Monitoring biophysical variables at a global scale over long time periods is vital to address the climatechange and food security challenges. Leaf Area Index (LAI) is a structure variable giving a measure of the canopysurface for radiation interception and canopy-atmosphere interactions. LAI is an important variable in manyecosystem models and it has been recognized as an Essential Climate Variable. This thesis aims to provide globaland continuous estimates of LAI from satellite observations in near-real time according to user requirements to beused for diagnostic and prognostic evaluations of vegetation state and functioning. There are already someavailable LAI products which show however some important discrepancies in terms of magnitude and somelimitations in terms of continuity and consistency. This thesis addresses these important issues. First, the nature ofthe LAI estimated from these satellite observations was investigated to address the existing differences in thedefinition of products. Then, different temporal smoothing and gap filling methods were analyzed to reduce noiseand discontinuities in the time series mainly due to cloud cover. Finally, different methods for near real timeestimation of LAI were evaluated. Such comparison assessment as a function of the level of noise and gaps werelacking for LAI.Results achieved within the first part of the thesis show that the effective LAI is more accurately retrievedfrom satellite data than the actual LAI due to leaf clumping in the canopies. Further, the study has demonstratedthat multi-view observations provide only marginal improvements on LAI retrieval. The study also found that foroptimal retrievals the size of the uncertainty envelope over a set of possible solutions to be approximately equal tothat in the reflectance measurements. The results achieved in the second part of the thesis found the method withlocally adaptive temporal window, depending on amount of available observations and Climatology as backgroundestimation to be more robust to noise and missing data for smoothing, gap-filling and near real time estimationswith satellite time series.
| Identifer | oai:union.ndltd.org:CCSD/oai:tel.archives-ouvertes.fr:tel-00967319 |
| Date | 13 March 2013 |
| Creators | Kandasamy, Sivasathivel |
| Publisher | Université d'Avignon |
| Source Sets | CCSD theses-EN-ligne, France |
| Language | fra |
| Detected Language | English |
| Type | PhD thesis |
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