Optimization approach to retrieve soil surface parameters from single-acquisition single-configuration SAR data

被引:10
作者
Ghorbanian, Arsalan [1 ,2 ]
Sahebi, Mahmod Reza [1 ,2 ]
Mohammadzadeh, Ali [1 ,2 ]
机构
[1] KN Toosi Univ Technol, Fac Geodesy & Geomat Engn, Photogrammetry & Remote Sensing Dept, Tehran, Iran
[2] KN Toosi Univ Technol, Remote Sensing Inst, Tehran, Iran
关键词
Soil moisture; Soil surface roughness; SAR; Optimization; Genetic algorithm; INTEGRAL-EQUATION MODEL; BARE SOIL; MOISTURE ESTIMATION; RADAR; INVERSION; BACKSCATTERING; POLARIZATION; CALIBRATION; SCATTERING; IMAGERY;
D O I
10.1016/j.crte.2018.11.005
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
This study suggests a novel approach to the retrieval of soil surface parameters using a single-acquisition single-configuration synthetic-aperture radar (SAR) system. Soil surface parameters such as soil moisture and surface roughness are key elements for many environmental studies, including Earth surface water cycles, energy exchange, agriculture, and geology. Remote sensing techniques, especially SAR data, are commonly used to retrieve such soil surface parameters over large areas. Several backscattering models have been proposed for soil surface parameters retrieval from SAR data. However, commonly, these backscattering models require multi configuration SAR data, including multi-polarization, multi-frequency, and multi-incidence angle. Here we propose a methodology that employs single-acquisition single-configuration SAR data for the retrieval of soil surface parameters. The originality is to use single-acquisition single-configuration SAR data to retrieve the soil surface parameters using an optimization approach by the genetic algorithm (GA); we have used the modified Dubois model (MDM) in HH polarization as the backscattering model. Three HH polarization and C band data sets from Quebec (Radarsat-1), Ontario (SIR-C), and Oklahoma (AIRSAR) were analyzed. The retrieved values of soil moisture and soil surface roughness were then compared to ground truth measurements with corresponding parameters. We employed diverse criteria, including the mean absolute error (MAE), the root mean square error (RMSE), the coefficient of performance (CP), and the correlation coefficient to investigate the performance of the proposed methodology. This analysis suggests the capability of the GA for the retrieval of soil surface parameters. Based on our findings, this method presents a viable alternative approach to the retrieval of soil surface parameters when only single-acquisition single-configuration SAR data is available. (C) 2019 Academie des sciences. Published by Elsevier Masson SAS. All rights reserved.
引用
收藏
页码:332 / 339
页数:8
相关论文
共 50 条
[1]   Global-scale comparison of passive (SMOS) and active (ASCAT) satellite based microwave soil moisture retrievals with soil moisture simulations (MERRA-Land) [J].
Al-Yaari, A. ;
Wigneron, J. -P. ;
Ducharne, A. ;
Kerr, Y. H. ;
Wagner, W. ;
De lannoy, G. ;
Reichle, R. ;
Al Bitar, A. ;
Dorigo, W. ;
Richaume, P. ;
Mialon, A. .
REMOTE SENSING OF ENVIRONMENT, 2014, 152 :614-626
[2]   Remote Sensing Image Classification Using Genetic-Programming-Based Time Series Similarity Functions [J].
Almeida, Alexandre E. ;
Torres, Ricardo da S. .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2017, 14 (09) :1499-1503
[3]  
Angles J., 2001, MEMOIRE DE MAITRISE
[4]   VEGETATION MODELED AS A WATER CLOUD [J].
ATTEMA, EPW ;
ULABY, FT .
RADIO SCIENCE, 1978, 13 (02) :357-364
[5]   Toward an Operational Bare Soil Moisture Mapping Using TerraSAR-X Data Acquired Over Agricultural Areas [J].
Aubert, Maelle ;
Baghdadi, Nicolas N. ;
Zribi, Mehrez ;
Ose, Kenji ;
El Hajj, Mahmoud ;
Vaudour, Emmanuelle ;
Gonzalez-Sosa, Enrique .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2013, 6 (02) :900-916
[6]  
Baghdadi N, 2002, INT GEOSCI REMOTE SE, P2646, DOI 10.1109/IGARSS.2002.1026729
[7]   Semi-Empirical Calibration of the Integral Equation Model for Co-Polarized L-Band Backscattering [J].
Baghdadi, Nicolas ;
Zribi, Mehrez ;
Paloscia, Simonetta ;
Verhoest, Niko E. C. ;
Lievens, Hans ;
Baup, Frederic ;
Mattia, Francesco .
REMOTE SENSING, 2015, 7 (10) :13626-13640
[8]   Estimation of soil moisture patterns in mountain grasslands by means of SAR RADARSAT2 images and hydrological modeling [J].
Bertoldi, Giacomo ;
Della Chiesa, Stefano ;
Notarnicola, Claudia ;
Pasolli, Luca ;
Niedrist, Georg ;
Tappeiner, Ulrike .
JOURNAL OF HYDROLOGY, 2014, 516 :245-257
[9]   Evaluation of polarimetric Radarsat-2 SAR data for development of soil moisture retrieval algorithms over a chronosequence of black spruce boreal forests [J].
Bourgeau-Chavez, Laura L. ;
Leblon, Brigitte ;
Charbonneau, Francois ;
Buckley, Joseph R. .
REMOTE SENSING OF ENVIRONMENT, 2013, 132 :71-85
[10]   Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters [J].
Bousbih, Safa ;
Zribi, Mehrez ;
Lili-Chabaane, Zohra ;
Baghdadi, Nicolas ;
El Hajj, Mohammad ;
Gao, Qi ;
Mougenot, Bernard .
SENSORS, 2017, 17 (11)