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Authors:
Roßberg, Thomas; Schmitt, Michael 
Document type:
Zeitschriftenartikel / Journal Article 
Title:
A Globally Applicable Method for NDVI Estimation from Sentinel-1 SAR Backscatter Using a Deep Neural Network and the SEN12TP Dataset 
Journal:
Journal of Photogrammetry, Remote Sensing and Geoinformation Science (PFG) 
Volume:
91 
Year:
2023 
Pages from - to:
171-188 
Language:
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 
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