Use the Data Literacy Cube to guide students’ exploration of data to enrich their observations and inferences.  This is a flexible resource that may be used with a variety of graphical representations of data.  This activity requires a graph for students to evaluate.   Fo

This NASA visualization shows sea surface salinity observations (September 2011-September 2014). Students review the video and answer questions.

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

In this activity, students explore three indicators of drought are: soil moisture, lack of precipitation, and decreased streamflows. Students investigate each of these parameters develop a sense for the effects of drought on land.

Students will examine a 2014-2015 El Niño Southern Oscillation (ENSO) event to identify relationships among sea surface height, sea surface temperature, precipitation, and wind vectors.

This mini lesson focuses on the 2015-2016 El Niño event and how its weather conditions triggered regional disease outbreaks throughout the world. Students will review a NASA article and watch the associated video to use as a tool to compare with maps related to 2015-2016 rainfall and elevated disease risk, and answer the questions.

This graphic organizer may be used to help students analyze the processes and components of Earth System phenomena.

In this activity, students investigate three different soil samples with varying moisture content. They use a soil moisture probe to determine the percentage (by volume) of water in each of the soil samples.

This mini-lesson guides students' observations of soil moisture anomalies (how much the moisture content was above or below the norm) for the continental US in May 2018.

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.