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RSS FeedsRemote Sensing, Vol. 11, Pages 285: A Generic First-Order Radiative Transfer Modelling Approach for the Inversion of Soil and Vegetation Parameters from Scatterometer Observations (Remote Sensing)

 
 

2 february 2019 02:00:10

 
Remote Sensing, Vol. 11, Pages 285: A Generic First-Order Radiative Transfer Modelling Approach for the Inversion of Soil and Vegetation Parameters from Scatterometer Observations (Remote Sensing)
 




We present the application of a generic, semi-empirical first-order radiative transfer modelling approach for the retrieval of soil- and vegetation related parameters from coarse-resolution space-borne scatterometer measurements ( σ 0 ). It is shown that both angular- and temporal variabilities of ASCAT σ 0 measurements can be sufficiently represented by modelling the scattering characteristics of the soil-surface and the covering vegetation-layer via linear combinations of idealized distribution-functions. The temporal variations are modelled using only two dynamic variables, the vegetation optical depth ( τ ) and the nadir hemispherical reflectance (N) of the chosen soil-bidirectional reflectance distribution function ( B R D F ). The remaining spatial variabilities of the soil- and vegetation composition are accounted for via temporally constant parameters. The model was applied to series of 158 selected test-sites within France. Parameter estimates are obtained by using ASCAT σ 0 measurements together with auxiliary Leaf Area Index ( L A I ) and soil-moisture ( S M ) datasets provided by the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land-surface model within the SURFEX modelling platform for a time-period from 2007–2009. The resulting parametrization was then used used to perform S M and τ retrievals both with and without the incorporation of auxiliary L A I and S M datasets for a subsequent time-period from 2010 to 2012.


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18 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 286: IoU-Adaptive Deformable R-CNN: Make Full Use of IoU for Multi-Class Object Detection in Remote Sensing Imagery (Remote Sensing)
Remote Sensing, Vol. 11, Pages 284: Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method (Remote Sensing)
 
 
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