Steam Digest

Imagine looking down at a forest from space and being able to distinguish its tallest trees, the layers of vegetation beneath the canopy, and the shape of the ground below. Now imagine doing that across a continuous stretch of land rather than collecting narrow strips of measurements separated by large gaps.

That is the challenge NASA is working to address with a new technology called CASALS.

In an announcement published on October 21, 2026, NASA described how its Concurrent Artificially-intelligent Spectrometry Lidar System combines machine learning, specialized laser optics, and sensitive light detectors to improve the way scientists measure Earth’s surface.

The idea is straightforward but technically ambitious: instead of gathering measurements along isolated lines and leaving substantial areas between them unobserved, CASALS is designed to scan a continuous three-dimensional swath of terrain.

The technology could eventually help researchers better understand forests, vegetation structure, land elevation, and other features that influence Earth’s ecosystems. It also illustrates how artificial intelligence is becoming part of the scientific instruments used to observe our planet.

There is an important distinction, however. CASALS has demonstrated its capabilities in airborne testing, but it is not yet an operational satellite instrument providing continuous global maps. NASA and its research partners still have work to do before a space-ready version becomes a reality.

The Problem With Mapping Earth One Line at a Time

Scientists have been using satellites and aircraft to observe Earth’s surface for decades. These measurements support research into forests, glaciers, coastlines, land elevation, and changes in the environment.

But measuring a planet as complex as Earth is not as simple as taking a photograph.

Different instruments collect different kinds of information. Some use cameras to capture reflected sunlight. Others measure radar signals or use laser pulses to determine the distance between a sensor and the surface below.

Laser-based instruments known as lidar systems are particularly useful when researchers need precise information about elevation and three-dimensional structure.

Lidar stands for light detection and ranging. A sensor sends out laser pulses and measures the light that returns after interacting with objects or surfaces. By analyzing the timing and characteristics of those returning signals, scientists can estimate distances and reconstruct details about the environment.

In a forest, for example, laser pulses may interact with the tops of trees, branches, leaves, understory vegetation, and the ground. These measurements can help researchers investigate the vertical structure of a forest rather than treating it as a flat green surface.

The difficulty is coverage.

Traditional space-based lidar instruments often measure narrow tracks along a satellite’s path. Even when those measurements are highly detailed, the ground between the tracks may not be directly measured by the instrument.

Researchers can combine different datasets, use other observation methods, or apply models to estimate missing information. However, these approaches do not make every gap equivalent to a location that has been directly observed.

For scientists studying complicated landscapes, the distinction matters. A narrow strip may capture valuable information about a forest, but it may not reveal how vegetation changes just beyond that strip.

CASALS is being developed to address this limitation by collecting measurements across a wider, continuous area.

What Makes CASALS Different?

The central innovation is the way CASALS directs its laser pulses.

Many conventional lidar systems rely on moving mechanical components, such as mirrors, to steer a laser beam. CASALS instead uses changes in laser wavelength and a specially designed optical grating to direct pulses toward different positions across the instrument’s field of view.

This approach is important because it reduces the need for heavy, moving beam-steering components.

According to NASA, CASALS uses a tunable laser centered at approximately 1,040 nanometers. The instrument rapidly cycles through 256 distinct wavelengths, sending the pulses through an optical component called a transmission grating.

The grating contains a microscopic pattern of ridges. When light passes through it, diffraction causes different wavelengths to travel at slightly different angles.

CASALS uses that physical effect to direct individual pulses toward different locations across the flight path.

Instead of mechanically moving a mirror to point the laser in different directions, the system uses wavelength changes to distribute the pulses spatially.

The resulting measurements can be assembled into a continuous three-dimensional swath.

This design offers several potential advantages. Removing mechanical steering parts can reduce the instrument’s size, weight, and power requirements. Fewer moving components may also simplify some aspects of operating the instrument in the demanding environment of space.

