Bridging the Gap Between Lidar, Thermal, and Multispectral Remote Sensing for Irrigation Scheduling Applications
Notice bibliographique
Résumé
Irrigation reduces the soil moisture deficit in crop production; however, the Canadian Prairies is a semi-arid landscape with limited water resource availability, requiring careful application of water management practices. This thesis studies methods to reduce irrigation water consumption in agricultural fields with complex soil texture dynamics using the water deficit index (WDI) to indirectly detect crop water stress and measure root zone soil moisture. This index is an extension of the crop water stress index (CWSI) that uses remotely sensed surface temperature (Ts) in addition to the fraction of vegetation (fc) to estimate the crop evaporative fraction through manipulation of the available energy balance equation. Seasonal and spatial relationships between WDI and volumetric water content (VWC) over a wheat and pea crop were observed at a study site with heterogeneous soil textures over two growing seasons; wheat was planted in the first growing season, and pea was planted in the second. Ten ground-based stations were used to observe average daily fluctuations in WDI by measuring Ts and deriving fc using the normalized difference vegetation index (NDVI). Results indicated that deep layers of sandier soils are more likely to cause high variations of WDI during dry-down events. Remotely sensed WDI agrees with measured eddy covariant energy fluxes at the beginning to middle of the growing season; however, NDVI is impacted by leaf senescence after seed fill for both crops, reducing the accuracy of WDI later in the growing season because of errors in fc. Light detection and ranging (lidar) is introduced as a more sophisticated approach to obtain fc and is used as a method to validate WDI obtained using NDVI canopy fraction using unpiloted aerial vehicle (UAV) imagery during the pea growing season. Canopy fraction obtained using NDVI UAV imagery produced WDI values that agreed with canopy fraction derived using lidar demonstrating that NDVI provides accurate fc for the calculation of WDI. A technical analysis was performed to assess the accuracy of crop height models obtained using Structure from Motion (SfM) photogrammetry techniques compared to lidar. Photogrammetry crop height models were obtained using high-quality red-green-blue (RGB) imagery with accurate real-time kinematic (RTK) positioning, or RGB, multispectral and thermal imagery georeferenced using 3D ground control points (3D-GCPs); thermal and RGB SfM crop height models georeferenced using 3D-GCPs were inaccurate when compared to lidar crop heights. Further analysis was performed on identifying the empirical relationship that existed between lidar-derived fc and crop height for wheat and pea crops. The ability to track seasonal and spatial relationships between WDI and VWC, and the ability to obtain crop height models using multispectral imagery provides exciting progress at bridging the gap between thermal, multispectral and lidar remote sensing for irrigation scheduling applications.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».