Fire Distinguisher: Using SMAP Data to Improve Wildfire Predictions

EarthzineDEVELOP 2016 Spring VPS, Original, Responding to Human Health Risks

Category: Responding to Human Health Risks
Project Team: Texas Water Resources II
Team Location: NASA Langley Research Center – Hampton, Virginia

Soil Moisture Active Passive image of Texas in April 2015. Soil moisture is defined in terms of cubic meters of water per cubic meters of soil. Image Credit: Texas Water Resources II Team

Greg Hoobchaak
Jessica Jozwik
Alyx Riebeling

Dr. Kenton Ross (NASA Langley Research Center)

Past/Other Contributors:
Emily Adams (Center Lead)
Megan Buzanowicz
Laura Lykens
Zacary Richards
Jeff Close


Each year, Texas experiences severe droughts, making large areas of the state vulnerable to wildfires that damage agriculture, infrastructure, and habitats across Texas. Texas Fire Services stated in its most recent report that just under 18,500 wildland fires occurred in 2014 causing almost $2 million in damages. The Texas Forest Service utilizes precipitation, temperature, vegetation, and soil moisture data to identify particular areas in danger of wildfires. Several methods exist to monitor soil moisture, but these methods rely on estimates from precipitation and temperature data or from testing specific locations with sensors. By incorporating satellite data into their monitoring practices, the Texas Forest Service can monitor and compare changing soil moisture levels throughout the year. Soil Moisture data obtained from NASA’s Soil Moisture Active Passive (SMAP) satellite was correlated with in situ data from the Slow Climate Analysis Network (SCAN) and Texas A&M University (TAMU) Soil Moisture Database. A single correction model for Texas was created from trends identified in the data.

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