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.  

NASA visualizers take data – numbers, codes – and turn them into animations people can see and quickly understand.

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.

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.

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

Students are introduced to the Earthrise phenomenon by seeing the Earth as the Apollo 8 astronauts viewed our home planet for the first time from the Moon.  They will analyze a time series of mapped plots of Earth science variables that NASA monitors to better understand the Earth

In this lesson, Observing Earth’s Seasonal Changes, students observe patterns of average snow and ice amounts as they change from one month to another, as well as connect the concepts of the tilt and orbit of the Earth (causing the changing of seasons) with monthly snow/ice data from January 2008

Students review a video showing a global view of the top-of-atmosphere shortwave radiation from January 26 and 27, 2012 and answer the questions that follow.

In this mini lesson, students analyze a bar graph showing the relative forcings from natural and human factors that affect Earth's climate. They use information from this graph to assess the relative importance of these factors.

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.