After learning about the different characteristics of satellite data, students will describe the advantages and disadvantages of using two different satellites to study the Urban Heat Island Effect.  

By matching pie charts with dates between 2002 and 2020, students will predict how air quality has changed over the past two decades. They will then use color-coded Air Quality Index signatures to assess the accuracy of their predictions.

Students will identify and describe the relationship between land cover classification and surface temperature as they relate to the urban heat island effect. Students will also describe patterns between population density and the locations of urban heat islands. 

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 will analyze how surface (skin) temperatures vary across a community and determine what factors contribute to this variation. Students will describe how human activity can affect the local environment. 

Students will describe how the spread of COVID-19 is affected by population density and explain why patterns in the spread of COVID-19 are happening over time. 

Students identify patterns and describe the relationship between chlorophyll concentration and incoming shortwave radiation. 

Students watch a short video to gather information about sources of methane emissions and then extend their understanding of these sources to evaluate monthly trends in the Alaska region, ultimately making connections to Earth’s energy budget.

Students will use coloring sheets to create a color coded model of El Niño and analyze it. If the Data Literacy Map Cube is used with this, students will color their models first.