A comparison of multi-polarization and multi-angular approaches for estimating bare soil surface roughness from spaceborne radar data

被引:29
|
作者
Sahebi, MR [1 ]
Angles, J [1 ]
Bonn, F [1 ]
机构
[1] Univ Sherbrooke, CARTEL, Sherbrooke, PQ J1K 2R1, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
D O I
10.5589/m02-060
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Soil surface roughness and moisture content both have a significant effect on microwave backscatter to the satellite. The purpose of this work is to evaluate the optimum sensor configuration for existing radar satellites to quantify soil surface roughness. A simulation study using theoretical and empirical models permits the estimation of the sensitivity of the backscatter coefficient to relative variations in soil parameters in terms of radar characteristics. Two different configurations for estimating surface roughness were tested, multi-polarization (co-polarizations) and multi-angular, and the results of the multi-angular configuration provided the best results. A normalized radar backscatter soil roughness index (NBRI) is presented for estimating soil roughness from a multi-angular approach using sensors such as RADARSAT-1. This index was tested using the geometric optics model (GOM) and RADARSAT data. Coefficients of determination of 99% and 83%, respectively, were obtained for each simulation.
引用
收藏
页码:641 / 652
页数:12
相关论文
共 50 条
  • [31] POLARIMETRIC MULTI-ANGULAR RADARSAT-2 DATA SENSITIVITY TO SURFACE PARAMETERS
    Wang, Hongquan
    Allain, Sophie
    Meric, Stephane
    Pottier, Eric
    2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2012, : 5125 - 5128
  • [32] Retrieving leaf area index from multi-angular airborne data
    Garcia-Haro, Francisco Javier
    Camacho-de Coca, Fernando
    Melia, Joaquin
    ANNALS OF GEOPHYSICS, 2006, 49 (01) : 209 - 218
  • [33] Ocean surface slick characterization by multi-polarization Radarsat-2 data
    Skrunes, Stine
    Brekke, Camilla
    Eltoft, Torbjorn
    SAR IMAGE ANALYSIS, MODELING, AND TECHNIQUES XII, 2012, 8536
  • [34] Analysis of multi-frequency polarimetric data for assessment of bare soil roughness
    Ben Khadhra, K
    Singh, D
    Boerner, T
    Hounam, D
    Wiesbeck, W
    IGARSS 2003: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS I - VII, PROCEEDINGS: LEARNING FROM EARTH'S SHAPES AND SIZES, 2003, : 1405 - 1407
  • [35] A validation of multi-scale surfaces roughness description for the modelling of radar backscattering from bare soil surfaces
    Davidson, M
    Le Toan, T
    Mattia, F
    Manninen, T
    Borderies, P
    Chenerie, I
    Borgeaud, M
    SECOND INTERNATIONAL WORKSHOP ON RETRIEVAL OF BIO- & GEO-PHYSICAL PARAMETERS FROM SAR DATA FOR LAND APPLICATIONS, 1998, 441 : 395 - 400
  • [36] Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data
    Gherboudj, Imen
    Magagi, Ramata
    Berg, Aaron A.
    Toth, Brenda
    REMOTE SENSING OF ENVIRONMENT, 2011, 115 (01) : 33 - 43
  • [37] Roughness determination from multi-angular ASAR Wide Swath mode observations for soil moisture retrieval over the Tibetan Plateau
    van der Velde, Rogier
    Su, Zhongbo
    Wen, Jun
    10TH EUROPEAN CONFERENCE ON SYNTHETIC APERTURE RADAR (EUSAR 2014), 2014,
  • [38] Monitoring Bare Agricultural Soil: Comparison between Ground Based SAR PoSAR System Measurements and Multi-angular RADARSAT-2 Datasets
    Wang, Hongquan
    Meric, Stephane
    Allain, Sophie
    Pottier, Eric
    10TH EUROPEAN CONFERENCE ON SYNTHETIC APERTURE RADAR (EUSAR 2014), 2014,
  • [39] On the retrival of vegetation parameters from multi-angular hyperspectral remote sensing data
    Hu, Baoxin
    Zhang, Frank
    Wang, Jianguo
    IEEE TIC-STH 09: 2009 IEEE TORONTO INTERNATIONAL CONFERENCE: SCIENCE AND TECHNOLOGY FOR HUMANITY, 2009, : 451 - 455
  • [40] POLARIMETRIC DECOMPOSITION OF MULTI-ANGULAR SAR DATA FOR SOIL MOISTURE RETRIEVAL OVER AGRICULTURAL FIELDS
    Wang, Hongquan
    Magagi, Ramata
    Goita, Kalifa
    2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2017, : 4947 - 4950