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

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.

Check out this interactive data visualization and simulation tool.  It explores the impact of collapsing polar ice sheets (Greenland and Antarctica) and their impact on global mean sea level rise, along with shrinkage in the livable area around the world.

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

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.  

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

Remote sensing scientists use sensors to analyze data and solve regional, national and global concerns. For instance, natural resource management, urban planning, and climate and weather prediction are applications of remote sensing. Many scientists develop new sensor systems, analytical techniques, or new applications for existing systems. 

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

Chemists study atomic and molecular structures and their interactions. 

The Earth System Satellite Images, along with the Data Literacy Cubes, help the learner determine relationships among variables.