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Proceedings Paper

A data fusion Kalman filter algorithm to estimate leaf area index evolution by using Modis LAI and PROBA–V top of canopy synthesis data
Author(s): Antonio Novelli
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Paper Abstract

Leaf Area Index (LAI) is essential in ecosystem and agronomic studies, since it measures energy and gas exchanges between vegetation and atmosphere. In the last decades, LAI values have widely been estimated from passive remotely sensed data. Common approaches are based on semi-empirical/statistic techniques or on radiative transfer model inversion. Although the scientific community has been providing several LAI retrieval methods, the estimated results are often affected by noise and measurement uncertainties.

The sequential data assimilation theory provides a theoretical framework to combine an imperfect model with incomplete observation data. In this document a data fusion Kalman filter algorithm is proposed in order to estimate the time evolution of LAI by combining MODIS LAI data and PROBA-V surface reflectance data. The reflectance data were linked to LAI by using the Reduced Simple Ratio index. The main working hypotheses were lacking input data necessary for climatic models and canopy reflectance models.

Paper Details

Date Published: 12 August 2016
PDF: 8 pages
Proc. SPIE 9688, Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016), 968813 (12 August 2016); doi: 10.1117/12.2240733
Show Author Affiliations
Antonio Novelli, Politecnico di Bari (Italy)


Published in SPIE Proceedings Vol. 9688:
Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016)
Kyriacos Themistocleous; Diofantos G. Hadjimitsis; Silas Michaelides; Giorgos Papadavid, Editor(s)

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