Generating spatial-temporal continuous LAI time-series from Landsat using neural network and meteorological data

High-quality Leaf Area Index (LAI) time-series is important for many ecological applications. Unfortunately, troubles of observations missing and low spatial-temporal resolution greatly restrict their further applications. Due to the increasing of algorithm uncertainty, the current time-series optimized (TSO) algorithms perform poorly when LAI observations are lost continuously or unavailable on the key phenology nodes. It is an effective way of improving performance of TSO by introducing prior knowledge which replenishes time-series detail information. Meteorological data is completely competent since its great potential of describing vegetation growing rules. In this paper, focusing on data missing trouble, we develop a new LAI time-series reconstruction algorithm, called MNNR (Meteorology and Neural Network based Reconstruction), by introducing external meteorological data and other prior information into a neural network model. The results demonstrate that the proposed MNNR algorithm is well capable of spatial-temporal LAI reconstruction and performs excellently when observations are lost continuously.