In this mini-lesson, students analyze soil moisture quantities associated with Hurricane Harvey around Houston, Texas on August 25, 2017.

Students identify patterns in chlorophyll concentration data to formulate their explanations of phytoplankton distribution. 

Students will identify and describe the relationship between watersheds and phytoplankton distribution. 

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.

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 will explore the Nitrogen Cycle by modeling the movement of a nitrogen atom as it passes through the cycle. Students will stop in the different reservoirs along the way, answering questions about the processes that brought them to the different reservoirs.  

This lesson was based on an activity from UCAR Center for Science Education. 

Students watch a visualization video and answer questions on the potential of increasing megadroughts in the southwest and central United States from 1950-2095 using models created by soil moisture data.

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.

Information from satellites if often used to display information about objects. This information can include how things appear, as well as their contents. Explore how pixel data sequences can be used to create an image and interpret it.

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.