For satellite missions, these characteristics matter. Spacecraft have strict limits on mass, power consumption, and available room. Instruments must also withstand launch forces, temperature changes, and years of operation without the maintenance options available to equipment on Earth.

A compact sensor that collects useful measurements efficiently could make advanced surface mapping more practical for future missions.

Where Artificial Intelligence Enters the Picture

The optical system is only one part of the CASALS concept. Artificial intelligence is another important element.

Machine learning can help scientific instruments process information, identify patterns, and make decisions based on incoming measurements. In an instrument designed for adaptive observation, algorithms can help determine where measurements are most useful and how the system should respond to the information it collects.

This matters because collecting more data is not always the same as collecting better data.

A forest is a complicated environment. Tree heights vary, branches overlap, and different surfaces reflect laser energy in different ways. Some signals may be weak, while others may be difficult to separate from surrounding measurements.

An adaptive instrument could potentially use information about the scene to guide its measurement strategy rather than following a fixed observation pattern in every situation.

The CASALS concept combines this intelligent approach with its wavelength-steering technology to support more complete surface mapping.

It is useful to distinguish this from the familiar idea of AI generating an image from a text prompt. CASALS is designed to measure the physical world using real laser signals. Machine learning supports the observation process; it does not replace the need to collect and validate measurements.

That distinction is especially important in Earth science. Scientists need data that can be checked, compared, and interpreted, not merely a visually convincing representation of a landscape.

As the technology develops, researchers will need to evaluate how well its measurements perform across different terrain types, vegetation conditions, and observation environments.

How Detailed Can the Measurements Be?

CASALS has already moved beyond a purely theoretical design.

In November 2024, an airborne prototype flew aboard NASA’s B200 King Air aircraft. The flight campaign produced science-quality datasets, according to NASA.

The aircraft flew at an altitude of approximately five kilometers, and the instrument demonstrated a horizontal resolution of about 22 centimeters.

That level of detail can help researchers distinguish relatively small features in vegetation and identify subtle changes in the shape of the ground.

To understand why this matters, consider two forests that appear similar in a conventional aerial photograph.

One may contain tall trees with relatively open space beneath the canopy. Another may have a dense understory, multiple vegetation layers, and a different distribution of branches.

A three-dimensional lidar dataset can help scientists examine these structural differences. It can also provide information about the relationship between vegetation and the underlying terrain.

The measurements could support research into forest structure, ecosystem conditions, habitat characteristics, and changes in landscapes over time.

However, horizontal resolution is not the same as complete accuracy in every dimension. The usefulness of a dataset also depends on factors such as measurement uncertainty, signal quality, coverage, and the ability to distinguish returns from vegetation and the ground.

The airborne demonstration is an encouraging technical milestone, but it does not by itself establish how well a future satellite version will perform across the entire planet.

Space-based operation introduces additional challenges, including instrument qualification, spacecraft integration, communications, power management, and processing large volumes of data.

Why Mapping Forests in 3D Matters

Forests are more than collections of trees visible from above. Their structure influences how ecosystems function.

The height and density of vegetation can affect how much sunlight reaches the forest floor, how water moves through an ecosystem, and how habitats are distributed. The relationship between vegetation and terrain can also help researchers understand differences between landscapes.

More complete three-dimensional measurements could improve the information available for ecological models and environmental research.

For example, scientists studying forest structure may want to compare the height distribution of trees across a region or investigate how vegetation varies with elevation.

If measurements cover only narrow tracks, researchers may have less direct information about the landscape between them. A continuous swath could provide a more detailed view of the spatial relationships between neighboring areas.

Such information could contribute to research into forest condition, ecosystem change, and the effects of environmental disturbances.

Lidar observations may also be useful in studies of areas where surface elevation is important, including changing coastlines, icy landscapes, and other regions where detailed topography helps scientists understand physical processes.

The value of CASALS will depend on the quality of the observations it ultimately produces and how well those observations complement other sources of Earth science data.

It is not a replacement for every existing satellite instrument. Different sensors measure different properties, and scientists often combine multiple datasets to understand a complex environmental problem.

