Extraction and application of leaf area index´s priori knowledge in time series for typical crops

The ill-posed inversion problem is to be solved urgently. Priori knowledge is introduced to increase the inversion information to improve the inversion quality. MODIS time-series leaf area index (LAI) data is used to extract the priori knowledge. Savitzky-Golay (SG) filter and bi-Gaussian curve-fit are performed to reconstruct the original time-series LAI data, then, the upper envelope of the smoothed time-series curve is achieved to get a fixed range of LAI in a certain growing season. The variation of LAI was restricted with the range based on Look-up Table (LUT) method with the PROSAIL model. The validation results show that the priori knowledge extracted from LAI time-series data is efficiency on improving the LAI inversion precision.