Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.594382
Title: 3D crop modelling
Author: Watt, J.
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2013
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Abstract:
Crop models have become increasingly useful tools for understanding and implementing sus¬tainable agricultural techniques and as a way of accurately predicting crop yields for economists and policy decision makers. Using remotely sensed imagery can significantly reduce the effort required to obtain the in¬puts for crop models and can provide regular sets of observations throughout a growing season. Empirical models can be used to extract information regarding the crop from remotely sensed images but have well-documented limitations. Coupling a crop model with a radiative transfer model allows comparison between modelled and actual reflectance, across a range of potential crop model states. The potential difference observed can then allow for recalibration of the crop model. This technique enables the crop model to be updated throughout crop development and growth, increasing its accuracy at predicting the development of the crop. As the structure of the crop changes significantly during growth and development, affecting the remote sensing signal, a 3D structural model which can represent this change is required. This thesis presents work developing and re-parameterising an existing 3D crop model to make it more generic, as well as coupling it with a radiative transfer model. The crop model being re-parameterised is ADEL-wheat. Extensive field work spanning two growing seasons has been carried out to measure the phenological and structural differences that occurred during the growth and development of different genotypes of winter wheat. These observed differences, particularly in phenology, have been implemented within the model, and then used to test the impact on the remote sensing signal. The work shows that structural differences between genotypes tend to have a greater impact on the resulting modelled signal than phenological variation. The combined structural and radiative transfer modelling approach is shown to be very flexible and can be used to improve/augment existing crop modelling approaches.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.594382  DOI: Not available
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