Students review the NASA video showing biosphere data over the North Atlantic Ocean as a time series animation displaying a decade of phytoplankton blooms and answer questions.
Educational Resources - Search Tool
Students identify patterns and describe the relationship between chlorophyll concentration and incoming shortwave radiation.
This StoryMap lesson plan allows students to explore global phytoplankton distribution using chlorophyll concentration data in a 5 E-learning cycle. Students will investigate the processes that allow phytoplankton populations to thrive, as well as how their role in the carbon cycle impacts the other spheres of the Earth System.
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 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 will identify and describe the relationship between watersheds and phytoplankton distribution.
The Earth System Satellite Images, along with the Data Literacy Cubes, help the learner determine relationships among variables.
The Earth System Satellite Images, along with the Data Literacy Cubes, help the learner visualize how different Earth system variables change over time, establish cause and effect relationships, identify patterns, and determine relationships among variables.
The activities in this guide will help students understand variations in environmental parameters by examining connections among different phenomena measured on local, regional and global scales.
The Earth System Satellite Images, along with the Data Literacy Cubes, help the learner visualize how different Earth system variables change over time, identify patterns, and determine relationships among two variables in three months.