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Remote Sensing: Overview
Remote sensing is the act of collecting data about an object without physically contacting the object. For example, scientists can determine the landscape (hills and valleys) of the ocean floor without walking on it. To do this they send out sound waves from a ship. The sound waves reflect off the ocean floor and travel back to the ship. The scientists examine the sound waves' echo to determine the landscape of the ocean floor below. This method of remote sensing is called SONAR (SOund NAvigation Ranging).

A similar method of remote sensing is called RADAR (RAdio Detection And Ranging). While SONAR uses the reflection of sound waves to remotely sense objects, RADAR relies on the reflection of electromagnetic waves. This technology is used to track the movement of many things in Earth's atmosphere including airplanes and precipitation (rain, snow, etc.).

Another form of remote sensing is satellite imagery. Some satellites take "pictures" of the amount of infrared and visible light Earth gives off or reflects. Scientists analyze these images to estimate things such as the pattern of vegetation or clouds covering the planet. Satellite images are an important tool used by scientists to keep track of what is going on around the world. 

Remotely sensed data is especially useful to meteorologists. Meteorologists are scientists who study the weather--the temperature, amount of precipitation, wind, etc.--of an area. Meteorologists study satellite images to determine cloud cover and water vapor in Earth's atmosphere. They use RADAR to remotely sense precipitation from the planet's surface.  Meteorologists use remotely sensed data to see current weather patterns and predict the way weather may change in the near future. The U.S. Weather Service: Interactive Information Network provides access to RADAR, satellite, and other weather-related images.

The long-term pattern of weather that characterizes a region is called climate. Scientists who study climate are called climatologists. Climatologists examine weather patterns and other data from the past in order to make predictions about Earth's overall climate in the future. They make such predictions by creating climate models

Climate models are sets of mathematical equations. These mathematical equations describe the relationships among many atmospheric properties, or variables. Such properties include wind speed, air pressure, temperature, precipitation, and the concentrations of many gases in the air. Climate models allow scientists to predict how a change in each variable will affect the other variables and ultimately the global climate on Earth. 

For example, climatologists know that if carbon dioxide (CO2) in the air increases then the average global air temperature will increase. Climatologists have created a climate model to represent this relationship. They can enter a value for the amount of CO2 in the air into the mathematical equation of the model. By solving the equation, they can estimate the resulting air temperature. This is very useful in predicting the future climate of Earth. A simple model of the relationship between CO2 and temperature is provided in this module.

Many scientists have already predicted the global climate of our planet will be warmer in the future. This is based on evidence that concentrations of CO2 in the air have been, and will continue to be, increasing. Such models are useful for predicting general global climate trends. However, one must keep in mind that estimates of climate change for a specific area are less exact.

One must also remember that the above climate model is an over-simplified example. Climate models describe the relationships among many variables. Each of these variables affects the others and thus affects the climate. For this reason, most climate models are sets of very complex equations with many variables examined at the same time. 

The Carbon Dioxide, Temperature, Precipitation, and Wheat pages of this puzzle piece all contain observed and predicted data. Some of the observed data may have been collected through direct contact with the source, while other observed data may have been remotely sensed. The predicted data is based on trends or patterns in the observed data. Examine the predicted changes in atmospheric carbon dioxide and consider how such changes may impact global temperatures and precipitation patterns. Also consider how changes in temperature and precipitation may, in turn, play a part in changes in Wheat.


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