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Title: A time-series approach to estimating soil moisture from vegetated surfaces using L-band radar backscatter

item OUELLETTE, J.D. - Naval Research Laboratory
item JOHNSON, J. - The Ohio State University
item BALENZANO, A. - Consiglio Nazionale Delle Ricerche
item MATTIA, F. - Consiglio Nazionale Delle Ricerche
item SATALINIO, G. - Consiglio Nazionale Delle Ricerche
item KIM, S. - Jet Propulsion Laboratory
item DUNBAR, R.S. - Jet Propulsion Laboratory
item COLLIANDER, A. - Jet Propulsion Laboratory
item Cosh, Michael
item CALDWELL, T. - University Of Texas
item WALKER, J. - Monash University
item BERG, A. - University Of Guelph

Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/24/2016
Publication Date: 2/24/2017
Citation: Ouellette, J., Johnson, J., Balenzano, A., Mattia, F., Satalinio, G., Kim, S., Dunbar, R., Colliander, A., Cosh, M.H., Caldwell, T., Walker, J., Berg, A. 2017. A time-series approach to estimating soil moisture from vegetated surfaces using L-band radar backscatter. IEEE Transactions on Geoscience and Remote Sensing. 99:1-8

Interpretive Summary: The NASA Soil Moisture Active Passive (SMAP) mission was designed to produce three resolutions of soil moisture product, 36, 9, and 3 km. The 3 km product was based on only the radar portion of the SMAP signal and is sensitive to dense vegetation. This study demonstrated the accuracy of the radar algorithm for estimating soil moisture at 3 km and it was shown to be accurate versus ground based networks. While no metric was required, the radar product had a root mean squared error of less than 0.07 m3/m3 volumetric soil moisture, which is very reasonable. This result is very useful for large scale farm operations and county extension agents who can work on the 3 km management scale.

Technical Abstract: Many previous studies have shown the sensitivity of radar backscatter to surface soil moisture content, particularly at L-band. Moreover, the estimation of soil moisture from radar for bare soil surfaces is well-documented, but estimation underneath a vegetation canopy remains unsolved. Vegetation significantly increases the complexity of modeling the electromagnetic scattering in the observed scene, and can even obstruct the contributions from the underlying soil surface. Existing approaches to estimating soil moisture under vegetation using radar typically rely on a forward model to describe the backscattered signal and often require that the vegetation characteristics of the observed scene be provided by an ancillary data source. However, such information may not be reliable or available during the radar overpass of the observed scene (e.g. due to cloud coverage if derived from an optical sensor). Thus, the approach described herein is an extension of a change-detection method for soil moisture estimation, which does not require ancillary vegetation information, nor does it make use of a complicated forward scattering model. Novel modifications to the original algorithm include extension to multiple polarizations and a new technique for bounding the radar-derived soil moisture product using radiometer-based soil moisture estimates. Soil moisture estimates are generated using data from the Soil Moisture Active/Passive (SMAP) satellite-borne radar and radiometer data, and are compared with up-scaled data from a selection of in-situ networks used in SMAP validation activities. These results show that the new algorithm can consistently achieve RMS errors less than 0.07 m3/m3 over a variety land cover types.