Data-based mechanistic modelling and validation for leaf area index estimation using multi-angular remote-sensing observation time series

Abstract

Spatially and temporally complete leaf area index (LAI) time series are required for crop growth monitoring, forest biomass estimation, and land surface process simulation studies. Global LAI products currently available include the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product. However, data quality still needs to be improved, especially with respect to temporal continuity. In this research, a new approach has been developed to estimate LAI time series using the data-based mechanistic (DBM) modelling procedure. Both the nadir viewing reflectance and anisotropic index (ANIX) time series derived from the MODIS product are used in LAI_DBM modelling and estimation, where the ANIX values are used as an auxiliary variable to represent the bidirectional reflectance anisotropy of the vegetation canopy. Both the MOD09GA multi-angular remote-sensing observations and the MOD15A2 LAI products are used in the LAI time series modelling and retrieval procedure. Ground measurements at typical vegetation sites are used to validate the estimated LAI. The preliminary results show that: (1) the new LAI_DBM approach using nadir viewing reflectance observation and ANIX time series can be used to improve the continuity of estimated LAI time series. The disturbance noise introduced by using the MOD09A1 directional reflectance observations directly can thus be reduced. (2) An ANIX time series can represent the vegetation canopy bidirectional reflectance anisotropy information and its dynamic changes. It works well in the retrieval procedure for improving LAI time series estimation. (3) The preliminary retrieval results demonstrate that the estimated LAIs can achieve better time series continuity than the original MODIS LAI product.