Estimating Effective Roughness Parameters of the L-MEB Model for Soil Moisture Retrieval Using Passive Microwave Observations From SMAPVEX12

被引:17
|
作者
Martens, Brecht [1 ]
Lievens, Hans [1 ]
Colliander, Andreas [2 ]
Jackson, Thomas J. [3 ]
Verhoest, Niko E. C. [1 ]
机构
[1] Univ Ghent, Lab Hydrol & Water Management, B-9000 Ghent, Belgium
[2] CALTECH, Jet Prop Lab, Pasadena, CA 91109 USA
[3] ARS, Hydrol & Remote Sensing Lab, USDA, Beltsville, MD 20705 USA
来源
基金
美国国家航空航天局;
关键词
Passive Active L-band Sensor (PALS); passive microwave remote sensing; roughness modeling; soil moisture; L-BAND; DISCHARGE PREDICTIONS; OCEAN SALINITY; SIMPLEX-METHOD; CALIBRATION; EMISSION; FIELD; IMPROVEMENT; VALIDATION; TEXTURE;
D O I
10.1109/TGRS.2015.2390259
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Despite the continuing efforts to improve existing soil moisture retrieval algorithms, the ability to estimate soil moisture from passive microwave observations is still hampered by problems in accurately modeling the observed microwave signal. This paper focuses on the estimation of effective surface roughness parameters of the L-band Microwave Emission from the Biosphere (L-MEB) model in order to improve soil moisture retrievals from passive microwave observations. Data from the SMAP Validation Experiment 2012 conducted in Canada are used to develop and validate a simple model for the estimation of effective roughness parameters. Results show that the L-MEB roughness parameters can be empirically related to the observed brightness temperatures and the leaf area index of the vegetation. These results indicate that the roughness parameters are compensating for both roughness and vegetation effects. It is also shown, using a leave-one-out cross validation, that the model is able to accurately estimate the roughness parameters necessary for the inversion of the L-MEB model. In order to demonstrate the usefulness of the roughness parameterization, the performance of the model is compared to more traditional roughness formulations. Results indicate that the soil moisture retrieval error can be reduced to 0.054 m(3)/m(3) if the roughness formulation proposed in this study is implemented in the soil moisture retrieval algorithm.
引用
收藏
页码:4091 / 4103
页数:13
相关论文
共 50 条
  • [21] Soil Moisture Retrieval Using Multistatic L-Band SAR and Effective Roughness Modeling
    Tronquo, Emma
    Lievens, Hans
    Bouchat, Jean
    Defourny, Pierre
    Baghdadi, Nicolas
    Verhoest, Niko E. C.
    [J]. REMOTE SENSING, 2022, 14 (07)
  • [22] Soil Moisture Retrievals From Biangular L-Band Passive Microwave Observations
    Wigneron, Jean-Pierre
    Calvet, Jean-Christophe
    De Rosnay, Patricia
    Kerr, Yann
    Waldteufel, Philippe
    Saleh, Kauzar
    Escorihuela, Maria Jose
    Kruszewski, Alain
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2004, 1 (04) : 277 - 281
  • [23] Soil moisture retrieval from airborne L-band passive microwave using high resolution multispectral data
    Hasan, Sayeh
    Montzka, Carsten
    Ruediger, Christoph
    Al, Muhammad
    Bogena, Heye R.
    Vereecken, Harry
    [J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2014, 91 : 59 - 71
  • [24] Evaluation of the Tau-Omega Model for Passive Microwave Soil Moisture Retrieval Using SMAPEx Datasets
    Gao, Ying
    Walker, Jeffrey P.
    Ye, Nan
    Panciera, Rocco
    Monerris, Alessandra
    Ryu, Dongryeol
    Rudiger, Christoph
    Jackson, Thomas J.
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2018, 11 (03) : 888 - 895
  • [25] Parameterized Exponentially Correlated Surface Emission Model for L-band Passive Microwave Soil Moisture Retrieval
    Zhao, Tianjie
    Shi, Jiancheng
    Mialon, Arnaud
    Kerr, Yann
    [J]. 2014 XXXITH URSI GENERAL ASSEMBLY AND SCIENTIFIC SYMPOSIUM (URSI GASS), 2014,
  • [26] Parametric exponentially correlated surface emission model for L-band passive microwave soil moisture retrieval
    Zhao Tianjie
    Shi Jiancheng
    Rajat, Bindlish
    Thomas, Jackson
    Michael, Cosh
    Jiang Lingmei
    Zhang Zhongjun
    Lan Huimin
    [J]. PHYSICS AND CHEMISTRY OF THE EARTH, 2015, 83-84 : 65 - 74
  • [27] On the Retrieval of Soil Moisture in Wheat Fields From L-Band SAR Based on Water Cloud Modeling, the IEM, and Effective Roughness Parameters
    Lievens, Hans
    Verhoest, Niko E. C.
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2011, 8 (04) : 740 - 744
  • [28] Effective roughness modelling as a tool for soil moisture retrieval from C-and L-band SAR
    Lievens, H.
    Verhoest, N. E. C.
    De Keyser, E.
    Vernieuwe, H.
    Matgen, P.
    Alvarez-Mozos, J.
    De Baets, B.
    [J]. HYDROLOGY AND EARTH SYSTEM SCIENCES, 2011, 15 (01) : 151 - 162
  • [29] Estimating Mixed-Pixel Component Soil Moisture Contents Using Biangular Observations From the HiWATER Airborne Passive Microwave Data
    Zhang, Tao
    Jiang, Lingmei
    Chai, Linna
    Zhao, Tianjie
    Wang, Qi
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2015, 12 (05) : 1146 - 1150
  • [30] Circumpolar Thin Arctic Sea Ice Thickness and Small-Scale Roughness Retrieval Using Soil Moisture and Ocean Salinity and Soil Moisture Active Passive Observations
    Jo, Suna
    Kim, Hyun-Cheol
    Kwon, Young-Joo
    Hong, Sungwook
    [J]. REMOTE SENSING, 2019, 11 (23)