The impact of spatiotemporal variability of environmental conditions on wheat yield forecasting using remote sensing data and machine learning
Notice bibliographique
Résumé
• Zone-based ML for field-level yield forecasting using environmental and remote sensing variables. • Spatial and temporal variability of the input variables affect ML accuracy. • Field management and weather shifts cause monthly fluctuations in ML accuracy. Climate change poses significant challenges to food security, especially in semi-arid agriculture areas. Effective monitoring of crop yield is important for establishing food emergency responses and developing long-term sustainable strategies. In Morocco, where cereals are the predominant crops, yield forecasting is important for addressing the yield gap as it enables farmers to take preventive actions before the harvesting period. This study aims to assess the impact of spatial and temporal heterogeneity of environmental conditions on wheat yield forecasting using machine learning models. It compares the 2019–2020 and 2020–2021 agricultural seasons using three sets of variables: (1) spectral indices; (2) weather data; and (3) a combination of both spectral indices and weather data. Weather data, including cumulative monthly precipitation from ERA5 data and average monthly temperature from PERSIANN data, were extracted for the wheat growing season (November to June). Spectral indices including the Normalized Difference Vegetation Index, Moisture Stress Index, and Terrestrial Chlorophyll Index were calculated from Sentinel-2 imagery for the same period and processed using Google Earth Engine. The study area was divided into homogeneous zones based on an existing landform classification, and XGBoost and Random Forest (RF) models were used for yield forecasting in each zone separately. The two models performed equally well across both the zones and the whole study area (SA) when using weather data as the input variable. For instance, across SA, they achieved average R 2 values of 0.60 and 0.81 for all months during the 2019–2020 and 2020–2021 agricultural seasons, respectively. However, when using spectral indices or combining these indices with weather data, RF consistently outperformed XGBoost. For example, in SA during the 2019–2020 season, RF achieved an average R 2 of 0.48 across the growing season, compared to XGBoost’s R 2 of 0.43. Similarly, in the 2020–2021 season, RF achieved an R 2 of 0.35 and an RMSE of 1083 kg ha -1 , while XGBoost performed slightly lower, with an R 2 of 0.29 and an RMSE of 1137 kg ha -1 . Comparing the prediction accuracy between the seasons for each set of variables, the RF model performs better when using spectral indices during the relatively dry 2019–2020 season as compared to the wet 2020–2021 season. Incorporating weather data, the model improved its performance for the 2020–2021 season. April showed the highest prediction performance overall, with R 2 values of 0.6 for SA using weather data alone in the 2019–2020 season, and 0.8 for SA using a combination of weather data and spectral indices in the 2020–2021 season. The 2019–2020 season showed strong fluctuations in accuracy throughout the growing season, whereas the 2020–2021 season had a consistent improvement in accuracy over time. These variations in accuracy are due to differing environmental conditions that should be taken into account for making better and more reliable yield predictions.
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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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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 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 ».