Neural networks for the inversion of soil surface parameters from synthetic aperture radar satellite data

2004 ◽  
Vol 31 (1) ◽  
pp. 95-108 ◽  
Author(s):  
Mahmod Reza Sahebi ◽  
Ferdinand Bonn ◽  
Goze B Bénié
Keyword(s):  
Neural Networks ◽  
Soil Moisture ◽  
Kalman Filter ◽  
Synthetic Aperture Radar ◽  
Satellite Data ◽  
Input Data ◽  
Soil Surface ◽  
Synthetic Aperture ◽  
Surface Parameters ◽  
Aperture Radar

This paper presents an application of neural networks to the extraction of bare soil surface parameters such as roughness and soil moisture content using synthetic aperture radar (SAR) satellite data. It uses a fast learning algorithm for training a multilayer feedforward neural network using the Kalman filter technique. Two different databases (theoretical and empirical) were used for the learning stage. Each database was configured as single and multiangular sets of input data (data acquired at two different incidence angles) that are compatible with data from one and two satellite images, respectively. All the configurations are trained and then evaluated using RADARSAT-1 and simulated data. The empirical (measured) database with the multiangular set of input data configuration had the best accuracy with a mean error of 1.54 cm for root mean square (rms) height of the surface roughness and 2.45 for soil dielectric constant in the study area. Based on these results the proposed approach was applied on RADARSAT-1 images from the Chateauguay watershed area (Quebec, Canada) and the final results are presented in the form of roughness and humidity maps.Key words: neural networks, Kalman filter, RADARSAT, SAR, soil roughness, soil moisture.

scholarly journals Software Tool for Soil Surface Parameters Retrieval from Fully Polarimetric Remotely Sensed SAR Data

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5085
Author(s):  
Davod Poreh ◽  
Antonio Iodice ◽  
Antonio Natale ◽  
Daniele Riccio
Keyword(s):  
Soil Moisture ◽  
Synthetic Aperture Radar ◽  
Electromagnetic Scattering ◽  
Experimental Studies ◽  
Soil Surface ◽  
Synthetic Aperture ◽  
Scale Model ◽  
Surface Parameters ◽  
Sar Data ◽  
Aperture Radar

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.

Retrieving surface roughness and soil moisture from synthetic aperture radar (SAR) data using neural networks

2002 ◽  
Vol 28 (5) ◽  
pp. 701-711 ◽  
Author(s):  
N. Baghdadi ◽  
S. Gaultier ◽  
C. King
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Soil Moisture ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Sar Data ◽  
Aperture Radar

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

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>

scholarly journals Integration of synthetic aperture radar and optical satellite data for corn biomass estimation

MethodsX ◽  
2020 ◽  
Vol 7 ◽  
pp. 100857
Author(s):  
Mehdi Hosseini ◽  
Heather McNairn ◽  
Scott Mitchell ◽  
Laura Dingle Robertson ◽  
Andrew Davidson ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Satellite Data ◽  
Synthetic Aperture ◽  
Biomass Estimation ◽  
Optical Satellite Data ◽  
Aperture Radar
Sign in / Sign up

Export Citation Format

Share Document