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Wet and Dry Snow Detection Using Sentinel-1 SAR Data for Mountainous Areas with a Machine Learning Technique

Tsai, Ya-Lun and Dietz, Andreas and Oppelt, Natascha and Künzer, Claudia (2019) Wet and Dry Snow Detection Using Sentinel-1 SAR Data for Mountainous Areas with a Machine Learning Technique. Remote Sensing, 11 (8), pp. 1-29. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs11080895 ISSN 2072-4292

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Official URL: https://www.mdpi.com/2072-4292/11/8/895

Abstract

Traditional studies on mapping wet snow cover extent (SCE) often feature limitations, especially in vegetated and mountainous areas. The aim of this study is to propose a new total and wet SCE mapping strategy based on freely accessible spaceborne synthetic aperture radar (SAR) data. The approach is transferable on a global scale as well as for different land cover types (including densely vegetated forest and agricultural regions), and is based on the use of backscattering coefficient, interferometric SAR coherence, and polarimetric parameters. Furthermore, four topographical factors were included in the simple tuning of random forest-based land cover type-dependent classification strategy. Results showed the classification accuracy was above 0.75, with an F-measure higher than 0.70, in all five selected regions of interest located around globally distributed mountain ranges. Whilst excluding forest-type land cover classes, the accuracy and F-measure increases to 0.80 and 0.75. In cross-location model set, the accuracy can also be maintained at 0.80 with non-forest accuracy up to 0.85. It has been found that the elevation and polarimetric parameters are the most critical factors, and that the quality of land cover information would also affect the subsequent mapping reliability. In conclusion, through comprehensive validation using optical satellite and in-situ data, our land cover-dependent total SCE mapping approach has been confirmed to be robustly applicable, and the holistic SCE map for different months were eventually derived.

Item URL in elib:https://elib.dlr.de/129204/
Document Type:Article
Title:Wet and Dry Snow Detection Using Sentinel-1 SAR Data for Mountainous Areas with a Machine Learning Technique
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Tsai, Ya-LunYa-Lun.Tsai (at) dlr.deUNSPECIFIED
Dietz, AndreasAndreas.Dietz (at) dlr.deUNSPECIFIED
Oppelt, NataschaLMU MünchenUNSPECIFIED
Künzer, Claudiaclaudia.kuenzer (at) dlr.deUNSPECIFIED
Date:2019
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:No
Volume:11
DOI :10.3390/rs11080895
Page Range:pp. 1-29
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:synthetic aperture radar; InSAR; PolSAR; backscattering; random forest; snow cover area; land use land cover; Sentinel-2; Landsat
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Dietz, Andreas
Deposited On:18 Sep 2019 10:03
Last Modified:21 Sep 2019 05:06

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