Teachers, are you looking for resources to help you engage students in data analysis related to changes in the cryosphere using albedo values? Check out these images. 

The advance-and-retreat cycle of snow cover drastically changes the whiteness and brightness of Earth. Using two maps created using NASA satellite data for 2017, students review the seasonal differences of snow and ice extent and answer questions on their observations.

This graphic organizer may be used to help students analyze the processes and components of Earth System phenomena.

This video provides tips for teachers on helping students make sense of data to help them understand and work with data. It is based on the work of Kristin Hunter-Thomson of Dataspire.org and uses data from the My NASA Data Earth System Data Explorer.

This mini lesson helps students visualize how the Hydrosphere and Cryosphere interact to produce changes in land and sea ice. 

Using various visualizations (i.e., images, charts, and graphs), students will explore changes in sea ice extent as it relates to other spheres within the Earth System. This StoryMap is intended to be used with students who have access to the internet in a 1:1 or 1:2 setting.

Students examine the two time series images to determine the differences between seasonal ice melt over water versus land.

Guided by the 5E model, this lesson allows students to work together to uncover how changes in sea ice extent in the Arctic and Antarctic regions are connected to Earth’s energy budget.

Students consider the impact of changing conditions on the remote island of Little Diomede, Alaska after they investigate the relationship between seasonal trends in sea ice extent with shortwave and longwave radiation flux described in Earth’s energy budget.

Students analyze two North Pole orthographic data visualizations produced from soil moisture data. After describing trends in the seasonal thaw of land surfaces, students demonstrate their understanding of Earth’s energy budget by explaining relationships and make predictions about the dataset.