The broader opportunity is to make the three-dimensional information available to researchers more complete.

From Airborne Prototype to Space Technology

One of the most important details in NASA’s announcement is what still needs to happen.

The airborne prototype has demonstrated that the basic technology can collect useful measurements. But a successful aircraft flight is not the same as a completed satellite mission.

NASA says the team is working on improvements before developing a space-ready prototype. These include widening the instrument’s measurement swath and adding onboard data-processing software.

A wider swath would allow the instrument to observe a broader area during a pass. Onboard processing could help manage the information collected by the sensor and prepare data for further use.

Both are important steps toward making the technology practical for space-based Earth observation.

The team is also planning another airborne flight campaign through the Airborne Surface, Cryosphere, Ecosystem, and Nearshore Topography, or ASCENT, campaign, scheduled for summer 2027.

Further testing can help researchers assess the instrument’s performance, refine its design, and identify issues that need to be addressed before space deployment.

NASA has not announced that CASALS is already operating in orbit or delivering operational global coverage. Its potential as a space-based mapping system remains a development goal.

That distinction is worth keeping in mind whenever a new scientific technology is described as revolutionary. A successful prototype is an important achievement, but reliable operational systems require additional engineering, testing, and validation.

Could This Technology Help Scientists Understand a Changing Planet?

Earth’s surface is constantly changing.

Forests grow, recover from disturbances, and respond to environmental conditions. Coastlines shift, glaciers change shape, and landscapes are altered by natural processes and human activity.

Understanding these changes requires measurements that are detailed enough to reveal what is happening and consistent enough to support comparisons over time.

A more complete three-dimensional mapping system could help researchers establish better baselines for studying these environments.

Repeated observations may eventually allow scientists to compare changes in vegetation structure or terrain, provided the measurements are sufficiently accurate and collected in a way that supports meaningful comparisons.

The same principle applies to environmental models. Models are only as useful as the observations and assumptions that inform them. More complete measurements can help researchers test their understanding of the physical world and identify where existing knowledge remains uncertain.

CASALS is one example of how improvements in sensors and data processing may strengthen that scientific foundation.

Its eventual contribution will depend on more than the novelty of its laser system. Coverage, accuracy, reliability, data availability, and the ability to integrate its measurements with other scientific records will all influence its practical value.

What CASALS Reveals About the Future of Earth Observation

The next generation of Earth-observation technology will not depend on satellites alone. It will also depend on the instruments they carry, the algorithms that help operate them, and the methods scientists use to turn measurements into reliable knowledge.

CASALS brings these elements together in a compact system that combines laser physics, optical engineering, sensitive detectors, and machine learning.

Its central idea is simple: the more completely scientists can measure the world, the better they can investigate its structure.

That does not mean every gap in an existing map will disappear overnight. Nor does it mean artificial intelligence can automatically solve every challenge in environmental monitoring.

Instead, CASALS represents a promising approach to a specific problem in remote sensing: how to collect detailed three-dimensional measurements across broader areas while keeping the instrument compact and efficient.

The airborne prototype has provided an important demonstration. The next phase will determine how the technology can be improved, how well it performs under additional conditions, and whether it can eventually be adapted for operation in space.

If those steps succeed, future versions could give researchers a more continuous view of forests and terrain, strengthening the data available for understanding Earth’s surface.

The big idea: Artificial intelligence is not just helping scientists interpret observations after they are collected. It is also becoming part of how scientific instruments can make observations in the first place.

And when that intelligence is paired with new ways of measuring the physical world, it may help us see parts of our planet that earlier instruments could only observe in fragments.

References

• NASA Science: NASA’s CASALS Instrument Bridges Gaps in 3D Maps of Earth’s Surface — October 21, 2026.
• NASA Earth Science Technology Office: ESTO News and Technology Updates.
• NASA Earth Science Technology Office: CASALS Project Abstract and Technical Background.
• NASA: Goddard Lidar Team Receives Center Innovation Award for Advancements.