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Autoren:
Roßberg, Thomas; Schmitt, Michael 
Dokumenttyp:
Zeitschriftenartikel / Journal Article 
Titel:
A Globally Applicable Method for NDVI Estimation from Sentinel-1 SAR Backscatter Using a Deep Neural Network and the SEN12TP Dataset 
Zeitschrift:
Journal of Photogrammetry, Remote Sensing and Geoinformation Science (PFG) 
Jahrgang:
91 
Jahr:
2023 
Seiten von - bis:
171-188 
Sprache:
Englisch 
Abstract:
Vegetation monitoring is important for many applications, e.g., agriculture, food security, or forestry. Optical data from space-borne sensors and spectral indices derived from their data like the normalised difference vegetation index (NDVI) are frequently used in this context because of their simple derivation and interpretation. However, optical sensors have one major drawback: cloud coverage hinders data acquisition, which is especially troublesome for moderate and tropical regions. One solu...    »
 
ISSN:
2512-2819 ; 2512-2789 
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