IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Jan 2022)
Regularized Dual-Channel Algorithm for the Retrieval of Soil Moisture and Vegetation Optical Depth From SMAP Measurements
- Julian Chaubell,
- Simon Yueh,
- R. Scott Dunbar,
- Andreas Colliander,
- Dara Entekhabi,
- Steven K. Chan,
- Fan Chen,
- Xiaolan Xu,
- Rajat Bindlish,
- Peggy O'Neill,
- Jun Asanuma,
- Aaron A. Berg,
- David D. Bosch,
- Todd Caldwell,
- Michael H. Cosh,
- Chandra Holifield Collins,
- Karsten H. Jensen,
- Jose Martinez-Fernandez,
- Mark Seyfried,
- Patrick J. Starks,
- Zhongbo Su,
- Marc Thibeault,
- Jeffrey P. Walker
Affiliations
- Julian Chaubell
- ORCiD
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
- Simon Yueh
- ORCiD
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
- R. Scott Dunbar
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
- Andreas Colliander
- ORCiD
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
- Dara Entekhabi
- ORCiD
- Massachusetts Institute of Technology, Cambridge, MA, USA
- Steven K. Chan
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
- Fan Chen
- ORCiD
- SSAL Inc., Greenbelt, MD and USDA Agricultural Research Service, Beltsville, MD, USA
- Xiaolan Xu
- ORCiD
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
- Rajat Bindlish
- ORCiD
- Goddard Space Flight Center, Greenbelt, MD, USA
- Peggy O'Neill
- Goddard Space Flight Center, Greenbelt, MD, USA
- Jun Asanuma
- Center for Research in Isotopes and Environmental Dynamics, Tsukuba University, Tsukuba, Japan
- Aaron A. Berg
- ORCiD
- Department of Geography, Environment and Geomatics, University of Guelph, Guelph, ON, Canada
- David D. Bosch
- ORCiD
- USDA Agricultural Research Service Southeast Watershed Research Laboratory, Tifton, GA, USA
- Todd Caldwell
- ORCiD
- University of Texas at Austin, Bureau of Economic Geology, Austin, USA
- Michael H. Cosh
- USDA Agricultural Research Service Beltsville Agricultural Research Center, Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA
- Chandra Holifield Collins
- USDA Agricultural Research Service Southwest Watershed Research, Southwest Watershed Research Center, Tucson, AZ, USA
- Karsten H. Jensen
- Department of Geosciences and Natural Resource Management, Copenhagen K, Denmark
- Jose Martinez-Fernandez
- Instituto de Investigacin en Agrobiotecnologa (CIALE), Universidad de Salamanca, Salamanca, Spain
- Mark Seyfried
- USDA Agricultural Research Service Northwest Watershed Research Center, Boise, ID, USA
- Patrick J. Starks
- USDA Agricultural Research Service Great Plains Agroclimate and Natural Resources Research Unit, El Reno, OK, USA
- Zhongbo Su
- ITC Faculty, University of Twente, Enschede, The Netherlands
- Marc Thibeault
- ORCiD
- Comisión Nacional de Actividades Espaciales, Buenos Aires, Argentina
- Jeffrey P. Walker
- ORCiD
- Monash University, Melbourne, VIC, Australia
- DOI
- https://doi.org/10.1109/JSTARS.2021.3123932
- Journal volume & issue
-
Vol. 15
pp. 102 – 114
Abstract
In August 2020, soil moisture active passive (SMAP) released a new version of its soil moisture and vegetation optical depth (VOD) retrieval products. In this article, we review the methodology followed by the SMAP regularized dual-channel retrieval algorithm. We show that the new implementation generates 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. Due to a lack of in situ measurements we cannot evaluate the accuracy of the VOD. In this article, 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.
Keywords
- Dual-channel algorithm
- soil moisture active passive (SMAP)
- soil moisture (SM) retrieval
- vegetation optical depth (VOD) retrieval
