Dense NDVI Time Series by Fusion of Optical and SAR-Derived Data

Dense NDVI Time Series by Fusion of Optical and SAR-Derived Data

Gaps in normalized difference vegetation index (NDVI) time series resulting from frequent cloud cover pose significant challenges in remote sensing for various applications, such as agricultural monitoring or forest disturbance detection. This study introduces a novel method to generate dense NDVI t...

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Bibliographic Details
Main Authors: Thomas Rosberg, Michael Schmitt
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Cloud removal
data fusion
deep learning
gap filling
recurrent neural network (RNN)
vegetation monitoring
Online Access:https://ieeexplore.ieee.org/document/10476648/

Internet

https://ieeexplore.ieee.org/document/10476648/

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