Sentinel-1 Backscatter Assimilation Using Support Vector Regression or the Water Cloud Model at European Soil Moisture Sites

Sentinel-1 Backscatter Assimilation Using Support Vector Regression or the Water Cloud Model at European Soil Moisture Sites

Sentinel-1 backscatter observations were assimilated into the Global Land Evaporation Amsterdam Model (GLEAM) using an ensemble Kalman filter. As a forward operator, which is required to simulate backscatter from soil moisture and leaf area index (LAI), we evaluated both the traditional water cloud...

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Veröffentlicht in:IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
Hauptverfasser: Rains, Dominik, Lievens, Hans, De Lannoy, Gabrielle J. M., Mccabe, Matthew F., de Jeu, Richard A. M., Miralles, Diego G.
Format: Artikel
Sprache:eng
Schlagworte:
Backscatter
Backscattering
Computational modeling
Data assimilation
Data models
Evaporation
Kalman filters
Leaf area
Leaf area index
Modelling
Moisture
radar backscatter
Soil
Soil moisture
Soil moisture measurements
Support vector machines
support vector regression (SVR)
Vegetation mapping
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Zusammenfassung:Sentinel-1 backscatter observations were assimilated into the Global Land Evaporation Amsterdam Model (GLEAM) using an ensemble Kalman filter. As a forward operator, which is required to simulate backscatter from soil moisture and leaf area index (LAI), we evaluated both the traditional water cloud model (WCM) and the support vector regression (SVR). With SVR, a closer fit between backscatter observations and simulations was achieved. The impact on the correlation between modeled and in situ soil moisture measurements was similar when assimilating the Sentinel data using WCM ( \Delta R = +0.037 ) or SVR ( \Delta R = +0.025 ).
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2021.3073484