A Time-Series Approach to Estimating Soil Moisture From Vegetated Surfaces Using L-Band Radar Backscatter

Jeffrey D. Ouellette, Joel T. Johnson, Anna Balenzano, Francesco Mattia, Giuseppe Satalino, Seung Bum Kim, Roy Scott Dunbar, Andreas Colliander, Michael H Cosh, Todd G. Caldwell, Jeffrey P. Walker, Aaron Berg

Research output: Contribution to journalArticleResearchpeer-review

28 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)3186-3193
Number of pages8
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume55
Issue number6
DOIs
Publication statusPublished - 1 Jun 2017

Keywords

  • Parameter estimation
  • radar
  • remote sensing, soil

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