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Title: Quality assurance for biophysical Earth-Observation (EO) products of vegetation flux and structure
Author: Adams, Jennifer Susan
ISNI:       0000 0004 7661 0921
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2019
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Vegetation flux and structure influence the biophysical and biogeochemical functioning processes that underpin terrestrial ecosystems. The precise quantification of rates of change within terrestrial ecosystems is important to establish if amplification mechanisms are operating through terrestrial processes, and within the climate system. Earth Observation (EO) products related to vegetation flux and structure can provide data at the spatial and temporal scales required for climate observations, change detection and modelling. The quality of EO products are paramount for their use in these applications, and detailed traceable and reliable uncertainty information needs to be provided. This thesis aims to evaluate the quality requirements of EO datasets relevant to vegetation flux and structure: land surface albedo, Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and Leaf Area Index (LAI). A 3D modelbased approach is used to benchmark processing chains, determine conformity to requirements and contributions to traceable uncertainty. This approach highlights the need for specific contributions of uncertainty, necessary to building uncertainty traceability chains, defined by a) the product definition, b) the product algorithms, c) site specific factors, d) validation measurement protocols and e) the method of testing compliance to quality requirements. Results indicate that goal requirements are unlikely to be met, particularly when stringent accuracy requirements and conformity testing methods are required. Given the current lack of detailed and traceable uncertainty information, as well as standard protocols for assessing quality, the 3D modelling approach is demonstrated to be a useful tool to EO and validation communities to better quantify and understand the quality of their data.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available