A data fusion Kalman filter algorithm to estimate leaf area index evolution by using Modis LAI and PROBA–V top of canopy synthesis data

Antonio Novelli

doi:10.1117/12.2240733

12 August 2016 A data fusion Kalman filter algorithm to estimate leaf area index evolution by using Modis LAI and PROBA–V top of canopy synthesis data

Antonio Novelli

Author Affiliations +

Proceedings Volume 9688, Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016); 968813 (2016) https://doi.org/10.1117/12.2240733
Event: Fourth International Conference on Remote Sensing and Geoinformation of the Environment, 2016, Paphos, Cyprus

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.

Citation Download Citation

Antonio Novelli "A data fusion Kalman filter algorithm to estimate leaf area index evolution by using Modis LAI and PROBA–V top of canopy synthesis data", Proc. SPIE 9688, Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016), 968813 (12 August 2016); https://doi.org/10.1117/12.2240733

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