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Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Surface soil moisture retrieval using the L-band synthetic aperture radar onboard the Soil Moisture Active Passive satellite and evaluation at core validation sites

Author
item Kim, S. - Jet Propulsion Laboratory
item Van Zyl, J. - Jet Propulsion Laboratory
item Johnson, J. - The Ohio State University
item Moghaddam, M. - University Of Michigan
item Tsang, L. - University Of Michigan
item Colliander, A. - Jet Propulsion Laboratory
item Dunbar, R.s. - Jet Propulsion Laboratory
item Jackson, Thomas
item Jarauwatanadilok, S. - Jet Propulsion Laboratory
item West, R. - Jet Propulsion Laboratory
item Berg, A. - University Of Guelph
item Caldwell, T. - University Of Texas
item Cosh, Michael
item Goodrich, David - Dave
item Livingston, Stanley
item Lopez, Baeza - University Of Valencia
item Rowlandson, Tracy - University Of Guelph
item Thibeault, M. - Universidad De Buenos Aires
item Walker, J. - Monash University
item Entekhabi, D. - Broad Institute Of Mit/harvard
item Njoku, E. - Jet Propulsion Laboratory
item O'neill, Peggy.e. - Goddard Space Flight Center
item Yueh, S. - Jet Propulsion Laboratory

Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/1/2016
Publication Date: 4/1/2017
Publication URL: http://handle.nal.usda.gov/10113/5729146
Citation: Kim, S., Van Zyl, J., Johnson, J., Moghaddam, M., Tsang, L., Colliander, A., Dunbar, R., Jackson, T.J., Jarauwatanadilok, S., West, R., Berg, A., Caldwell, T., Cosh, M.H., Goodrich, D.C., Livingston, S.J., Lopez, B., Rowlandson, T., Thibeault, M., Walker, J., Entekhabi, D., Njoku, E., O'Neill, P., Yueh, S. 2017. Surface soil moisture retrieval using the L-band synthetic aperture radar onboard the Soil Moisture Active Passive satellite and evaluation at core validation sites. IEEE Transactions on Geoscience and Remote Sensing. 55(4):1897-1914.

Interpretive Summary: The retrieval of soil moisture in the top 5-cm layer at a 3-km spatial resolution using the L-band dual-copolarized Soil Moisture Active Passive (SMAP) synthetic aperture radar data was validated using in situ observations. 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. Retrievals were assessed at core validation sites representing a wide range of global soil and vegetation conditions over grass, pasture, shrub, woody savanna, corn, wheat, and soybean fields. Soil moisture retrievals were better than the accuracy target of 0.06 m3/m3 unbiased root mean square error. 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.

Technical 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-RMSE (root mean square error, ubRMSE) and -0.05 dB (bias) for both co-polarizations. Soil moisture retrievals have an accuracy of 0.052 m3/m3 ubRMSE, -0.015 m3/m3 bias, and a correlation of 0.50, as compared to in-situ measurements, thus meeting the accuracy target of 0.06 m3/m3 unbiased RMSE. 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.