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Article

Mapping Extreme Wildfires Using a Critical Threshold in SMAP Soil Moisture

by
Benjamin D. Goffin
1,2,*,
Aashutosh Aryal
1,2,
Quinton Deppert
2,
Kenton W. Ross
2,3 and
Venkataraman Lakshmi
1
1
Department of Civil and Environmental Engineering, University of Virginia, Charlottesville, VA 22904, USA
2
NASA DEVELOP National Program, Hampton, VA 23666, USA
3
NASA Langley Research Center, Hampton, VA 23666, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(13), 2457; https://doi.org/10.3390/rs16132457
Submission received: 23 April 2024 / Revised: 21 June 2024 / Accepted: 26 June 2024 / Published: 4 July 2024

Abstract

This study analyzed the ground conditions that allowed some extreme wildfires in 2017 and 2023 to take such proportions and burn around 750,000 ha across Central Chile. Using publicly available satellite data, we examined the relationship between the burned areas from the Moderate Resolution Imaging Spectroradiometers (MODIS) and their antecedent soil moisture from the Soil Moisture Active Passive (SMAP) mission. We found that a small number of fires were responsible for disproportionately large burned areas and that these megafires (i.e., >10,000 ha) were more likely to exhibit relatively drier conditions in the months and days prior. Based on this, we tested various thresholds in low antecedent soil moisture to identify areas more prone to megafires. By differentiating the moisture conditions below and above 0.14 m3/m3, we were able to map all of the 2017 megafires, at least in part. Our classification balanced the success and errors in prediction, yielding 54.1% recall and 75.9% precision (well above the 56.3% baseline). For 2023, the burned areas could not be classified as accurately, due to differences in pre-fire conditions. Overall, our research provided new insights into the link between satellite-based soil moisture and extreme wildfire events. Among other things, this study demonstrated that certain critical thresholds in SMAP had predictive skill to identify conditions more conducive to megafires. Ultimately, this work can be expanded to other parts of the world in support of enhanced wildfire mitigation and management.
Keywords: wildfire; burned area; soil moisture; MODIS; SMAP; binary classification; precision–recall curve; confusion matrix; Central Chile wildfire; burned area; soil moisture; MODIS; SMAP; binary classification; precision–recall curve; confusion matrix; Central Chile
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MDPI and ACS Style

Goffin, B.D.; Aryal, A.; Deppert, Q.; Ross, K.W.; Lakshmi, V. Mapping Extreme Wildfires Using a Critical Threshold in SMAP Soil Moisture. Remote Sens. 2024, 16, 2457. https://doi.org/10.3390/rs16132457

AMA Style

Goffin BD, Aryal A, Deppert Q, Ross KW, Lakshmi V. Mapping Extreme Wildfires Using a Critical Threshold in SMAP Soil Moisture. Remote Sensing. 2024; 16(13):2457. https://doi.org/10.3390/rs16132457

Chicago/Turabian Style

Goffin, Benjamin D., Aashutosh Aryal, Quinton Deppert, Kenton W. Ross, and Venkataraman Lakshmi. 2024. "Mapping Extreme Wildfires Using a Critical Threshold in SMAP Soil Moisture" Remote Sensing 16, no. 13: 2457. https://doi.org/10.3390/rs16132457

APA Style

Goffin, B. D., Aryal, A., Deppert, Q., Ross, K. W., & Lakshmi, V. (2024). Mapping Extreme Wildfires Using a Critical Threshold in SMAP Soil Moisture. Remote Sensing, 16(13), 2457. https://doi.org/10.3390/rs16132457

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