# Prediction of Sentinel-2 multi-band imagery with attention BiLSTM for continuous earth surface monitoring

arXiv: https://arxiv.org/abs/2407.00834 · HTML: https://mlsanity.com/paper/2407.00834/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2024.

## Built on by (most cited first, 1 of 1)

- [Remote Sensing Based Crop Health Classification Using NDVI and Fully Connected Neural Networks](https://mlsanity.com/paper/2504.10522/) (2025)

## Abstract (arXiv)

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.