Very High Spatial Resolution Soil Moisture Observation of Heterogeneous Subarctic Catchment Using Nonlocal Averaging and Multitemporal SAR Data

A soil moisture estimation method was developed for Sentinel-1 synthetic aperture radar (SAR) ground range detected high resolution (GRDH) data to analyze moisture conditions in a gently undulating and heterogeneous subarctic area containing forests, wetlands, and open orographic tundra. In order to...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-17
Hauptverfasser:	Manninen, Terhikki, Jaaskelainen, Emmihenna, Lohila, Annalea, Korkiakoski, Mika, Rasanen, Aleksi, Virtanen, Tarmo, Muhic, Filip, Marttila, Hannu, Ala-Aho, Pertti, Markovaara-Koivisto, Mira, Liwata-Kenttala, Pauliina, Sutinen, Raimo, Hanninen, Pekka
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Catchment area Forestry High resolution Hydrology Learning algorithms Machine learning Moisture nonlocal mean filtering Pixels Resolution Root-mean-square errors SAR (radar) Soil Soil measurements Soil moisture Soil surfaces Spatial discrimination Spatial resolution Statistical methods Synthetic aperture radar synthetic aperture radar (SAR) temporal classification Training Tundra Vegetation mapping
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