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

Data sets

SMAP soil moisture assimilated Noah-MP model output J. A. Ahmad, B. A. Forman, and S. V. Kumar

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

FluxSAT Gross Primary Production Goddard Space Flight Center

GOME-2 Fluorescence Goddard Space Flight Center

ECOSTRESS Evapotranspiration dis-ALEXI Daily L3 CONUS 70 m V001 S. Hook and K. Cawse-Nicholson

Global hybrid STATSGO/FAO soil texture National Center for Atmospheric Research

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

The observation data of soil temperature and moisture on the Tibetan Plateau (2008-2016) B. Su

Model code and software


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.