Droughts can severely reduce the productivity of agricultural lands and forests. The United States Department of Agriculture (USDA) Southeast Regional Climate Hub (SERCH) has launched the Lately Identified Geospecific Heightened Threat System (LIGHTS) to inform its users of potential water deficiency threats. The system identifies droughts and other climate anomalies such as extreme precipitation and heat stress. However, the LIGHTS model lacks input from soil moisture observations. This research aims to develop a simple and easy-to-interpret soil moisture and drought warning index—standardized soil moisture index (SSI)—by fusing the space-borne Soil Moisture Active Passive (SMAP) soil moisture data with the North American Land Data Assimilation System (NLDAS) Noah land surface model (LSM) output. Ground truth soil moisture data from the Soil Climate Analysis Network (SCAN) were collected for validation. As a result, the accuracy of using SMAP to monitor soil moisture content generally displayed a good statistical correlation with the SCAN data. The validation through the Palmer drought severity index (PDSI) and normalized difference water index (NDWI) suggested that SSI was effective and sensitive for short-term drought monitoring across large areas.

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.