Use the Data Literacy Cube to guide students’ exploration of data to enrich their observations and inferences.  This is a flexible resource that may be used with a variety of graphical representations of data.  This activity requires a graph for students to evaluate.   Fo

The purpose of this activity is for students to create a desktop soil profile based on the biome region of the United States where your school is located.

The purpose of this activity is to have students use an Earth Systems perspective to identify the various causes associated with changes to Earth's forests as they review Landsat imagery of site locations from around the world.

This activity is modified from the USDA/US Forest Services' lesson found in the Natural Inquirer newsletter.  The purpose of this hands-on activity is to engage students in a similar process for monitoring forests as NASA scientists use to study the Biosphere, whereby they apply what they kn

Learn about volcanic ash and watch a visualization of the Calbuco volcano to see how ash travels around the world. 

Students analyze historic plant growth data (i.e., Peak Bloom dates) of Washington, D.C.’s famous cherry blossom trees, as well as atmospheric near surface temperatures as evidence for explaining the phenomena of earlier Peak Blooms in our nation’s capital.

Students collect and analyze temperature data to explore what governs how much energy is reflected. 

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

The activities in this guide will help students understand variations in environmental parameters by examining connections among different phenomena measured on local, regional and global scales.

The Earth System Satellite Images, help the learner visualize how different Earth system variables change over time, establish cause and effect relationships for a specific variable, identify patterns, and determine relationships among variables over one year.