Evaluation of the SMOS L-MEB passive microwave soil moisture retrieval algorithm

被引:97
|
作者
Panciera, Rocco [1 ]
Walker, Jeffrey P. [1 ]
Kalma, Jetse D. [2 ]
Kim, Edward J. [3 ]
Saleh, Kauzar [4 ]
Wigneron, Jean-Pierre [5 ]
机构
[1] Univ Melbourne, Dept Civil & Environm Engn, Parkville, Vic 3010, Australia
[2] Univ Newcastle, Sch Engn, Callaghan, NSW 2308, Australia
[3] NASA, Goddard Space Flight Ctr, Washington, DC USA
[4] Univ Cambridge, Dept Geog, Cambridge CB2 1TN, England
[5] EPHYSE, INRA, Bordeaux, France
基金
澳大利亚研究理事会;
关键词
Soil moisture; Microwave radiometry; SMOS; NAFE; L-BAND; CROP FIELDS; VEGETATION; EMISSION; RADIOMETRY; FREQUENCY; MODEL; GRASS; POLARIZATION; BEHAVIOR;
D O I
10.1016/j.rse.2008.10.010
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Soil moisture will be mapped globally by the European Soil Moisture and Ocean Salinity (SMOS) mission to be launched in 2009. The expected soil moisture accuracy will be 4.0 %v/v. The core component of the SMOS soil moisture retrieval algorithm is the L-band Microwave Emission of the Biosphere (L-MEB) model which simulates the microwave emission at L-band from the soil-vegetation layer. The model parameters have been calibrated with data acquired by tower mounted radiometer studies in Europe and the United States, with a typical footprint size of approximately 10 m. In this study. aircraft L-band data acquired during the National Airborne Field Experiment (NAFE) intensive campaign held in South-eastern Australia in 2005 are used to perform the first evaluation of the L-MEB model and its proposed parameterization when applied to coarser footprints (62.5 m), The model could be evaluated across large areas including a wide range of land surface conditions, typical of the Australian environment. Soil moisture was retrieved from the aircraft brightness temperatures using L-MEB and ground measured ancillary data (soil temperature, soil texture, vegetation water content and surface roughness) and subsequently evaluated against ground measurements of soil moisture. The retrieval accuracy when using the L-MEB 'default' set of model parameters was found to be better than 4.0 %v/v only over grassland covered sites. Over crops the model was found to underestimate soil moisture by up to 32 %v/v. After site specific calibration of the vegetation and roughness parameters, the retrieval accuracy was found to be equal or better than 4.8 %v/v for crops and grasslands at 62.5-m resolution. it is suggested that the proposed value of roughness parameter H(R) for crops is too low, and that variability of HR With Soil moisture must be taken into consideration to obtain accurate retrievals at these scales. The analysis presented here is a crucial step towards validating the application of L-MEB for soil moisture retrieval from satellite observations in an operational context. (C) 2008 Elsevier Inc. All rights reserved.
引用
收藏
页码:435 / 444
页数:10
相关论文
共 50 条
  • [1] Calibration of L-MEB for soil moisture retrieval over forests
    Grant, J. P.
    Wigneron, J. -P.
    de Griend, A. A. Van
    Guglielmetti, M.
    Saleh, K.
    Schwank, M.
    [J]. IGARSS: 2007 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-12: SENSING AND UNDERSTANDING OUR PLANET, 2007, : 2248 - +
  • [2] Estimating Effective Roughness Parameters of the L-MEB Model for Soil Moisture Retrieval Using Passive Microwave Observations From SMAPVEX12
    Martens, Brecht
    Lievens, Hans
    Colliander, Andreas
    Jackson, Thomas J.
    Verhoest, Niko E. C.
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2015, 53 (07): : 4091 - 4103
  • [3] The SMOS Soil Moisture Retrieval Algorithm
    Kerr, Yann H.
    Waldteufel, Philippe
    Richaume, Philippe
    Wigneron, Jean Pierre
    Ferrazzoli, Paolo
    Mahmoodi, Ali
    Al Bitar, Ahmad
    Cabot, Francois
    Gruhier, Claire
    Juglea, Silvia Enache
    Leroux, Delphine
    Mialon, Arnaud
    Delwart, Steven
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2012, 50 (05): : 1384 - 1403
  • [4] Analysis and Reduction of the Uncertainties in Soil Moisture Estimation With the L-MEB Model Using EFAST and Ensemble Retrieval
    Li, Dazhi
    Jin, Rui
    Zhou, Jian
    Kang, Jian
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2015, 12 (06) : 1337 - 1341
  • [5] Global Sensitivity Analysis of the L-MEB Model for Retrieving Soil Moisture
    Wang, Zengyan
    Che, Tao
    Liou, Yuei-An
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2016, 54 (05): : 2949 - 2962
  • [6] Effect of Structural Uncertainty in Passive Microwave Soil Moisture Retrieval Algorithm
    Karthikeyan, Lanka
    Pan, Ming
    Kumar, Dasika Nagesh
    Wood, Eric F.
    [J]. SENSORS, 2020, 20 (04)
  • [7] FIRST GLANCE ON A REVISED SMOS SOIL MOISTURE RETRIEVAL ALGORITHM: EVALUATION WITH RESPECT TO ECMWF SOIL MOISTURE SIMULATIONS
    Al-Yaari, A.
    Fernandez-Moran, R.
    Wigneron, J. -P.
    Mialon, A.
    Mahmoodi, A.
    Al Bitar, A.
    Kerr, Y.
    [J]. 2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2017, : 1566 - 1569
  • [8] Evaluating an Improved Parameterization of the Soil Emission in L-MEB
    Wigneron, Jean-Pierre
    Chanzy, Andre
    Kerr, Yann H.
    Lawrence, Heather
    Shi, Jiancheng
    Escorihuela, Maria Jose
    Mironov, Valery
    Mialon, Arnaud
    Demontoux, Francois
    de Rosnay, Patricia
    Saleh-Contell, Kauzar
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2011, 49 (04): : 1177 - 1189
  • [9] Soil moisture retrieval for the SMOS mission
    Petitcolin, F
    Vergely, JL
    Waldteufel, P
    Cot, C
    [J]. IGARSS 2003: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS I - VII, PROCEEDINGS: LEARNING FROM EARTH'S SHAPES AND SIZES, 2003, : 911 - 913
  • [10] Soil moisture retrieval and applications from L band Radiometry (SMOS)
    Kerr, YH
    Wigneron, JP
    Waldteufel, P
    [J]. IGARSS 2001: SCANNING THE PRESENT AND RESOLVING THE FUTURE, VOLS 1-7, PROCEEDINGS, 2001, : 3 - 5