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Integration of GNSS-R derived soil moisture into the USGS Wildland Fire Potential Index

Kurtis Nelson·Sreeja Roy-SIngh·Vinay Ravindra·Mahta Moghaddam·Archana Kannan·Amer Melebari
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Abstract

Observations of soil moisture have been found to improve predictions of fire danger as soil moisture impacts both fuel load and fuel moisture. A relative soil moisture process was developed and integrated into the USGS Wildland Fire Potential Index data products. The process uses soil moisture estimates from the CYGNSS GNSS-R constellation and compares current to historical values which then modifies the fuel moisture term in the WFPI equations. Comparisons of WFPI with the soil moisture enhanced version over known large fire events indicate a potential improvement in predicting large fire probability. The goal of this process is to identify areas prone to large fire activity that can be used to target additional CYGNSS observations to assist with wildfire monitoring.

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Kurtis Nelson, Sreeja Roy-SIngh, Vinay Ravindra, Mahta Moghaddam, Archana Kannan, Amer Melebari. 2025. Integration of GNSS-R derived soil moisture into the USGS Wildland Fire Potential Index. https://doi.org/10.1109/igarss55030.2025.11242770

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