Project Team: Southeast U.S. Water Resources Team
Team Location: Marshall Space Flight Center, Huntsville, Alabama
Vegetative Normalized Multi-band Drought Index showing the drier conditions of the Black Belt running through Mississippi, Alabama, and Georgia on May 20, 2007.
Authors:
Kaylin Bugbee (University of Alabama in Huntsville)
Samuel Ayers (University of Alabama in Huntsville)
Leigh Baggett (University of Alabama in Huntsville)
Modeste Muhire (Michigan Technological University)
Mentors/Advisors:
Dr. Jeffrey Luvall (NASA Global Hydrology and Climate Center; Science Advisor)
Past/Other Contributors:
Alabama Agriculture Team (Marshall DEVELOP, Fall 2013)
Alabama Agriculture Team (Marshall DEVELOP, Summer 2012)
Abstract:
Several western states grow nearly half of all U.S. fruits, vegetables, and nuts. However, due to the scarcity of western water resources, southeastern agricultural production is expected to increase. This increase in production may lead to a higher demand of water resources. Droughts also strain water resources. Drought monitoring tools must be developed to improve crop and water resources management in the Southeast. Current monitoring methods utilize averaged weekly stream flow conditions to identify drought areas. However, this method often overlooks areas prone to micro-level droughts. To address this issue, alternative drought monitoring methods using NASA Earth observation data are investigated. Drought indices, including the Normalized Multi-band Drought Index (NMDI), the Land Surface Temperature (LST) – Normalized Difference Vegetation Index (NDVI) ratio (LST/NDVI), and the Vegetation Health Index (VHI), are calculated using Terra’s Moderate Resolution Imaging Spectroradiometer (MODIS). Groundwater withdrawals also are estimated using the Gravity Recovery and Climate Experiment (GRACE) instrument. Additionally, the drought indices are cross-checked with water stress values from the Grid Decision Support System for Agrotechnology Transfer (GriDSSAT) crop model. The drought indices investigated in this project can be used to create an easily accessible model that will serve as a decision-support tool for water monitoring agencies such as the U.S. Geological Survey (USGS). This tool will ultimately improve knowledge of drought conditions which will lead to more reliable water usage management practices and agricultural planning.
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