLARS) and leave-one-out cross-validation (LOOCV) scheme. SM was found to provide a better descriptor of canola stress than the more widely utilized NDVI, being selected as a predictor in 74.2 % of developed ecodistrict models over the 7-year period, compared to just 41.2 % for NDVI. The difference between model R2 values (i.e. R2diff) when SMOS SM predictors were included and excluded from the forecast, respectively, revealed varying degrees of model improvements; however, the majority of ecodistricts under study (53.3 %) showed improved model fit (i.e. R2diff > 0) with observed canola yields when SMOS SM indices were included as potential predictors within the CCYF. Overall, greater improvements in the CCYF performance were observed in Manitoba and Saskatchewan where meteorological stations are more sparsely distributed. However, performance for both sets of model inputs was relatively low with R2 values ranging from 0 to 0.74 (mean = 0.13) and from 0 to 0.52 (mean = 0.12) across the study area both when SM was included and excluded from the model, respectively. These findings suggest that while SMOS SM observations may provide a more effective indicator of canola yields, the CCYF’s performance at the township-scale, where interannual yield variability is often quite high, is limited by the short temporal satellite record.
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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 | 0,001 |
| 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 ».