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  1. Article: Surface Soil Moisture Retrieval Using the L-Band Synthetic Aperture Radar Onboard the Soil Moisture Active-Passive Satellite and Evaluation at Core Validation Sites.

    Kim, Seung-Bum / van Zyl, Jakob J / Johnson, Joel T / Moghaddam, Matha / Tsang, Leung / Colliander, Andreas / Dunbar, Roy Scott / Jackson, Thomas J / Jaruwatanadilok, Sermsak / West, Richard / Berg, Aaron / Caldwell, Todd / Cosh, Michael H / Goodrich, David C / Livingston, Stanley / López-Baeza, Ernesto / Rowlandson, Tracy / Thibeault, Marc / Walker, Jeffrey P /
    Entekhabi, Dara / Njoku, Eni G / O'Neill, Peggy E / Yueh, Simon H

    IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society

    2019  Volume Volume 55, Issue Iss 4, Page(s) 1897–1914

    Abstract: This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active-Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid- ... ...

    Abstract This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active-Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture retrievals using radar observations have been challenging in the past due to complicating factors of surface roughness and vegetation scattering. Here, physically based forward models of radar scattering for individual vegetation types are inverted using a time-series approach to retrieve soil moisture while correcting for the effects of static roughness and dynamic vegetation. Compared with the past studies in homogeneous field scales, this paper performs a stringent test with the satellite data in the presence of terrain slope, subpixel heterogeneity, and vegetation growth. The retrieval process also addresses any deficiencies in the forward model by removing any time-averaged bias between model and observations and by adjusting the strength of vegetation contributions. The retrievals are assessed at 14 core validation sites representing a wide range of global soil and vegetation conditions over grass, pasture, shrub, woody savanna, corn, wheat, and soybean fields. The predictions of the forward models used agree with SMAP measurements to within 0.5 dB unbiased-root-mean-square error (ubRMSE) and -0.05 dB (bias) for both copolarizations. Soil moisture retrievals have an accuracy of 0.052 m
    Language English
    Publishing date 2019-07-22
    Publishing country United States
    Document type Journal Article
    ISSN 0196-2892
    ISSN 0196-2892
    DOI 10.1109/TGRS.2016.2631126
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Surface Soil Moisture Retrieval Using the L-Band Synthetic Aperture Radar Onboard the Soil Moisture Active–Passive Satellite and Evaluation at Core Validation Sites

    Kim, Seung-Bum / Van Zyl, Jakob J / Johnson, Joel T / Moghaddam, Matha / Tsang, Leung / Colliander, Andreas / Dunbar, Roy Scott / Jackson, Thomas J / Jaruwatanadilok, Sermsak / West, Richard / Berg, Aaron / Caldwell, Todd / Cosh, Michael H / Goodrich, David C / Livingston, Stanley / Lopez-Baeza, Ernesto / Rowlandson, Tracy / Thibeault, Marc / Walker, Jeffrey P /
    Entekhabi, Dara / Njoku, Eni G / O'Neill, Peggy E / Yueh, Simon H

    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. 2017 Jan. 19, v. 55, no. 4

    2017  

    Abstract: This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active–Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid- ... ...

    Abstract This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active–Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture retrievals using radar observations have been challenging in the past due to complicating factors of surface roughness and vegetation scattering. Here, physically based forward models of radar scattering for individual vegetation types are inverted using a time-series approach to retrieve soil moisture while correcting for the effects of static roughness and dynamic vegetation. Compared with the past studies in homogeneous field scales, this paper performs a stringent test with the satellite data in the presence of terrain slope, subpixel heterogeneity, and vegetation growth. The retrieval process also addresses any deficiencies in the forward model by removing any time-averaged bias between model and observations and by adjusting the strength of vegetation contributions. The retrievals are assessed at 14 core validation sites representing a wide range of global soil and vegetation conditions over grass, pasture, shrub, woody savanna, corn, wheat, and soybean fields. The predictions of the forward models used agree with SMAP measurements to within 0.5 dB unbiased root- mean-square error (ubRMSE) and −0.05 dB (bias) for both copolarizations. Soil moisture retrievals have an accuracy of 0.052 m3/m3 ubRMSE, −0.015 m3/m3 bias, and a correlation of 0.50, compared to in situ measurements, thus meeting the accuracy target of 0.06 m3/m3 ubRMSE. The successful retrieval demonstrates the feasibility of a physically based time series retrieval with L-band SAR data for characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation.
    Keywords accuracy ; corn ; data collection ; grasses ; models ; pastures ; prediction ; remote sensing ; roughness ; satellites ; savannas ; shrubs ; soil water ; soybeans ; surface roughness ; synthetic aperture radar ; time series analysis ; wheat
    Language English
    Dates of publication 2017-0119
    Size p. 1897-1914.
    Document type Article
    ISSN 0196-2892
    DOI 10.1109/TGRS.2016.2631126
    Database NAL-Catalogue (AGRICOLA)

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