Estimation of Vegetation Structure Parameters from SMAP Radar Intensity Observations

Discrete scatterer; Soil Moisture Active Passive (SMAP); Backscattering; Electromagnetic wave emission; Incoherent scattering; Polarimeters; Radar; Soil moisture; Soil surveys; Tropics; Vegetation; Backscatter intensity; Estimation approaches; Model based decompositions; Orientation distributions; Polarimetric decomposition; Spatial heterogeneity; Vegetation scattering; Parameter estimation

Abstract :

[en] In this article, we present a multipolarimetric estimation approach for two model-based vegetation structure parameters (shape A P and orientation distribution ψ of the main canopy elements). The approach is based on a reduced observation set of three incoherent (no phase information) polarimetric backscatter intensities (| HH |2, | HV |2, and | S\VV |2) combined with a two-parameter (A P and ψ) discrete scatterer model of vegetation. The objective is to understand whether this confined set of observations contains enough information to estimate the two vegetation structure parameters from the L-band radar signals. In order to disentangle soil and vegetation scattering influences on these signals and ultimately perform a vegetation-only retrieval of vegetation shape A P and orientation distribution ψ, we use the subpixel spatial heterogeneity expressed by the covariation of co-and cross-polarized backscatter Γ PP-PQ of the neighboring cells and assume it is indicative for the amount of a vegetation-only co-to-cross-polarized backscatter ratio μ PP-PQ. The ratio-based retrieval approach enables a relative (no absolute backscatter) estimation of the vegetation structure parameters which is more robust compared to retrievals with absolute terms. The application of the developed algorithm on global L-band Soil Moisture Active Passive (SMAP) radar data acquired from April to July 2015 indicates the potential and limitations of estimating these two parameters when no fully polarimetric data are available. A focus study on six different regions of interest, spanning land cover from barren land to tropical rainforest, shows a steady increase in orientation distribution toward randomly oriented volumes and a continuous decrease in shape arriving at dipoles for tropical vegetation. A comparison with independent data sets of vegetation height and above-ground biomass confirms this consistent and meaningful retrieval of AP and ψ. The retrieved shapes and orientation distributions represent the main vegetation elements matching the literature results from model-based decompositions of fully polarimetric L-band data at the SMAP spatial resolution. Based on our findings, A P and ψ can be directly applied for parameterizing the vegetation scattering component of model-based polarimetric decompositions. This should facilitate decomposition into ground and vegetation scattering components and improve the retrieval of soil parameters (moisture and roughness) under vegetation. © 1980-2012 IEEE.

Disciplines :

Environmental sciences & ecology
Earth sciences & physical geography

Author, co-author :

Jagdhuber, T.; German Aerospace Center, Microwaves and Radar Institute, Wessling, 82234, Germany

Montzka, C.; Research Center Jülich, Institute of Bio-and Geoscience, Jülich, 52428, Germany

Lopez-Martinez, C.; Department of Signal Theory and Communications, Universitat Polytécnica de Catalunya, Barcelona, 08034, Spain

Baur, M. J.; German Aerospace Center, Microwaves and Radar Institute, Wessling, 82234, Germany

Link, M.; German Aerospace Center, Microwaves and Radar Institute, Wessling, 82234, Germany

Piles, M.; Image Processing Lab, University of Valencia, Valencia, 46980, Spain

Das, N. N.; Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, United States

Jonard, François ; Université de Liège - ULiège > Département de géographie > Systèmes d'information géographiques

Language :

English

Title :

Estimation of Vegetation Structure Parameters from SMAP Radar Intensity Observations

Publication date :

2021

Journal title :

IEEE Transactions on Geoscience and Remote Sensing

ISSN :

0196-2892

eISSN :

1558-0644

Publisher :

Institute of Electrical and Electronics Engineers Inc.

Volume :

Issue :

Pages :

151-167

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 21 September 2021

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