Understand thermal imagery and how to apply it.
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.
Every object with a temperature above absolute zero emits electromagnetic radiation. Hotter objects emit more energy, a relationship described by blackbody radiation curves:
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.
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.
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.
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.
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.
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.
Now that you've got the basics down, let's look at how Hydrosat's data products fit in.
Hydrosat's data processing pipeline translates measurements from our satellites into progressively higher-level data products, ranging from raw sensor observations to analysis-ready surface temperature.
The bottom line is that every pixel in a thermal image tells a story, but the context for that story depends on exactly which data product you're looking at.
If you're using the Level-1A data product, you're looking at the closest representation of what the satellite saw.
Level-1A pixels are at-sensor radiance values in units of watts per square meter per steradian per micrometer (W/m²/sr/µm). You can think of this as the uncorrected thermal radiation that reached the detector after traveling through the atmosphere.
Radiance data is a good choice if you're interested in applying your own higher-level processing or developing advanced workflows. It's not the best choice if you want to dive right into analyzing conditions at the land surface.
The Level-1B brightness temperature product is still a direct representation of what the satellite observed, but it's a little more intuitive to work with than Level-1A radiance.
Pixel values represent the apparent temperature in units Kelvin based solely on the radiation measured by the satellite. In other words, this is the temperature a blackbody would need to have in order to emit that amount of radiation.
You might use brightness temperature if you want an intuitive thermal measurement that avoids additional assumptions and uncertainties introduced during Level-2 processing.
The Level-2 data product is our best estimate of what's happening at the Earth's surface, after accounting for emissivity and the atmosphere.
Each pixel value is a temperature in units Kelvin that describes how hot the Earth's surface was—including vegetation, soil, water, pavement, and other surfaces—at the moment the satellite passed overhead.
If you're interested in monitoring something like urban heat, vegetation dynamics, or drought, surface temperature will give you the most direct window into that process.
