Investigating FWI Moisture Codes in Relation to Satellite-Derived Soil Moisture Data across Varied Resolutions

• Published in: Fire (Basel, Switzerland) Vol. 7; no. 8; p. 272
• Main Authors: Atalay, Hatice, Sunar, Ayse Filiz, Dervisoglu, Adalet
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-08-2024
• Subjects: 21st century; Accuracy; Algorithms; Artificial satellites in remote sensing; Climate change; Correlation; correlations; Datasets; Drought; Environmental aspects; Environmental conditions; Environmental monitoring; fire weather index; Forest & brush fires; Forest fires; Fuel; fuel moisture codes; Fuels; Growing season; Humidity; Land surface temperature; Landsat; Methods; Moisture content; Precipitation; Radiation; Rain; Remote sensing; Risk assessment; Satellite observation; Satellites; SMAP; SMOS; Soil analysis; Soil investigations; Soil moisture; Soil stresses; Soil temperature; Statistical models; Temperature; Vegetation; Water content; Weather; Wildfires; Turkey; Greece; Portugal; Spain
• ISSN: 2571-6255; 2571-6255
• DOI: 10.3390/fire7080272

In the Mediterranean region, particularly in Antalya, southern Türkiye, rising forest fire risks due to climate change threaten ecosystems, property, and lives. Reduced soil moisture during the growing season is a key factor increasing fire risk by stressing plants and lowering fuel moisture content. This study assessed soil moisture and fuel moisture content (FMC) in ten fires (2019–2021) affecting over 50 hectares. The Fire Weather Index (FWI) and its components (FFMC, DMC, DC) were calculated using data from the General Directorate of Meteorology, EFFIS (8 km), and ERA5 (≈28 km) satellite sources. Relationships between FMCs, satellite-based soil moisture datasets (SMAP, SMOS), and land surface temperature (LST) data (MODIS, Landsat 8) were analyzed. Strong correlations were found between FWI codes and satellite soil moisture, particularly with SMAP. Positive correlations were observed between LST and FWIs, while negative correlations were evident with soil moisture. Statistical models integrating in situ soil moisture and EFFIS FWI (R: −0.86, −0.84, −0.83 for FFMC, DMC, DC) predicted soil moisture levels during extended fire events effectively, with model accuracy assessed through RMSE (0.60–3.64%). The SMAP surface (0–5 cm) dataset yielded a lower RMSE of 0.60–2.08%, aligning with its higher correlation. This study underlines the critical role of soil moisture in comprehensive fire risk assessments and highlights the necessity of incorporating modeled soil moisture data in fire management strategies, particularly in regions lacking comprehensive in situ monitoring.