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Autoren:
Roßberg, Thomas; Schmitt, Michael 
Dokumenttyp:
Zeitschriftenartikel / Journal Article 
Titel:
Dense NDVI Time Series by Fusion of Optical and SAR-Derived Data 
Zeitschrift:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 
Jahrgang:
17 
Jahr:
2024 
Seiten von - bis:
7748-7758 
Sprache:
Englisch 
Stichwörter:
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 
Fakultät:
Fakultät für Luft- und Raumfahrttechnik 
Institut:
LRT 9 - Institut für Raumfahrttechnik und Weltraumnutzung 
Professur:
Schmitt, Michael 
Projekt:
DESTSAM - Dense Satellite Time Series for Agricultural Monitoring 
Open Access ja oder nein?:
Ja / Yes 
Art der OA-Lizenz:
CC BY 4.0