A new deep learning-based model for reconstructing high-quality NDVI time-series data in heavily cloudy areas: fusion of Sentinel 1 and 2 data

A new deep learning-based model for reconstructing high-quality NDVI time-series data in heavily cloudy areas: fusion of Sentinel 1 and 2 data

Reconstructing high-quality Normalized Difference Vegetation Index time series data is essential for ecological and agricultural applications but remains challenging in heavily cloudy areas. Fusing Sentinel SAR and optical data with deep learning could be helpful but is also challenging for stable m...

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Bibliographic Details
Main Authors: Yang Chen, Ruyin Cao, Shuaijun Liu, Longkang Peng, Xuehong Chen, Jin Chen
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:International Journal of Digital Earth
Subjects:
NDVI time-series data
Synthetic Aperture Radar data
Recurrent Neural Networks (RNN)
heavily cloudy areas
Sentinel 1/ 2
Online Access:https://www.tandfonline.com/doi/10.1080/17538947.2024.2407941

Internet

https://www.tandfonline.com/doi/10.1080/17538947.2024.2407941

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