Operational performance of current synthetic aperture radar sensors in mapping soil surface characteristics in agricultural environments: application to hydrological and erosion modelling

2007 ◽  
Vol 22 (1) ◽  
pp. 9-20 ◽  
Author(s):  
Nicolas Baghdadi ◽  
Olivier Cerdan ◽  
Mehrez Zribi ◽  
Véronique Auzet ◽  
Frédéric Darboux ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Soil Surface ◽  
Surface Characteristics ◽  
Synthetic Aperture ◽  
Operational Performance ◽  
Radar Sensors ◽  
Erosion Modelling ◽  
Aperture Radar

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.

Long‐term subsidence in Mexico City from 2004 to 2018 revealed by five synthetic aperture radar sensors

2019 ◽  
Vol 30 (15) ◽  
pp. 1785-1801 ◽  
Author(s):  
Zheyuan Du ◽  
Linlin Ge ◽  
Alex Hay‐Man Ng ◽  
Qinggaozi Zhu ◽  
Qi Zhang ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Mexico City ◽  
Synthetic Aperture ◽  
Radar Sensors ◽  
Aperture Radar

Flood Mapping Using Synthetic Aperture Radar Sensors From Local to Global Scales

2018 ◽  
pp. 55-77 ◽  
Author(s):  
Antara Dasgupta ◽  
Stefania Grimaldi ◽  
RAAJ Ramsankaran ◽  
Valentijn R. N. Pauwels ◽  
Jeffrey P. Walker ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Flood Mapping ◽  
Radar Sensors ◽  
Aperture Radar

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.

scholarly journals Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review

2020 ◽  
Vol 12 (16) ◽  
pp. 2546 ◽  
Author(s):  
Sung Wook Paek ◽  
Sivagaminathan Balasubramanian ◽  
Sangtae Kim ◽  
Olivier de Weck
Keyword(s):  
Synthetic Aperture Radar ◽  
Systems Engineering ◽  
Weather Conditions ◽  
Synthetic Aperture ◽  
Satellite Constellations ◽  
Small Satellites ◽  
Radar Sensors ◽  
Research Areas ◽  
Technological Advances ◽  
Aperture Radar

Space-based radar sensors have transformed Earth observation since their first use by Seasat in 1978. Radar instruments are less affected by daylight or weather conditions than optical counterparts, suitable for continually monitoring the global biosphere. The current trends in synthetic aperture radar (SAR) platform design are distinct from traditional approaches in that miniaturized satellites carrying SAR are launched in multiples to form a SAR constellation. A systems engineering perspective is presented in this paper to track the transitioning of space-based SAR platforms from large satellites to small satellites. Technological advances therein are analyzed in terms of subsystem components, standalone satellites, and satellite constellations. The availability of commercial satellite constellations, ground stations, and launch services together enable real-time SAR observations with unprecedented details, which will help reveal the global biomass and their changes owing to anthropogenic drivers. The possible roles of small satellites in global biospheric monitoring and the subsequent research areas are also discussed.

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