In this activity students will compare different methods for observing the Sun’s corona and make predictions about what they will observe during the April 8, 2024 total solar eclipse.
In this activity students will compare different methods for observing the Sun’s corona and make predictions about what they will observe during the April 8, 2024 total solar eclipse.
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.
Students observe monthly images of changing vegetation patterns, looking for seasonal changes occurring throughout 2017. These data can be used by students to develop their own models of change.
This mini lesson focuses on Landsat satellite data and how it is used to detect changes in land use. Students will answer questions based off of a NASA Video that features how Landsat data are interpreted in the forests of the Pacific Northwest, and gives examples of the effects insects and logging have with land management.
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.
The Great Smoky Mountains have a unique climate and weather pattern. Students will review a Landsat image and read about the history of the area and why Native Americans called the area “Shaconage.” Then they will answer the questions about what caused the unusual “blue smoke.”
This mini lesson helps students visualize how the Hydrosphere and Cryosphere interact to produce changes in land and sea ice.
Students analyze and compare satellite data of Ocean Chlorophyll Concentrations with Sea Surface Temperatures, beginning with the North Atlantic region, while answering questions about the global patterns of these phenomenon.
Students collect and analyze temperature data to explore what governs how much energy is reflected.
This lesson walks students through the use of Landsat false-color imagery and identification of different land cover features using these as models.