Science Olympiad Remote Sensing: What to Study and How to Prepare

Last updated: October 31, 2026

Science Olympiad Remote Sensing is a study event in both Division B (middle school) and Division C (high school) for the 2027 season. You interpret satellite imagery, data and maps to answer questions about Earth. In Division B, the focus is the basic principles of remote sensing and Earth observation. In Division C, you use imagery, data and computational skills to work on climate change processes in the Earth system. This guide explains what to study, the physics behind every satellite image, and how to practice with real data.

If you enjoy finding the story hidden in a satellite image, you already think like a researcher. Take one question about our planet further, one-on-one with a PhD mentor. See if you qualify for the STEM Research Program →

What is Remote Sensing?

Remote sensing means measuring something without touching it, usually from aircraft or satellites. Every satellite image is a measurement of energy: sunlight reflected by the surface, or heat given off by the Earth itself.

Science Olympiad's official 2027 descriptions differ by division:

Division B: "Participants will demonstrate an understanding of the basic principles of remote sensing and interpret imagery, data, and maps related to Earth observation."
Division C: "Participants will use remote sensing imagery, data, and computational process skills to complete tasks related to climate change processes in the Earth system."

The two divisions have different topics, so make sure your study materials match your division. Read your division's rules first. They list the exact topics and what you may bring to the event. Download them free from the 2027 Rules page.

Remote Sensing is supported by the IEEE Geoscience and Remote Sensing Society (GRSS), and the soinc.org event page links to its educational resources.

The physics behind every satellite image

Both divisions rest on the same foundation: how electromagnetic energy travels, interacts with Earth and reaches a sensor. Master these five ideas first.

1. The electromagnetic spectrum

Electromagnetic energy spans a broad spectrum from long radio waves to short gamma rays. When it meets an object, it can be transmitted, reflected, absorbed, refracted or scattered, depending on what the object is made of (NASA, Tour of the Electromagnetic Spectrum).

For remote sensing, the most useful regions are:

Visible light: what our eyes see, and what a normal photo records.
Near-infrared: reflected strongly by healthy leaves. NASA notes that scientists use reflected near-infrared to study the health of vegetation and soil composition.
Thermal infrared: heat given off by the Earth's surface and atmosphere.
Microwaves: these pass through clouds, dust, smoke, snow and rain, so microwave instruments can observe the surface in any weather.

2. Passive and active sensors

According to the Canada Centre for Remote Sensing's free textbook, Fundamentals of Remote Sensing:

Passive sensors measure energy that is naturally available. They can record reflected sunlight only when the Sun lights the surface, but they can record emitted heat, such as thermal infrared, day or night.
Active sensors provide their own energy. They send out radiation and measure what returns from the target, so they can collect measurements at any time. Radar is the classic example.

3. The four kinds of resolution

The same textbook defines four resolutions. Know each one and the trade-offs between them:

Spatial resolution: the size of the smallest feature the sensor can detect.
Spectral resolution: how narrow each wavelength band is. Finer spectral resolution lets you separate materials that look alike.
Radiometric resolution: how small a difference in energy the sensor can detect.
Temporal resolution: how often the sensor views the same place, set by its orbit and revisit period.

4. Orbits

Satellite orbits control what a sensor can see and how often:

Geostationary orbits, at about 36,000 km, match Earth's rotation, so the satellite watches the same area continuously. Weather satellites often use them.
Near-polar orbits pass close to both poles. Many are also sun-synchronous, so they cross each area at the same local time of day, which keeps lighting consistent between images.

5. Spectral signatures and false color

Different surfaces reflect different wavelengths in different amounts. That pattern is a spectral signature, and it is how you identify features in an image.

The most tested example is vegetation. Chlorophyll strongly absorbs visible light for photosynthesis, while the cell structure of leaves strongly reflects near-infrared (NASA Earth Observatory, Measuring Vegetation). Soil and water, by contrast, reflect red and near-infrared about equally.

This is also why many images look "wrong" at first. In a standard false-color image, near-infrared is shown as red. Healthy vegetation, which reflects a lot of near-infrared, therefore appears bright red. If you can read a false-color image quickly, you have one of the core skills of this event.

Satellite data is how scientists track drought, forests and ice in real time. Imagine asking your own question of that data, and answering it with a PhD mentor beside you. See if you qualify for the STEM Research Program →

Calculating NDVI: the vegetation index

The Normalized Difference Vegetation Index (NDVI) turns the vegetation signature into a single number. NASA Earth Observatory gives the formula:

NDVI = (NIR − VIS) ÷ (NIR + VIS)

NIR is the near-infrared reflectance and VIS is the visible (usually red) reflectance. NDVI always falls between −1 and +1.

Two worked examples:

Dense forest: NIR = 0.50, red = 0.08. NDVI = 0.42 ÷ 0.58 ≈ 0.72.
Sparse grassland: NIR = 0.30, red = 0.20. NDVI = 0.10 ÷ 0.50 = 0.20.

