This lesson walks students through the use of Landsat false-color imagery and identification of different land cover features using these as models. Building from an original GLOBE lesson, this resource features Google Slide and Jamboard to assist in both face-to-face and virtual learning.

Scientific data are often represented by assigning ranges of numbers to specific colors. The colors are then used to make false color images which allow us to see patterns more easily. Students will make a false-color image using a set of numbers.

Students identify and classify kinds of land cover (such as vegetation, urban areas, water, and bare soil) in Landsat satellite images of Phoenix, Arizona taken in 1984 and 2018.

Students will analyze the monthly seasonal chlorophyll concentration images in our global oceans for the four different months of 2017, and then answer the following questions.

This mini lesson engages students by watching a NASA video related to seasonal chlorophyll concentration as it relates to net radiation using NASA's Aqua satellite. Students will examine the model and answer the questions.

Students categorize causes, effects, and responses to volcanic hazards through an Earth system perspective. They use remotely sensed images to examine the visible effects of the eruption of Mount St. Helens in 1980 and identify a buffer zone for safer locations for development.

Several heat domes have occurred over the last few summers and around the world. This lesson provides one example from 2021 in Portland, Oregon, with temperature and ozone data.

Students move through a series of short activities to explore and evaluate global solar radiation data from NASA satellites.  In this process, students make qualitative and quantitative observations about seasonal variations in net energy input to the Earth System.

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

Students will engage in a “Zoom In Inquiry” learning routine to understand a world map that shows changes in PM_2.5-attributable mortality per 100,000 population (Bondie, 2013).