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.

翻译：叶面积指数（LAI）对预测冬小麦产量至关重要。通过Sentinel-2遥感影像获取作物生长状况可能受到持续性云层的阻碍，从而影响产量预测。合成孔径雷达（SAR）可提供全天候成像，其交叉极化通道与同极化通道的比率（C波段）与冬小麦种植区的时间序列LAI高度相关。本研究评估了利用时间序列Sentinel-1 VH/VV进行LAI插补的可行性，旨在提升时空密度。我们采用双向LSTM（BiLSTM）网络对时间序列LAI进行插补，并以每个时间步的半均方误差作为损失函数。基于德国南部和华北平原的数据，我们仅使用Sentinel-1 VH/VV与Sentinel-2生成的LAI数据训练模型。实验结果表明，BiLSTM优于传统回归方法，能够捕捉多个时间序列间的非线性动态变化。该模型在不同生长条件下均表现出鲁棒性，即使在Sentinel-2影像有限的情况下仍能有效运行。BiLSTM的性能超过LSTM，尤其在作物衰老期表现更为突出。因此，BiLSTM可结合时间序列Sentinel-1 VH/VV与Sentinel-2数据用于LAI插补，该方法可推广至其他时间序列插补问题。