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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #370695
Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Agricultural drought monitoring via the assimilation of SMAP soil moisture retrievals into a global soil water balance model

Author
item MLADENOVA, I. - Goddard Space Flight Center
item BOLTEN, J. - Goddard Space Flight Center
item Crow, Wade
item SAZIB, N. - Goddard Space Flight Center
item REYNOLDS, C. - US Department Of Agriculture (USDA)

Submitted to: Frontiers in Big Data
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/15/2020
Publication Date: 4/14/2020
Citation: Mladenova, I., Bolten, J., Crow, W.T., Sazib, N., Reynolds, C. 2020. Agricultural drought monitoring via the assimilation of SMAP soil moisture retrievals into a global soil water balance model. Frontiers in Big Data. https://doi.org/10.3389/fdata.2020.00010.
DOI: https://doi.org/10.3389/fdata.2020.00010

Interpretive Summary: The USDA Foreign Agricultural Service (FAS) is tasked with the monitoring international agricultural productivity to: i) support the global competitiveness of US domestic farm production, ii) anticipate social disruptions due to food insecurity, and iii) provide early famine detection. A key part of this monitoring is assessing the availability of adequate water in the soil to support crop and rangeland production. The USDA FAS global soil moisture monitoring system has recently been upgraded via the use of satellite-derived surface soil moisture observations. This paper examines the impact of this upgrade on the system’s ability to monitor the early onset of agricultural drought using three recent drought events as case studies. Results demonstrate the ability of satellite-derived soil moisture products to aid in the global monitoring of agricultural drought. Results of this study are currently being used by USDA FAS to refine their soil moisture monitoring system and improve their ability to forecast disruptions in global food supplies due to agricultural drought.

Technical Abstract: From an agricultural perspective, drought refers to a deficiency of plant available water in the root-zone of the soil profile. This paper focusses 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. To this end, 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, PM+SMAP soil moisture estimates are compared against the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) rainfall dataset and the Normalized Difference Vegetation Index (NDVI). Results demonstrate the benefit of assimilating SMAP and confirm its potential for improving USDA-FAS 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.