> For the complete documentation index, see [llms.txt](https://satdocs.hydrosat.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://satdocs.hydrosat.com/explore-thermal.md). # Explore Thermal Thermal imagery is unique. By capturing heat emitted directly by the Earth's surface, it reveals physical processes that drive changes across landscapes. Here, we'll build some intuition about how thermal imaging works and why it's fundamentally distinct from other remote sensing techniques.

Thermal imagery reveals patterns that are invisible to the human eye.
Imagery © 2025 Hydrosat.

## Thermal Imaging 101 Every object with a temperature above absolute zero emits electromagnetic radiation. Hotter objects emit more energy, a relationship described by blackbody radiation curves:

Blackbody radiation provides the physical basis for measuring temperature from space.

Real-world materials aren't perfect blackbodies, but they follow the same fundamental behavior. As temperature increases, the total emitted energy increases. Thermal sensors exploit this relationship by measuring emitted radiation in specific infrared wavelengths. This means that thermal imagery and traditional optical imagery observe fundamentally different things. Optical sensors measure sunlight reflected from the Earth's surface, just like how our eyes see the world. But thermal sensors directly measure that emitted radiation, providing a unique lens into how landscapes store and release heat, rather than just how they look. By extension, this also means that thermal sensors can operate both day and night, enabling continuous observation even after sunset. ## From Radiance to Temperature Thermal sensors don't measure temperature directly. Instead, they measure the amount of infrared radiation that reaches the detector. That measured signal depends not only on the temperature of the Earth's surface, but also on the physical properties of the surface, as well as the atmosphere between the surface and the satellite. Let's break down how we account for those factors to translate measurements into meaningful temperature values. ### The Role of Emissivity Different materials emit thermal radiation with different efficiencies. This is a concept known as emissivity. A perfect blackbody has an emissivity of 1; it emits the maximum possible radiation for its temperature. Real-world materials have lower emissivities, and those emissivities can vary significantly depending on the material type. This means that two surfaces at the same physical temperature can emit very different amounts of thermal radiation. For example, metals are highly reflective, so they absorb less and emit less radiation than vegetation or bare soil does. Without accounting for emissivity, a thermal sensor can't distinguish whether a difference in measured radiation is actually due to temperature or to variability in surface properties. ### The Role of the Atmosphere As emitted radiation from the surface makes its way to the satellite, it must pass through the atmosphere. Along the way, gases like water vapor and carbon dioxide absorb and re-emit part of the thermal signal. So by the time the radiation reaches the detector, it has already been modified by the atmosphere. Atmospheric correction algorithms estimate and remove these effects to better recover the true surface signal. ### Deriving Surface Temperature We've seen that thermal sensors measure emitted infrared radiation, not temperature directly. Since many users require knowledge of Earth's surface temperature (LST), LST retrieval algorithms, like the one implemented by Hydrosat, work backward from radiance. These physics-based models correct for atmospheric effects, account for surface emissivity, and apply the principles of thermal radiation to produce a physically meaningful estimate of LST that can be compared across different locations and over time.

Surface temperature changes dynamically from day to day. Users can leverage this signal to understand how landscapes respond to different environmental conditions.

Unlike many landscape characteristics that change gradually over the course of weeks or months (e.g., vegetation greenness), LST is one of the most dynamic properties of the Earth's surface. Adjacent agricultural fields might differ by several degrees because of differences in vegetation or moisture levels. A single location may experience large temperature swings throughout the day as it absorbs and releases heat. Frequent, high-resolution LST observations capture these dynamics help users identify anomalies, monitor environmental conditions, and better understand the processes driving change across ecosystems.