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Mladenova IE, Bolten JD, Crow W, Sazib N, Reynolds C. Agricultural Drought Monitoring via the Assimilation of SMAP Soil Moisture Retrievals Into a Global Soil Water Balance Model. Front Big Data 2021; 3:10. [PMID: 33693385 PMCID: PMC7931972 DOI: 10.3389/fdata.2020.00010] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 03/09/2020] [Indexed: 11/30/2022] Open
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.
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Affiliation(s)
- Iliana E Mladenova
- NASA GSFC, Hydrological Sciences Lab (617), Greenbelt, MD, United States.,UMD, Earth System Science Interdisciplinary Center, College Park, MD, United States
| | - John D Bolten
- NASA GSFC, Hydrological Sciences Lab (617), Greenbelt, MD, United States
| | - Wade Crow
- USDA ARS, Hydrology and Remote Sensing Lab, Beltsville, MD, United States
| | - Nazmus Sazib
- NASA GSFC, Hydrological Sciences Lab (617), Greenbelt, MD, United States.,Science Application International Corporation, Lanham, MD, United States
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Uz SS, Ruane AC, Duncan BN, Tucker CJ, Huffman GJ, Mladenova IE, Osmanoglu B, Holmes TR, McNally A, Peters-Lidard C, Bolten JD, Das N, Rodell M, McCartney S, Anderson MC, Doorn B. Earth observations and integrative models in support of food and water security. Remote Sens Earth Syst Sci 2019; 2:18-38. [PMID: 33005873 PMCID: PMC7526267 DOI: 10.1007/s41976-019-0008-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 12/26/2018] [Accepted: 01/17/2019] [Indexed: 11/28/2022]
Abstract
Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries.
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Affiliation(s)
| | - Alex C. Ruane
- NASA Goddard Institute for Space Studies, Climate Impacts Group, New York, NY, USA
| | | | | | | | - Iliana E. Mladenova
- NASA Goddard Space Flight Center, Greenbelt, MD, USA
- Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
| | | | | | - Amy McNally
- NASA Goddard Space Flight Center, Greenbelt, MD, USA
- Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
| | | | | | - Narendra Das
- NASA Jet Propulsion Laboratory, Pasadena, CA, USA
| | | | - Sean McCartney
- NASA Goddard Space Flight Center, Greenbelt, MD, USA
- Science Systems and Applications, Inc., Lanham, MD, USA
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