Roughness and vegetation parameterizations at L-band for soil moisture retrievals over a vineyard field

被引:36
|
作者
Fernandez-Moran, R. [1 ,2 ]
Wigneron, J-P [2 ]
Lopez-Baeza, E. [1 ]
Al-Yaari, A. [2 ]
Coll-Pajaron, A. [1 ]
Mialon, A. [3 ]
Miernecki, M. [4 ]
Parrens, M. [2 ,3 ]
Salgado-Hernanz, P. M. [1 ]
Schwank, M. [5 ,6 ]
Wang, S. [2 ,7 ]
Kerr, Y. H. [3 ]
机构
[1] Univ Valencia, Fac Phys, Dept Earth Phys & Thermodynam, Climatol Satellites Grp, E-46100 Valencia, Spain
[2] INRA, UMR ISPA 1391, Ctr INRA Bordeaux Aquitaine, F-33140 Villenave Dornon, France
[3] CESBIO, CNES CNRS IRD UPS, UMR 5126, Toulouse, France
[4] Univ Hamburg, Ctr Marine & Atmospher Sci ZMAW, Hamburg, Germany
[5] Swiss Fed Res Inst WSL, CH-8903 Birmensdorf, Switzerland
[6] Gamma Remote Sensing AG, CH-3073 Gumlingen, Switzerland
[7] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China
关键词
Microwave radiometry; L-band; Soil moisture; Soil roughness; Vegetation; L-MEB; SMOS; L-MEB MODEL; MICROWAVE EMISSION; SMOS; TEMPERATURE; TEXTURE;
D O I
10.1016/j.rse.2015.09.006
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The capability of L-band radiometry to monitor surface soil moisture (SM) at global scale has been analyzed in numerous studies, mostly in the framework of the ESA SMOS and NASA SMAP missions. To retrieve SM from L-band radiometric observations, two significant effects have to be accounted for, namely soil roughness and vegetation optical depth. In this study, soil roughness effects on retrieved SM values were evaluated using brightness temperatures acquired by the L-band ELBARA-II radiometer, over a vineyard field at the Valencia Anchor Station (VAS) site during the year 2013. Different combinations of the values of the model parameters used to account for soil roughness effects (H-R, Q(R), N-RH and N-RV) in the L-MEB model were evaluated. The L-MEB model (L-band Microwave Emission of the Biosphere) is the forward radiative transfer model used in the SMOS soil moisture retrieval algorithm. In this model, HR parameterizes the intensity of roughness effects, Q(R) accounts for polarization effects, and N-RH and N-RV parameterize the variations of the soil reflectivity as a function of the observation angle, theta, respectively for both H (Horizontal) and V (Vertical) polarizations. These evaluations were made by comparing in-situ measurements of SM (used here as a reference) against SM retrievals derived from tower-based ELBARA-II brightness temperatures mentioned above. The general retrieval approach consists of the inversion of L-MEB. Two specific configurations were tested: the classical 2-Parameter (2-P) retrieval configuration where SM and T-NAD (vegetation optical depth at nadir) are retrieved, and a 3-Parameter (3-P) configuration, accounting for the additional effects of the vineyard vegetation structure. Using the 2-P configuration, it was found that setting N-Rp (p = H or V) equals to 1 provided the best SM estimations in terms of correlation and unbiased Root Mean Square Error (ubRMSE). The assumption N-RV = N-RH = -1 simplifies the L-MEB retrieval, since the two parameters Two and H-R can then be grouped and retrieved as a single parameter (method here defined as the Simplified Retrieval Method (SRP)). The main advantage of the SRP method is that it is not necessary to calibrate H-R before performing the SM retrievals. Using the 3-P configuration, the results improved, with respect to SM retrievals, in terms of correlation and ubRMSE, as the structural characteristics of the vineyards were better accounted for. However, this method still requires the calibration of H-R, a disadvantage for operational applications. Finally, it was found that the use of in-situ roughness measurements to calibrate the roughness model parameters did not provide significant improvements in the SM retrievals as compared to the SRP method. (C) 2015 Elsevier Inc. All rights reserved.
引用
收藏
页码:269 / 279
页数:11
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