Introduction to thermal remote sensing and applications in urban heat island mapping
Description
Extreme heat events are defined as prolonged periods of excessively high temperatures for multiple consecutive days. According to the World Health Organization (WHO), heat stress is the leading cause of weather-related deaths and can exacerbate accidents, underlying illnesses, and the transmission of some infectious diseases. This intermediate-level training equips participants with the foundational theory and practical skills to leverage thermal infrared (TIR) remote sensing to quantify these risks.
The course begins by establishing the physical principles of TIR, including emissivity and blackbody radiation. While these fundamentals are broadly applicable to numerous applications, this training specifically focuses on using data from NASA’s Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) to identify heat-vulnerable communities and quantify urban heat island (UHI) effects.
In the hands-on component, participants will process ECOSTRESS Land Surface Temperature (LST) data in R – including quality filtering and time-of-day subsetting – and apply the interactive ECOSTRESS LST Downscaling Tool in Google Earth Engine. This tool uses a random forest model to enhance spatial resolution from 70 m to 10 m, translating satellite observations into street-scale thermal maps suitable for urban planning, strategic greenspace placement, and extreme heat early warning systems.
No prior programming experience is required.
Description
Extreme heat events are defined as prolonged periods of excessively high temperatures for multiple consecutive days. According to the World Health Organization (WHO), heat stress is the leading cause of weather-related deaths and can exacerbate accidents, underlying illnesses, and the transmission of some infectious diseases. This intermediate-level training equips participants with the foundational theory and practical skills to leverage thermal infrared (TIR) remote sensing to quantify these risks.
The course begins by establishing the physical principles of TIR, including emissivity and blackbody radiation. While these fundamentals are broadly applicable to numerous applications, this training specifically focuses on using data from NASA’s Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) to identify heat-vulnerable communities and quantify urban heat island (UHI) effects.
In the hands-on component, participants will process ECOSTRESS Land Surface Temperature (LST) data in R – including quality filtering and time-of-day subsetting – and apply the interactive ECOSTRESS LST Downscaling Tool in Google Earth Engine. This tool uses a random forest model to enhance spatial resolution from 70 m to 10 m, translating satellite observations into street-scale thermal maps suitable for urban planning, strategic greenspace placement, and extreme heat early warning systems.
No prior programming experience is required.
Prerequisites
Objectives
By the end of this training attendees will be able to:
- Identify the fundamental concepts and physical principles of thermal infrared remote sensing;
- Define the role of emissivity retrievals in ensuring the accuracy of satellite-derived land surface temperature products;
- Distinguish key differences between thermal and optical remote sensing approaches, including emission versus reflection, day/night capability, and atmospheric window considerations;
- Identify applications of thermal remote sensing data for ecosystems stewardship, agricultural management, climate adaptation, and urban planning;
- Compare the characteristics of current and upcoming thermal missions in context of their suitability to different application uses;
- Filter and visualize ECOSTRESS Land Surface Temperature (LST) data using provided R-based data processing workflows;
- Downscale native 70 m ECOSTRESS LST data to a fine 10 m spatial resolution using a Random Forest machine learning model implemented on an interactive Google Earth Engine (GEE) interface to analyze neighborhood-level urban heat patterns.
Target audience
Primary audiences: Urban planners, climate adaptation practitioners, and climate researchers who work with satellite data and have applied remote sensing knowledge. Participants should be comfortable with intermediate-level data analysis and have some experience with Python or the willingness to follow along with code examples.
Secondary audiences: Graduate students in environmental science, geography, or related fields; government personnel working on forest monitoring, urban heat mapping, or climate adaptation; and NGO staff involved in conservation and climate resilience projects.
Course format
- The complete course consists of two 1.5-hour parts, with Part 1 offered on May 26 and Part 2 on June 2.
- On each day, there are two opportunities to take the course (identical offerings):
- Session A: 11:00 a.m. to 12:30 p.m. EDT (UTC-5)
- Session B: 2:00 p.m. to 3:30 p.m. EDT (UTC-5)
- Each part will include a 30-minute live Q&A.
- Those who attend Parts 1 & 2 and complete the homework by the due date will receive a certificate of attendance.