Soil moisture estimation in South Asia  via assimilation of SMAP retrievals

Ahmad, Jawairia A.; Forman, Barton A.; Kumar, Sujay V.

doi:https://doi.org/10.5194/hess-26-2221-2022

Articles | Volume 26, issue 8

https://doi.org/10.5194/hess-26-2221-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Microwave remote sensing for improved understanding of vegetation–water...

https://doi.org/10.5194/hess-26-2221-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 8

Research article

|

29 Apr 2022

Research article |

| 29 Apr 2022

Soil moisture estimation in South Asia via assimilation of SMAP retrievals

Jawairia A. Ahmad, Barton A. Forman, and Sujay V. Kumar

Supplement

https://doi.org/10.5194/hess-26-2221-2022-supplement

Data sets

SMAP soil moisture assimilated Noah-MP model output J. A. Ahmad, B. A. Forman, and S. V. Kumar https://doi.org/10.13016/meau-teqa

SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, Version 8 P. E. O'Neill, S. Chan, E. G. Njoku, T. Jackson, R. Bindlish, and J. Chaubell https://doi.org/10.5067/OMHVSRGFX38O

FluxSAT Gross Primary Production Goddard Space Flight Center https://avdc.gsfc.nasa.gov/pub/tmp/FluxSat_GPP/

GOME-2 Fluorescence Goddard Space Flight Center https://avdc.gsfc.nasa.gov/pub/data/satellite/MetOp/GOME_F/

ECOSTRESS Evapotranspiration dis-ALEXI Daily L3 CONUS 70 m V001 S. Hook and K. Cawse-Nicholson https://doi.org/10.5067/ECOSTRESS/ECO3ETALEXI.001

Global hybrid STATSGO/FAO soil texture National Center for Atmospheric Research https://doi.org/10.80024/UCAR/NCAR/EOL/DATASET/8.43

SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, Version 6 P. O'Neill, S. Chan, E. Njoku, T. Jackson, and R. Bindlish https://doi.org/10.5067/EVYDQ32FNWTH

The observation data of soil temperature and moisture on the Tibetan Plateau (2008-2016) B. Su https://www.geo.tuwien.ac.at/insitu/data_viewer/

Model code and software

NASA-LIS/LISF S. Kumar https://github.com/NASA-LIS/LISF

Short summary

Assimilation of remotely sensed data into a land surface model to improve the spatiotemporal estimation of soil moisture across South Asia exhibits potential. Satellite retrieval assimilation corrects biases that are generated due to an unmodeled hydrologic phenomenon, i.e., irrigation. The improvements in fine-scale, modeled soil moisture estimates by assimilating coarse-scale retrievals indicates the utility of the described methodology for data-scarce regions.