Students can interact with NASA data to build a custom visualizations of local, regional, or global plant growth patterns over time, using the Earth System Data Explorer to generate plots of satellite data as they develop models of this phenomenon.

This mini lesson engages students by watching a NASA video related to plant growth activity around the world using data from the NASA/NOAA Suomi NPP satellite and answering questions on these stability and change relationships.

Students analyze historic plant growth data (i.e., Peak Bloom dates) of Washington, D.C.’s famous cherry blossom trees, as well as atmospheric near surface temperatures as evidence for explaining the phenomena of earlier Peak Blooms in our nation’s capital.

This Lesson Plan provides maps, graphs, and data tables for use with the Data Literacy Cubes. Because it is a differentiated resource, it is appropriate for multiple grade bands. 

Interpret the map, or model, to find patterns in the occurrence of tropical cyclones from 1842 through 2018.

Using various visualizations (i.e., images, charts, and graphs), students will explore the energy exchange that occurs when hurricanes extract heat energy from the ocean. This StoryMap is intended to be used with students who have access to the internet in a 1:1 or 1:2 setting. 

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.

Students learn how to estimate the "energy efficiency" of photosynthesis, or the amount of energy that plants absorb for any given location on Earth. This is the ratio of the amount of energy stored to the amount of light energy absorbed and is used to evaluate and model photosynthesis efficiency.

Students investigate the effects of Hurricane Sandy and make a scale model of the storm over the continental United States to assess the area of impact.

The purpose of this lesson is for students to compare data displays to determine which best answers the driving question. To do this they will evaluate the spread of the data and what the displays show.