Towards a Global Model for NDVI Estimation from Sentinel-1 SAR Backscatter
Titel Konferenzpublikation:
EUSAR 2022
Untertitel Konferenzpublikation:
14th European Conference on Synthetic Aperture Radar, July 25 - 27, 2022, Leipzig, Germany Electronic Proceedings
Konferenztitel:
European Conference on Synthetic Aperture Radar (14., 2022, Leipzig)
Tagungsort:
Leipzig
Jahr der Konferenz:
2022
Datum Beginn der Konferenz:
25.07.2022
Datum Ende der Konferenz:
27.07.2022
Verlagsort:
Berlin ; Offenbach
Verlag:
VDE Verlag
Jahr:
2022
Seiten von - bis:
245-248
Sprache:
Englisch
Abstract:
Vegetation monitoring using remotely sensed data is useful for many applications, for example crop yield prediction. Many of these applications utilize the normalized difference vegetation index (NDVI) acquired using space-borne optical sensors. Using the NDVI however has one drawback: cloud coverage prevents data acquisition. To tackle this we present a method to estimate the NDVI from cloud penetrating radar sensors using a convolutional neural network (CNN). This model is trained with a global, balanced dataset called SEN12TP consisting of temporally paired Sentinel-1 and cloud-free Sentinel-2 images together with auxiliary data. A good performance is achieved with this globally applicable model. Additionally we show that radiometric terrain correction of the radar backscatter is unnecessary if the model is provided with the elevation data. «
Vegetation monitoring using remotely sensed data is useful for many applications, for example crop yield prediction. Many of these applications utilize the normalized difference vegetation index (NDVI) acquired using space-borne optical sensors. Using the NDVI however has one drawback: cloud coverage prevents data acquisition. To tackle this we present a method to estimate the NDVI from cloud penetrating radar sensors using a convolutional neural network (CNN). This model is trained with a globa... »
ISBN:
978-3-8007-5823-4
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