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.

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

Exploring salinity patterns is a great way to better understand the relationships between the water cycle, ocean circulation, and climate. In this mini lesson, students analyze sea surface salinity mapped plots created from the Earth System Data Explorer, paired with questions (and answers) from the Aquarius Mission. Credit: Aquarius Education

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 use in-water profiles of historical ocean data to analyze how sea surface salinity varies with depth. 

Students review an animation of monthly average wind speed at 10 meters above the ocean surface for our global ocean to analyze the relationship between winds and ocean surface currents.

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.

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.