Agricultural Drought Monitoring via the Assimilation of SMAP Soil Moisture Retrievals Into a Global Soil Water Balance Model

Iliana E Mladenova; John D Bolten; Wade Crow; Nazmus Sazib; Curt Reynolds

doi:10.3389/fdata.2020.00010

Agricultural Drought Monitoring via the Assimilation of SMAP Soil Moisture Retrievals Into a Global Soil Water Balance Model

Front Big Data. 2020 Apr 9:3:10. doi: 10.3389/fdata.2020.00010. eCollection 2020.

Authors

Iliana E Mladenova^{1

2}, John D Bolten¹, Wade Crow³, Nazmus Sazib^{1

4}, Curt Reynolds⁵

Affiliations

¹ NASA GSFC, Hydrological Sciences Lab (617), Greenbelt, MD, United States.
² UMD, Earth System Science Interdisciplinary Center, College Park, MD, United States.
³ USDA ARS, Hydrology and Remote Sensing Lab, Beltsville, MD, United States.
⁴ Science Application International Corporation, Lanham, MD, United States.
⁵ USDA FAS, Washington, DC, United States.

Abstract

From an agricultural perspective, drought refers to an unusual deficiency of plant available water in the root-zone of the soil profile. This paper focuses on evaluating the benefit of assimilating soil moisture retrievals from the Soil Moisture Active Passive (SMAP) mission into the USDA-FAS Palmer model for agricultural drought monitoring. This will be done by examining the standardized soil moisture anomaly index. The skill of the SMAP-enhanced Palmer model is assessed over three agricultural regions that have experienced major drought since the launch of SMAP in early 2015: (1) the 2015 drought in California (CA), USA, (2) the 2017 drought in South Africa, and (3) the 2018 mid-winter drought in Australia. During these three events, the SMAP-enhanced Palmer soil moisture estimates (PM+SMAP) are compared against the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) rainfall dataset and Normalized Difference Vegetation Index (NDVI) products. Results demonstrate the benefit of assimilating SMAP and confirm its potential for improving U.S. Department of Agriculture-Foreign Agricultural Service root-zone soil moisture information generated using the Palmer model. In particular, PM+SMAP soil moisture estimates are shown to enhance the spatial variability of Palmer model root-zone soil moisture estimates and adjust the Palmer model drought response to improve its consistency with ancillary CHIRPS precipitation and NDVI information.

Keywords: SMAP; agricultural drought; data assimilation; hydrologic modeling; soil moisture.