A data fusion algorithm based on the Kalman filter to estimate leaf area index evolution in durum wheat by using field measurements and MODIS surface reflectance data

The use of 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 although estimated results were often affected by noise and measurement uncertainties. In this article, we propose a Kalman filter algorithm in order to estimate the time evolution of LAI by combining field-measured and Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data. The scalar equation of the dynamic LAI model (state transition model) was derived by the field-measured data while the MODIS red, near-infrared and shortwave infrared reflectance data were used to implement the observation model. The reflectance data were linked to LAI by using the reduced simple ratio. The method was tested in an experimental field located in the north-western part of the Apulia region (Italy). The results showed a good agreement between the LAI estimated through the algorithm and the LAI derived from field data, with a coefficient of determination (R2) of 0.96 and a corresponding root mean square error of 0.124.