Coupling Fused High Spatio-Temporal Resolution Remote Sensing Data and Crop Modelling to Predict Wheat Yield at the Field Scale
thesis
posted on 2022-01-10, 03:20 authored by YUVAL SADEHThis study aimed to improve in-season yield predictions by coupling crop modelling and satellite images with a focus on wheat in Australia. Specifically, it has developed a new approach to predict crop yield without ground-calibration data, making it broadly applicable across regions.
History
Campus location
AustraliaPrincipal supervisor
Xuan ZhuAdditional supervisor 1
David DunkerleyAdditional supervisor 2
Jeffrey WalkerAdditional supervisor 3
Karine ChenuYear of Award
2022Department, School or Centre
Earth, Atmosphere and EnvironmentCourse
Doctor of PhilosophyDegree Type
DOCTORATEFaculty
Faculty of ScienceUsage metrics
Categories
Keywords
Remote sensingCrop yield estimationSatellitesData fusionSowing dateHarvest dateAgricultureWheatYield predictionYield mapLeaf Area IndexLAICubeSatCrop simulationAPSIMChange detectionCrop monitoringYield forecastingAgricultural EngineeringEarth Sciences not elsewhere classifiedImage ProcessingPhotogrammetry and Remote Sensing
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC