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.

Students explore the spatial patterns observed in meteorological data and learn how this information is used to predict weather and understand climate behavior.

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.

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

NASA Worldview is a free online visualization tool that is a great launchpad for learners who are new (or veteran) users of satellite data.

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

LIDAR Remote Sensing Technologists uses remote sensing strategies to analyze data to solve problems in areas across the globe. They use LIDAR - Light Detection and Ranging - as a method of remote sensing to examine the surface of the Earth. 

At the core of scientific visualization is the representation of data graphically - through images, animations, and videos - to improve understanding and develop insight. Data visualizers develop data-driven images, maps, and visualizations from information collected by Earth-observing satellites, airborne missions, and ground measurements. Visualizations allow us to explore data, phenomena and behavior; they are particularly effective for showing large scales of time and space, and "invisible" processes (e.g. flows of energy and matter) as integral parts of the models.

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.