Remote sensing is a general term that describes gathering information about an object or area without physically touching it. Think of a camera capturing an image from a distance. Passive satellite remote sensing is a specific type that utilizes naturally occurring energy sources, such as sunlight or heat, to collect data from the Earth's surface and other planets via spaceborne satellites. This differs from active remote sensing, which emits its own energy, such as a laser, and measures what bounces back (e.g., LiDAR or Radar).
Passive sensors, which are the focus of this guide, rely on the sun's energy. They capture the sunlight reflected off Earth's surface. The information they collect is based on how different materials—like soil, water, and vegetation—absorb, reflect, and emit energy across the electromagnetic spectrum. This is what makes it possible to distinguish between different features on the ground.
Satellites are strategically placed in different orbits to serve various purposes. Orbiting satellites maintain a variety of consistent, repeating paths around the Earth, capturing imagery that provides a near-global perspective. Geostationary satellites orbit at a much higher altitude and match the Earth's rotation, allowing them to provide continuous, real-time monitoring of a specific area, which is essential for tracking short-term phenomena like hurricanes or wildfires.
Satellite imagery is an incredibly versatile data source, used across a wide range of disciplines, from environmental science to urban planning. Its utility stems from four key dimensions: spatial, temporal, spectral, and radiometric resolution. These characteristics, determined by a satellite's orbit and instrument capabilities, dictate the type of information that can be extracted. Because there are inherent trade-offs between these resolutions, it's crucial to consider which combination of factors best fits the requirements of a research question.
USGS Landsat 8 true color and surface temperature visualizations - Okavango Delta, Kalahari Basin, southern Africa
Spatial resolution refers to the smallest feature that can be discerned in an image. It's typically expressed as the dimensions of a single pixel on the ground. For example, an image with a spatial resolution of 30 meters means each pixel represents a 30 x 30 meter square area on Earth's surface. A finer resolution (e.g., 0.5 meters) allows for the identification of smaller objects, such as cars or individual trees, while a coarser resolution (e.g., 1 kilometer) is suitable for large-scale phenomena, like global weather patterns.
What to consider: Is the spatial resolution fine enough to observe the specific features you're studying? For instance, if you're analyzing urban development at the city block level, you need a high-resolution image to see individual buildings and streets.
USDA National Agriculture Imagery Program (NAIP) image showing a portion of northwestern New Mexico in 2018. The image is displayed as a near-infrared (NIR) false-color composite, highlighting active vegetation in red.
Temporal resolution refers to the frequency at which a satellite revisits and collects imagery over the same location. This is often called the return period. A satellite with a high temporal resolution (e.g., daily) can capture rapid changes, while one with a low temporal resolution (e.g., every two weeks) is better for observing long-term trends. The available time range of the imagery is also a key consideration.
What to consider: What time frame and return interval do you need to answer your research question? If you're studying seasonal vegetation change, you might need imagery collected every few weeks throughout the year. If you're analyzing land cover change over decades, a dataset covering a specific time range, like 2000 to 2025, would be necessary.
NASA MODIS Burned Area Product showing the area of the 2024 Texas Panhandle fires
Spectral resolution describes a sensor's ability to capture data across different portions of the electromagnetic (EM) spectrum. Different materials on Earth, like vegetation, water, and soil, reflect and absorb EM energy in unique ways. A sensor with high spectral resolution can capture these subtle differences across multiple, narrow bands of the spectrum. For example, a sensor might have multiple near-infrared (NIR) bands, which can be particularly useful for differentiating between different types of vegetation or assessing plant health.
Example Use: The Normalized Difference Vegetation Index (NDVI) is a common remote sensing application that uses the red and near-infrared bands to monitor plant health. Healthy plants absorb red light and strongly reflect NIR light, so this combination effectively highlights areas of lush vegetation.
Four-year median NDVI image of central Austin Texas (2020-2024) from Landsat 8 imagery
Radiometric resolution is the sensor's ability to detect and distinguish between subtle differences in the intensity of light. This is measured by the number of bits used to store the data for each pixel. A higher radiometric resolution (e.g., 16-bit) can differentiate between more shades of gray or colors than a lower one (e.g., 8-bit), allowing for a more nuanced and accurate representation of the data. This is crucial for applications that require precise measurements of energy.
Example Use: In climate studies, a high radiometric resolution is essential for accurately measuring small changes in temperature, which are represented by subtle variations in emitted thermal energy. This precision is vital for developing accurate climate models and weather forecasts.
Notable satellite imagery programs/platforms that are freely available:
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