Regularized dual-channel algorithm for the retrieval of soil moisture and vegetation optical depth from SMAP measurements

Mario J. Chaubell, Simon H. Yueh, Scott Dunbar, Andreas Colliander, Dara Entekhabi, Steven K. Chan, Fan Chen, Xiaolan Xu, Rajat Bindlish, Peggy E. Oneill, Jun Asanuma, Aaron Berg, David D. Bosch, Todd Caldwell, Michael Cosh, Chandra D. Holifield Collins, Karsten H. Jensen, Jose Martinez-Fernandez, Mark Seyfried, Patrick StarksBob Su, Marc Thibeault, Jeffrey Walker

Research output: Contribution to journalArticleResearchpeer-review

12 Citations (Scopus)


In August 2020, SMAP released a new version of its soil moisture (SM) and vegetation optical depth (VOD) retrieval products. In this work, we review the methodology followed by the SMAP regularized dual-channel (DCA) retrieval algorithm. We show that the new implementation generated SM retrievals that not only satisfy the SMAP accuracy requirements but also show a performance comparable to the single-channel algorithm that uses the V polarized brightness temperature (SCA-V). Due to a lack of in situ measurements we cannot evaluate the accuracy of the VOD. In this work, we show analyses with the intention of providing an understanding of the VOD product. We compare the VOD results with those from SMOS. We also study the relation of the SMAP VOD with two vegetation parameters: tree height and biomass.

Original languageEnglish
Pages (from-to)102-114
Number of pages13
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication statusPublished - 29 Oct 2021


  • Brightness temperature
  • dual-channel algorithm
  • Histograms
  • L-band
  • Ocean temperature
  • Optical polarization
  • Optical sensors
  • SMAP
  • soil moisture retrieval
  • Vegetation mapping
  • vegetation optical depth retrieval

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