Estimating time-dependent vegetation biases  in the SMAP soil moisture product

Zwieback, Simon; Colliander, Andreas; Cosh, Michael H.; Martínez-Fernández, José; McNairn, Heather; Starks, Patrick J.; Thibeault, Marc; Berg, Aaron

doi:https://doi.org/10.5194/hess-22-4473-2018

Articles | Volume 22, issue 8

https://doi.org/10.5194/hess-22-4473-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-22-4473-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 8

Research article

| Highlight paper

|

22 Aug 2018

Research article | Highlight paper |

| 22 Aug 2018

Estimating time-dependent vegetation biases in the SMAP soil moisture product

Simon Zwieback, Andreas Colliander, Michael H. Cosh, José Martínez-Fernández, Heather McNairn, Patrick J. Starks, Marc Thibeault, and Aaron Berg

Supplement

https://doi.org/10.5194/hess-22-4473-2018-supplement

Model code and software

Bayesian Triple Collocation S. Zwieback https://doi.org/10.5281/zenodo.1326856

Short summary

Satellite soil moisture products can provide critical information on incipient droughts and the interplay between vegetation and water availability. However, time-variant systematic errors in the soil moisture products may impede their usefulness. Using a novel statistical approach, we detect such errors (associated with changing vegetation) in the SMAP soil moisture product. The vegetation-associated biases impede drought detection and the quantification of vegetation–water interactions.