Continuous monitoring of crops and forecasting crop conditions through time series analysis is crucial for effective agricultural management. This study proposes a framework based on an attention Bidirectional Long Short-Term Memory (BiLSTM) network for predicting multiband images. Our model can forecast target images on user-defined dates, including future dates and periods characterized by persistent cloud cover. By focusing on short sequences within a sequence-to-one forecasting framework, the model leverages advanced attention mechanisms to enhance prediction accuracy. Our experimental results demonstrate the model's superior performance in predicting NDVI, multiple vegetation indices, and all Sentinel-2 bands, highlighting its potential for improving remote sensing data continuity and reliability.

翻译：通过时间序列分析对农作物进行持续监测并预测其生长状况，对于有效的农业管理至关重要。本研究提出了一种基于注意力机制的双向长短期记忆网络（BiLSTM）框架，用于预测多波段影像。我们的模型能够预测用户指定日期（包括未来日期以及存在持续云覆盖的时段）的目标影像。通过在序列到单点的预测框架中聚焦于短序列，该模型利用先进的注意力机制来提升预测精度。实验结果表明，该模型在预测归一化植被指数（NDVI）、多种植被指数以及Sentinel-2所有波段方面均表现出优越性能，凸显了其在提升遥感数据连续性与可靠性方面的潜力。