Effective roughness modelling as a tool for soil moisture retrieval from C-and L-band SAR

被引:68
作者
Lievens, H. [1 ]
Verhoest, N. E. C. [1 ]
De Keyser, E. [2 ]
Vernieuwe, H. [2 ]
Matgen, P. [3 ]
Alvarez-Mozos, J. [4 ]
De Baets, B. [2 ]
机构
[1] Univ Ghent, Lab Hydrol & Water Management, B-9000 Ghent, Belgium
[2] Univ Ghent, Dept Appl Math Biometr & Proc Control, B-9000 Ghent, Belgium
[3] Publ Res Ctr Gabriel Lippmann, Dept Environm & Agrobiotechnol, L-4422 Belvaux, Luxembourg
[4] Univ Publ Navarra, Dept Projects & Rural Engn, Pamplona 31006, Spain
关键词
SURFACE SCATTERING MODELS; INTEGRAL-EQUATION MODEL; CORRELATION LENGTH; RADAR; BACKSCATTERING; PARAMETERIZATION; CALIBRATION; APPLICABILITY; VARIABILITY; DERIVATION;
D O I
10.5194/hess-15-151-2011
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Soil moisture retrieval from Synthetic Aperture Radar (SAR) using state-of-the-art backscatter models is not fully operational at present, mainly due to difficulties involved in the parameterisation of soil surface roughness. Recently, increasing interest has been drawn to the use of calibrated or effective roughness parameters, as they circumvent issues known to the parameterisation of field-measured roughness. This paper analyses effective roughness parameters derived from C- and L-band SAR observations over a large number of agricultural seedbed sites in Europe. It shows that parameters may largely differ between SAR acquisitions, as they are related to the observed backscatter coefficients and variations in the local incidence angle. Therefore, a statistical model is developed that allows for estimating effective roughness parameters from microwave backscatter observations. Subsequently, these parameters can be propagated through the Integral Equation Model (IEM) for soil moisture retrieval. It is shown that fairly accurate soil moisture results are obtained both at C-and L-band, with an RMSE ranging between 4 vol% and 6.5 vol%.
引用
收藏
页码:151 / 162
页数:12
相关论文
共 58 条
[1]   Variability in ERS scatterometer measurements over land [J].
Abdel-Messeh, M ;
Quegan, S .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2000, 38 (04) :1767-1776
[2]   Effective versus measured correlation length for radar-based surface soil moisture retrieval [J].
Alvarez-Mozos, J. ;
Gonzalez-Audicana, M. ;
Casali, J. ;
Larranaga, A. .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2008, 29 (17-18) :5397-5408
[3]   Assessment of the operational applicability of RADARSAT-1 data for surface soil moisture estimation [J].
Alvarez-Mozos, J ;
Casalí, J ;
González-Audícana, M ;
Verhoest, NEC .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2006, 44 (04) :913-924
[4]   Influence of Surface Roughness Spatial Variability and Temporal Dynamics on the Retrieval of Soil Moisture from SAR Observations [J].
Alvarez-Mozos, Jesus ;
Verhoest, Niko E. C. ;
Larranaga, Arantzazu ;
Casali, Javier ;
Gonzalez-Audicana, Maria .
SENSORS, 2009, 9 (01) :463-489
[5]   VEGETATION MODELED AS A WATER CLOUD [J].
ATTEMA, EPW ;
ULABY, FT .
RADIO SCIENCE, 1978, 13 (02) :357-364
[6]   Calibration of the Integral Equation Model for SAR data in C-band and HH and VV polarizations [J].
Baghdadi, N ;
Holah, N ;
Zribi, M .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2006, 27 (04) :805-816
[7]   An empirical calibration of the integral equation model based on SAR data, soil moisture and surface roughness measurement over bare soils [J].
Baghdadi, N ;
King, C ;
Chanzy, A ;
Wigneron, JP .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2002, 23 (20) :4325-4340
[8]   Semi-empirical calibration of the IEM backscattering model using radar images and moisture and roughness field measurements [J].
Baghdadi, N ;
Gherboudj, I ;
Zribi, M ;
Sahebi, M ;
King, C ;
Bonn, F .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2004, 25 (18) :3593-3623
[9]  
Beckman P., 1963, SCATTERING ELECTROMA
[10]   Retrieving near-surface soil moisture from Radarsat SAR data [J].
Biftu, GF ;
Gan, TY .
WATER RESOURCES RESEARCH, 1999, 35 (05) :1569-1579