Soil Moisture Estimation in South-Eastern New Mexico Using High Resolution Synthetic Aperture Radar (SAR) Data

The retrieval of soil surface parameters, in particular soil moisture and roughness, based on Synthetic Aperture Radar (SAR) data, has been the subject of a large number of studies, of which results are available in the scientific literature. However, although refined methods based on theoretical/analytical scattering models have been proposed and successfully applied in experimental studies, at the operative level very simple, empirical models with a number of adjustable parameters are usually employed. One of the reasons for this situation is that retrieval methods based on analytical scattering models are not easy to implement and to be employed by non-expert users. Related to this, commercially and freely available software tools for the processing of SAR data, although including routines for basic manipulation of polarimetric SAR data (e.g., coherency and covariance matrix calculation, Pauli decomposition, etc.), do not implement easy-to-use methods for surface parameter retrieval. In order to try to fill this gap, in this paper we present a user-friendly computer program for the retrieval of soil surface parameters from Polarimetric Synthetic Aperture Radar (PolSAR) imageries. The program evaluates soil permittivity, soil moisture and soil roughness based on the theoretical predictions of the electromagnetic scattering provided by the Polarimetric Two-Scale Model (PTSM) and the Polarimetric Two-Scale Two-Component Model (PTSTCM). In particular, nine different retrieval methodologies, whose applicability depends on both the used polarimetric data (dual- or full-pol) and the characteristics of the observed scene (e.g., on its topography and on its vegetation cover), as well as their implementation in the Interactive Data Language (IDL) platform, are discussed. One specific example from Germany’s Demmin test-site is presented in detail, in order to provide a first guide to the use of the tool. Obtained retrieval results are in agreement with what was expected according to the available literature.

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C- and Multiangle Ku-Band Synthetic Aperture Radar Data for Bare Soil Moisture Estimation in Agricultural Areas

Remote Sensing of Environment ◽

10.1016/s0034-4257(97)00170-3 ◽

1998 ◽

Vol 64 (1) ◽

pp. 77-90 ◽

Cited By ~ 18

Author(s):

E.E. Sano ◽

M.S. Moran ◽

A.R. Huete ◽

T. Miura

Keyword(s):

Soil Moisture ◽

Synthetic Aperture Radar ◽

Bare Soil ◽

Radar Data ◽

Synthetic Aperture ◽

Synthetic Aperture Radar Data ◽

Agricultural Areas ◽

Moisture Estimation ◽

Ku Band ◽

Aperture Radar

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A Ground Moving Target Detection Approach Based on Shadow Feature With Multichannel High-Resolution Synthetic Aperture Radar

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2016.2597159 ◽

2016 ◽

Vol 13 (10) ◽

pp. 1572-1576 ◽

Cited By ~ 10

Author(s):

Huajian Xu ◽

Zhiwei Yang ◽

Guozhong Chen ◽

Guisheng Liao ◽

Min Tian

Keyword(s):

High Resolution ◽

Synthetic Aperture Radar ◽

Target Detection ◽

Synthetic Aperture ◽

Moving Target ◽

Moving Target Detection ◽

Detection Approach ◽

Ground Moving Target Detection ◽

Aperture Radar

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Estimation of Synthetic Aperture Radar (SAR) soil moisture with the use of fractal roughness

10.5194/egusphere-egu21-3203 ◽

2021 ◽

Author(s):

Ju Hyoung Lee ◽

Notarnicola Claudia ◽

Jeff Walker

Keyword(s):

Spatial Distribution ◽

Soil Moisture ◽

Synthetic Aperture Radar ◽

Synthetic Aperture ◽

Accurate Estimation ◽

Distribution Data ◽

Fractal Surface ◽

Fractal Method ◽

Local Point ◽

Aperture Radar

<p>To estimate surface soil moisture from Sentinel-1 backscattering, accurate estimation of soil roughness is a key. However, it is usually error source, due to complexity of surface heterogeneity. This study investigates the fractal methods that takes multi-scale roughness into account. Fractal models are widely recognized as one of the best approaches to depict soil roughness of natural system. Unlike the conventional approach of fractal method that uses local roughness measured in the field or Digital Elevation Model information seldom considering a stochastic characteristic of soil surface, fractal surface is generated with the roughness spatially inverted from Synthetic Aperture Radar (SAR) backscatter. Assuming that the land surface in study site is on small to intermediate scales, pseudo-roughness is spatially estimated by modelling SAR roughness with the one-sided power-law spectrum. In addition, it is assumed that both multiple and single scales of roughness affect SAR backscatter in an integrative way. For validation, soil moisture is retrieved with this time-varying roughness. Based upon local validation and cost minimization, as compared with an inversion approach of surface scattering models (Integral Equation Model), a fractal method seems geometrically more sensible than an inversion, based upon a spatial distribution and a priori knowledge in the field. Although inverted roughness is used as an input, fractal model does not reproduce the same roughness. Results will show local point validation, fractal surface, and estimation of coefficients, and various spatial distribution data. This study will be useful for future satellite missions such as NASA-ISRO SAR mission.</p>

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A Novel Orthogonal Waveform Separation Scheme for Airborne MIMO-SAR Systems

Sensors ◽

10.3390/s18103580 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3580 ◽

Cited By ~ 1

Author(s):

Jie Wang ◽

Ke-Hong Zhu ◽

Li-Na Wang ◽

Xing-Dong Liang ◽

Long-Yong Chen

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Synthetic Aperture Radar ◽

3D Imaging ◽

Synthetic Aperture ◽

Separation Scheme ◽

Sar Images ◽

Wide Swath ◽

Aperture Radar

In recent years, multi-input multi-output (MIMO) synthetic aperture radar (SAR) systems, which can promote the performance of 3D imaging, high-resolution wide-swath remote sensing, and multi-baseline interferometry, have received considerable attention. Several papers on MIMO-SAR have been published, but the research of such systems is seriously limited. This is mainly because the superposed echoes of the multiple transmitted orthogonal waveforms cannot be separated perfectly. The imperfect separation will introduce ambiguous energy and degrade SAR images dramatically. In this paper, a novel orthogonal waveform separation scheme based on echo-compression is proposed for airborne MIMO-SAR systems. Specifically, apart from the simultaneous transmissions, the transmitters are required to radiate several times alone in a synthetic aperture to sense their private inner-aperture channels. Since the channel responses at the neighboring azimuth positions are relevant, the energy of the solely radiated orthogonal waveforms in the superposed echoes will be concentrated. To this end, the echoes of the multiple transmitted orthogonal waveforms can be separated by cancelling the peaks. In addition, the cleaned echoes, along with original superposed one, can be used to reconstruct the unambiguous echoes. The proposed scheme is validated by simulations.

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