Students will analyze and interpret maps of the average net atmospheric radiation to compare the flow of energy from the Sun toward Earth in different months and for cloudy versus clear days. Students will draw conclusions and support them with evidence.

Students will synthesize information from maps that show population, concentrations of PM_2.5, and PM_2.5-attributable mortality across the globe in order to draw conclusions about the relationship between particulate pollution and human health. 

Carbon dioxide concentration in the atmosphere is affected by many processes including fires, deforestation, and plant respiration. Students will evaluate a Landsat image to determine the rate of carbon dioxide sequestration in a particular area.

Students will analyze a pie chart (circle graph) showing the distribution of different parts of the Earth system's absorption and reflection of energy. 

My NASA Data has recently released several new resources, StoryMaps, for use in educational settings.

This StoryMap 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. 

The Earth System Satellite Images, along with the Data Literacy Cubes, helps the learner identify patterns in a specific image.  

Students will engage in a “Zoom In Inquiry” learning routine to understand the symbols on a world map that shows population-weighted concentrations of PM_2.5. They will reflect on how their perception of the image changed as they saw more of the image.

Students will engage in a “Zoom In Inquiry” learning routine to understand a world map that shows changes in PM_2.5-attributable mortality per 100,000 population (Bondie, 2013).

The Earth System Satellite Images, along with the Data Literacy Cubes, help the learner visualize how different Earth system variables change over time, establish cause and effect relationships, identify patterns, and determine relationships among variables.