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

TL;DR

This paper introduces an attention BiLSTM model for predicting Sentinel-2 multi-band images, enabling continuous earth surface monitoring even during cloud cover, thereby improving remote sensing data reliability.

Contribution

The study presents a novel attention BiLSTM framework that forecasts multi-band satellite images on user-defined dates, including future and cloudy periods, enhancing temporal prediction accuracy.

Findings

Superior prediction of NDVI and vegetation indices

Effective forecasting during cloud-covered periods

Enhanced remote sensing data continuity

Abstract

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.