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Improved Estimation of Temporal Dynamics in the ASCAT Backscatter-Incidence Angle Relation Using Regularization
Author(s) -
Paco Frantzen,
Susan Steele-Dunne,
Tristan Quaife,
Mariette Vreugdenhil,
Sebastian Hahn,
Wolfgang Wagner
Publication year - 2025
Publication title -
ieee journal of selected topics in applied earth observations and remote sensing
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 1.246
H-Index - 88
eISSN - 2151-1535
pISSN - 1939-1404
DOI - 10.1109/jstars.2025.3572306
Subject(s) - geoscience , signal processing and analysis , power, energy and industry applications
The relation between microwave backscatter and incidence angle estimated from observations of the Advanced Scatterometer (ASCAT) onboard the Metop satellites contains valuable information on the dynamics of vegetation water content and structure. The relation between backscatter and incidence angle (parameterized using so-called slope and curvature parameter) has been related to vegetation water dynamics in studies on the North American Grasslands and the Cerrado Savannah. The current approach to estimate time series of the slope and curvature parameters involves a kernel smoother, weighing observations according to their temporal distance to the day of interest. While this approach provides a robust representation of backscatter-incidence angle relation over longer time scales, it does not accurately capture the timing of short-term changes. To further improve the correspondence between backscatter-incidence angle relation and vegetation water dynamics, the timing of short-term changes should be preserved in the estimation of slope and curvature. This would allow slope and curvature to be reconciled with independent estimates of biogeophysical variables, and allow us to isolate high-frequency variations due to, for example, intercepted precipitation or soil moisture. Here, an alternative method is introduced to estimate the ASCAT backscatter-incidence angle relation using temporally constrained least squares. While the proposed method yields similar performance to the kernel smoother in aggregated statistics, this method retains the timing of short-term changes.

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