Data and code from: Characterizing wildfire behavior with ECOSTRESS land surface temperature across four California case studies

Since 2000, wildfires in the western United States have increased in both frequency and intensity due to climate warming, prolonged drought, and expanded human activity. Although geostationary systems enable rapid detection and moderate-resolution sensors offer broad coverage, a gap persists for high-spatial-resolution thermal observations that can assess fine-scale fire behavior. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) provides 70-meter land surface temperature (LST) observations with multi-day revisit intervals, providing enhanced spatial detail for active-fire analysis. In this study, we evaluate the capability of ECOSTRESS Level 2 LST data, which uses 5 thermal bands, to characterize wildfire behavior across four California fires: Carr (2018), Kincade (2019), August Complex (2020), and Dixie (2021). We developed a consistent framework to identify hotspots (LST ≥ 60°C), estimate a satellite-derived rate-of-spread (ROS) proxy using the 95th ..., , # Data and code from: Characterizing wildfire behavior with ECOSTRESS land surface temperature across four California case studies

## ECOSTRESS LST Wildfire Analysis

Code and data repository for \"Characterizing Wildfire Behavior with ECOSTRESS Land Surface Temperature Across Four California Case Studies.\" This repository contains the complete processing workflow, analysis scripts, and datasets used to characterize fire behavior, rate of spread, hotspot distributions, burn severity relationships, and spatial validation of ECOSTRESS LST against VIIRS fire radiative power across the Carr (2018), Kincade (2019), August Complex (2020), and Dixie (2021) fires.

## Repository Structure

```
├── README.md
├── Part_I_Raw_Data_Processing.mlx # MATLAB live script (raw HDF5 to daily GeoTIFFs)
├── Part_II_ROS_HS_dNBR_Analysis.py # Python — ROS, hotspots, dNBR, regression
├── Part_III_Validation.py # Python ..., ,