Evaluation of SMOS Data to Provide Prefire Conditions’ Information for Forest Fire Danger Rating System in Canada

Forest fires in Canada’s boreal forest can cause a great deal of concern for populations, environment and infrastructures. One of the tools developed to predict potential fire activity related to these events is the Canadian Fire Weather Index System (FWI<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">_CAN</i>). This study aims to analyse the potential of Soil Moisture and Ocean Salinity (SMOS) satellite products (Soil Moisture (SM), Vegetation Optical Depth (VOD) and Root Zone Soil Moisture (RZSM)) to provide additional information on pre-fire soil and vegetion conditions for forest fire danger rating system. Using Random Forest algorithm, we show that adding SMOS data to the FWI<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">_CAN</i> system indices slightly increases the accuracy of the predictions of potential fire activity. The RZSM from SMOS data was the variable that best improved the model performance, whereas the VOD provided no additional information. In the aim to evaluate the method for regions with limited in situ meteorological data, we used FWI system indices calculated from ERA5 available over the globe (FWI<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">_ERA5</i>). Taking into account FWIERA5, SMOS data allow to improve substantially the ability to predict forest fire.