Assessing the SMOS Soil Moisture Retrieval Parameters With High-Resolution NAFE'06 Data

被引:21
|
作者
Merlin, Olivier [1 ]
Walker, Jeffrey Phillip [2 ]
Panciera, Rocco [2 ]
Jose Escorihuela, Maria [3 ]
Jackson, Thomas J. [4 ]
机构
[1] Ctr Etud Spatiales Biosphere, F-31401 Toulouse, France
[2] Univ Melbourne, Dept Civil & Environm Engn, Melbourne, Vic 3010, Australia
[3] IsardSAT, Barcelona 08031, Spain
[4] USDA, Annapolis, MD 21409 USA
基金
澳大利亚研究理事会;
关键词
Airborne experiment; calibration; L-band radiometry; National Airborne Field Experiment (NAFE); retrieval algorithm; soil moisture; Soil Moisture and Ocean Salinity (SMOS); MICROWAVE EMISSION; MODEL;
D O I
10.1109/LGRS.2009.2012727
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
The spatial and temporal invariance of Soil Moisture and Ocean Salinity (SMOS) forward model parameters for soil moisture retrieval was assessed at 1-km resolution on a diurnal basis with data from the National Airborne Field Experiment 2006. The approach used was to apply the SMOS default parameters uniformly over 27 1-km validation pixels, retrieve soil moisture from the airborne observations, and then to interpret the differences between airborne and ground estimates in terms of land use, parameter variability, and sensing depth. For pastures ( 17 pixels) and nonirrigated crops ( 5 pixels), the root mean square error (rmse) was 0.03 volumetric (vol./vol.) soil moisture with a bias of 0.004 vol./vol. For pixels dominated by irrigated crops ( 5 pixels), the rmse was 0.10 vol./vol., and the bias was -0.09 vol./vol. The correlation coefficient between bias in irrigated areas and the 1-km field soil moisture variability was found to be 0.73, which suggests either 1) an increase of the soil dielectric roughness ( up to about one) associated with small-scale heterogeneity of soil moisture or/and 2) a difference in sensing depth between an L-band radiometer and the in situ measurements, combined with a strong vertical gradient of soil moisture in the top 6 cm of the soil.
引用
收藏
页码:635 / 639
页数:5
相关论文
共 50 条
  • [21] Combining SMOS with visible and near/shortwave/thermal infrared satellite data for high resolution soil moisture estimates
    Sanchez-Ruiz, Sergio
    Piles, Maria
    Sanchez, Nilda
    Martinez-Fernandez, Jose
    Vall-Ilossera, Merce
    Camps, Adriano
    [J]. JOURNAL OF HYDROLOGY, 2014, 516 : 273 - 283
  • [22] Toward a Surface Soil Moisture Product at High Spatiotemporal Resolution: Temporally Interpolated, Spatially Disaggregated SMOS Data
    Malbeteau, Y.
    Merlin, O.
    Balsamo, G.
    Er-Raki, S.
    Khabba, S.
    Walker, J. P.
    Jarlan, L.
    [J]. JOURNAL OF HYDROMETEOROLOGY, 2018, 19 (01) : 183 - 200
  • [23] Using optical satellite based data to improve soil moisture retrieval from SMOS mission
    Cros, S.
    Chanzy, A.
    Pellarin, T.
    Calvet, J-C
    Wigneron, J-P
    [J]. 2006 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8, 2006, : 2021 - +
  • [24] AN APPROACH FOR SURFACE SOIL MOISTURE RETRIEVAL USING MICROWAVE VEGETATION INDICES BASED ON SMOS DATA
    Cui, Qian
    Shi, Jiancheng
    Zhao, Tianjie
    Liu, Qang
    [J]. 2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2013, : 2692 - 2695
  • [25] Using high-resolution soil moisture data to assess soil water dynamics in the vadose zone
    Starr, JL
    Timlin, DJ
    [J]. VADOSE ZONE JOURNAL, 2004, 3 (03) : 926 - 935
  • [26] Application of SMOS Soil Moisture and Brightness Temperature at High Resolution With a Bias Correction Operator
    Kornelsen, Kurt C.
    Davison, Bruce
    Coulibaly, Paulin
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2016, 9 (04) : 1590 - 1605
  • [27] SMOS BASED HIGH RESOLUTION SOIL MOISTURE ESTIMATES FOR DESERT LOCUST PREVENTIVE MANAGEMENT
    Jose Escorihuela, Maria
    Merlin, Olivier
    Stefan, Vivien
    Indrio, Gianfranco
    Piou, Cyril
    [J]. IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2018, : 8275 - 8278
  • [28] Determining hot moments/spots of hillslope soil moisture variations based on high-resolution spatiotemporal soil moisture data
    Lv, Ligang
    Liao, Kaihua
    Zhou, Zhiwen
    Zhu, Qing
    Shen, Chunzhu
    [J]. CATENA, 2019, 173 : 150 - 161
  • [29] A Novel Bias Correction Method for Soil Moisture and Ocean Salinity (SMOS) Soil Moisture: Retrieval Ensembles
    Lee, Ju Hyoung
    Im, Jungho
    [J]. REMOTE SENSING, 2015, 7 (12): : 16045 - 16061
  • [30] SOIL MOISTURE RETRIEVAL FROM SMOS OBSERVATIONS USING NEURAL NETWORKS
    Rodriguez-Fernandez, N.
    Richaume, P.
    Aires, F.
    Prigent, C.
    Kerr, Y.
    Kolassa, J.
    Jimenez, C.
    Cabot, F.
    Mahmoodi, A.
    [J]. 2014 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2014, : 2431 - 2434