Students move through a series of short activities to explore and evaluate global solar radiation data from NASA satellites. In this process, students make qualitative and quantitative observations about seasonal variations in net energy input to the Earth System.
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This story map allows students to explore the urban heat island effect using land surface temperature and vegetation data in a 5 E-learning cycle. Students investigate the processes that create differences in surface temperatures, as well as how human activities have led to the creation of urban heat islands.
This lesson, "Awenasa Goes to Camp!," is a data analysis activity that presents maps of NASA Earth satellite data for a variety of locations across the United States for four unidentified months throughout the year. Each location represents a real science camp th
Learners follow adventurous camper, Awenasa, as she travels the United States and attends various camp locations throughout the year. Learners analyze data to find her location among the various campsites using monthly averaged NASA satellite data (Cloud Coverage, Surface (S
Students observe monthly images of changing vegetation patterns, looking for seasonal changes occurring throughout 2017. These data can be used by students to develop their own models of change.
By investigating the data presented in a model that displays extreme summer air temperatures, students explain energy transfer in the Earth system and consider the impact of excessive heat on local communities.
Students construct explanations about Earth’s energy budget by connecting a model with observations from side-by-side animations of the monthly mapped data showing incoming and outgoing shortwave radiation from Earth’s surface.
The Earth System Satellite Images help students observe and analyze global Earth and environmental data, understand the relationship among different environmental variables, and explore how the data change seasonally and over longer timescales.
This lesson walks students through the use of Landsat false-color imagery and identification of different land cover features using these as models.