Abstract
Leaf area index (LAI) is one of the key parameters in crop growth monitoring and global change studies. Multiple LAI products have been generated from satellite observations, many of which suffer from data discontinuities due to persistent cloud contamination and retrieval algorithm inaccuracies. This study proposes an extended data-based mechanistic method (EDBM) for estimating LAI time series from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The data-based mechanistic model is universalized to supply the LAI background information, and then the vegetation canopy radiative-transfer model (PROSAIL) is coupled to calculate reflectances with the same observation geometry as MODIS reflectance data. The ensemble Kalman filter (ENKF) is introduced to improve LAI estimation based on the difference between simulated and observed reflectances. Field measurements from seven Benchmark Land Multisite Analysis and Intercomparison of Products (BELMANIP) sites and reference maps from the Imagine-S project La Albufera, Spain site were used to validate the model. The results demonstrate that when compared with field measurements, the LAI time-series estimates obtained using this approach were superior to those obtained with the MODIS 500 m resolution LAI product. The root mean square errors (RMSE) of the MODIS LAI product and of the LAI estimated with the proposed method were 1.26 and 0.5, respectively. When compared with reference LAI maps, the results indicate that the estimated LAI is spatially and temporally consistent with LAI reference maps. The average differences between EDBM and the LAI reference map on the selected four days was 0.32.
Highlights
Leaf area index (LAI) is a key parameter in crop growth monitoring, crop yield estimation, land surface process simulation, and global change studies
extended data-based mechanistic method (EDBM)) time series compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product (LAIMODIS ), 4the
The results indicate that the universal data-based mechanistic (UDBM) model can provide the LAI
Summary
Leaf area index (LAI) is a key parameter in crop growth monitoring, crop yield estimation, land surface process simulation, and global change studies. Researchers have focused on using crop growth models to supply the LAI estimation background, and with the coupled use of radiative transfer models and data assimilation methods, have made great progress in improving LAI estimation accuracy and yield forecasting [5,6,7,8,9,10,11,12]. The crop growth models that supplied the crop growth background and LAI dynamics in their work guaranteed estimation continuity, but were feasible only if the land cover units (crop areas) in the specific regions studied were homogeneous enough that crop-specific biophysical variables could be retrieved and that dynamic changes could be characterized by crop growth models. Bastiaanssen and Ali [13] and Mo, Liu [14]
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