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Authors:
Roßberg, Thomas; Schmitt, Michael 
Document type:
Zeitschriftenartikel / Journal Article 
Title:
Dense NDVI Time Series by Fusion of Optical and SAR-Derived Data 
Journal:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 
Volume:
17 
Year:
2024 
Pages from - to:
7748-7758 
Language:
Englisch 
Keywords:
Cloud removal ; data fusion ; deep learning ; gap filling , recurrent neural network (RNN) ; vegetation monitoring 
Abstract:
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 time series without these gaps, enhancing the reliability and application range of NDVI time series. We combine Sentinel-2 NDVI time series containing cloud-induced gaps with NDVI time series derived f...    »
 
ISSN:
1939-1404 ; 2151-1535 
Department:
Fakultät für Luft- und Raumfahrttechnik 
Institute:
LRT 9 - Institut für Raumfahrttechnik und Weltraumnutzung 
Chair:
Schmitt, Michael 
Project:
DESTSAM - Dense Satellite Time Series for Agricultural Monitoring 
Open Access yes or no?:
Ja / Yes 
Type of OA license:
CC BY 4.0