  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.
