Introduction to Thermal Remote Sensing and Applications in Urban Heat Island Mapping

Start Date:
May 26, 2026
End Date:
June 2, 2026
Description:
Extreme heat events are defined as prolonged periods of excessively high temperatures for multiple consecutive days. According to the World Health Organization, 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 remote sensing to quantify these risks.
Targeted 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.
Expected Outcomes:
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.
Host:
NASA Applied Remote Sensing Training Program (ARSET)
Organizer:
NASA Applied Remote Sensing Training Program (ARSET)
Format/Training Type:
Online Course, Webinar, Workshop
Language:
English
Attendance:
Open
Application Deadline:
June 2, 2026
Qualifications:
Fundamentals of Remote Sensing (https://www.earthdata.nasa.gov/learn/trainings/fundamentals-remote-sensing-0) or equivalent knowledge
Contact Name:
Brock Blevins
Contact Email:
brock.blevins@nasa.gov
Link:
https://www.earthdata.nasa.gov/learn/trainings/introduction-thermal-remote-sensing-applications-urban-heat-island-mapping
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