NASA gives these typical ranges: 0.1 and below for barren rock, sand or snow; 0.2 to 0.3 for shrub and grassland; and 0.6 to 0.8 for temperate and tropical rainforests. Comparing one month's NDVI with the long-term average for the same month shows whether vegetation is healthier or more stressed than normal, which is how satellites help monitor drought.

Practice these calculations until they are fast. "Computational process skills" are part of the official Division C description.

Division C: climate processes and Earth's energy balance

Division C applies remote sensing to climate change. The central idea is Earth's energy budget, explained in NASA Earth Observatory's Climate and Earth's Energy Budget:

Averaged over the whole planet, sunlight at the top of the atmosphere is about 340 watts per square meter.
About 29% of that sunlight is reflected back to space by clouds, particles in the air and bright surfaces such as sea ice and snow.
When incoming solar energy equals the heat Earth radiates to space, Earth is in radiative equilibrium, and global temperature is relatively stable. Anything that changes the incoming or outgoing energy disturbs that balance, and global temperature rises or falls in response.

This connects directly to satellite data. Satellites measure reflected sunlight and emitted heat, so they can track the quantities in the energy budget: cloud cover, ice extent, surface temperature and more.

Science Olympiad's Remote Sensing page links a free content introduction written for the 2026 Division C event. It covers instrumentation and physics (spectroscopy, blackbody radiation, orbits and resolution), image interpretation (such as sea surface temperature and atmospheric carbon dioxide), climate processes (atmospheric gases, El Niño–Southern Oscillation, the carbon and nitrogen cycles) and energy balance modeling. It is a strong starting map of the field. Check every topic against the 2027 rules, because topics can change from season to season.

Middle school parent? If your child loses an evening exploring satellite maps, that curiosity can become a real research project. See if your child qualifies for the STEM Explorers Program →

How to prepare, step by step

Read your division's rules and turn the topic list into a checklist.
Learn the foundations in this guide first. The free Fundamentals of Remote Sensing textbook and NASA's Tour of the Electromagnetic Spectrum cover them well.
Split the topics with your partner. One of you can lead on physics and sensors, the other on Earth systems and image interpretation. Then teach each other.
Work with real imagery. The soinc.org event page links free data platforms, including USGS EarthExplorer, the Landsat image gallery, Esri Living Atlas and the Copernicus Data Space. Find an area you know, and compare true-color and false-color views of it.
Take practice tests under time pressure. The soinc.org Remote Sensing page links practice tests from many invitational tournaments in recent seasons. Mark every question you miss and add the answer to your notes.
Build your notes to the rules. If the rules allow notes or reference materials, prepare them exactly as allowed. Include formulas, typical NDVI and reflectance values, orbit facts and labeled example images.
Drill calculations. NDVI, unit conversions, energy budget percentages and resolution questions should become automatic.

The soinc.org page also links the USGS Remote Sensing Classroom, NASA ARSET's Fundamentals of Remote Sensing training and IEEE GRSS resources. The Science Olympiad store also offers a Remote Sensing starter pack and national test packets.

Where Remote Sensing leads

Remote sensing is a working tool in climate science, agriculture, forestry, disaster response, oceanography and urban planning. Science Olympiad's event page links career guides for remote sensing scientists and technologists and for geographers.

It is also one of the most accessible paths into real research. Decades of satellite data are free and public, so a high school student can ask a real question, such as how a city's green space has changed over 20 years, and answer it with real data. For help getting started, see how to choose a research topic in high school and our list of research topics for high school students. For the full picture of the competition, read our Science Olympiad guide.

Medals show what you know about the planet. A research paper built on satellite data shows what you discovered. See if you qualify for the STEM Research Program →

Frequently asked questions

What is Remote Sensing in Science Olympiad? A study event in which you interpret satellite imagery, data and maps. Division B focuses on the basic principles of remote sensing and Earth observation; Division C applies imagery, data and computation to climate change processes.

Is Remote Sensing a 2027 Science Olympiad event? Yes. Remote Sensing is on the official 2027 event lists for both Division B and Division C.

What is the difference between Remote Sensing B and C? They have different official descriptions and different topics. Division B covers basic principles and interpreting Earth observation imagery. Division C covers climate change processes using imagery, data and computational skills.

What should I study first for Remote Sensing? The electromagnetic spectrum, passive and active sensors, the four kinds of resolution, orbits and spectral signatures. Then move to your division's specific topics in the rules.

How do you calculate NDVI? NDVI = (NIR − VIS) ÷ (NIR + VIS), where NIR is near-infrared reflectance and VIS is visible (usually red) reflectance. Dense healthy vegetation gives high values; NASA gives 0.1 and below for barren rock, sand or snow.

Why does vegetation look red in satellite images? In a standard false-color image, near-infrared is displayed as red. Healthy vegetation reflects near-infrared strongly, so it appears bright red.

Where can I find Remote Sensing practice tests? The Science Olympiad Remote Sensing page links practice tests from invitational tournaments in recent seasons.

Sources

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