  The Leaf Area Index (LAI) is vital for predicting winter wheat yield.
Acquisition of crop conditions via Sentinel-2 remote sensing images can be
hindered by persistent clouds, affecting yield predictions. Synthetic Aperture
Radar (SAR) provides all-weather imagery, and the ratio between its cross- and
co-polarized channels (C-band) shows a high correlation with time series LAI
over winter wheat regions. This study evaluates the use of time series
Sentinel-1 VH/VV for LAI imputation, aiming to increase spatial-temporal
density. We utilize a bidirectional LSTM (BiLSTM) network to impute time series
LAI and use half mean squared error for each time step as the loss function. We
trained models on data from southern Germany and the North China Plain using
only LAI data generated by Sentinel-1 VH/VV and Sentinel-2. Experimental
results show BiLSTM outperforms traditional regression methods, capturing
nonlinear dynamics between multiple time series. It proves robust in various
growing conditions and is effective even with limited Sentinel-2 images.
BiLSTM's performance surpasses that of LSTM, particularly over the senescence
period. Therefore, BiLSTM can be used to impute LAI with time-series Sentinel-1
VH/VV and Sentinel-2 data, and this method could be applied to other
time-series imputation issues.
