NASA's CASALS Lidar System Demonstrates Continuous 3D Earth Surface Mapping

Current space-based laser instruments measure Earth's forests and terrain one narrow line at a time, leaving enormous gaps that include millions of acres of land scientists never directly measure. NASA's newly demonstrated CASALS instrument replaces that fragmented approach with a continuous three-dimensional sweep.[1][4]

Legacy orbital lidars, such as those currently tracking global ice elevation and forest canopy heights, rely on mechanical mirrors or overlapping footprints to build their topographical maps. This method inherently creates isolated profiles separated by vast, unmapped distances across the globe, severely limiting the resolution of the resulting datasets.[1][2]

Because of these blind spots, researchers are forced to mathematically interpolate the elevation and vegetation structure in the spaces between tracks. This guesswork introduces significant uncertainty into critical climate models, particularly when assessing dense, multi-layered forests or rapidly changing coastal environments where topography varies drastically over short distances.[1]

Wavelength Steering

The Concurrent Artificially-intelligent Spectrometry Lidar System (CASALS) abandons moving parts entirely to solve this coverage gap. Developed at NASA's Goddard Space Flight Center, the spaceflight-prototype instrument steers laser pulses using wavelength diffraction rather than heavy, failure-prone motorized mirrors.[1][4]

The system centers on a fast-pulsing, tunable seed laser operating at 1040 nanometers. During operation, this laser rapidly cycles through 256 unique wavelengths as it pulses through an optical component featuring a microscopic pattern of ridges known as a transmission grating.[1][3]

As the light passes through this grating, the physics of diffraction takes over. Because light bends at slightly different angles depending on its exact wavelength, the grating naturally steers each of the 256 pulses into a distinct, predictable position across the flight path.[1][4]

This non-mechanical dispersion paints a continuous three-dimensional swath of the ground below. Stripping away the motorized components drastically reduces the instrument's size, weight, and power requirements, making the hardware significantly more reliable for the harsh environment of orbit.[1]

Photon-Starved Environments

To capture the returning light, CASALS employs a highly sensitive detector array that operates at 80 percent quantum efficiency. This metric indicates that the sensor converts nearly all incoming photons into an electrical signal, wasting almost none of the reflected light.[1][2]

High quantum efficiency is critical for spaceborne lidars, which operate in photon-starved conditions where return signals are exceptionally weak. The heightened sensitivity allows CASALS to collect useful topographical measurements from dark surfaces or through dense, multi-layered forest canopies that absorb most light.[1][4]

The technology proved its operational capabilities during an airborne campaign aboard a NASA B200 King Air aircraft. Flying over Delaware's Great Cypress Swamp on November 12, 2024, the prototype produced science-quality datasets on its very first attempt.[1][3]

During these flights, the instrument achieved a 22-centimeter horizontal resolution from an altitude of five kilometers. The resulting three-dimensional renderings clearly distinguished individual tree branches, near-ground vegetation, and subtle changes in the underlying ground elevation beneath the swamp's canopy.[1][4]

Autonomous Orbital Decisions

The visual data output from the airborne tests mapped tree canopy tops in bright green, the solid ground in purple, and the near-ground vegetation in blue. This unprecedented level of detail allows scientists to assess the structural complexity of an entire ecosystem in a single, continuous pass.[1]

"We're able to see really fine details of vegetation structure and changes in the topography of the surface," said John Yorks, physical research scientist at Goddard and principal investigator for the CASALS project.[1][2]

"I'm very impressed with Guang Yang, Jeffrey Chen, and all our engineers, and how well CASALS performed," Yorks added. "This worked really well from the first time we flew it, which is pretty impressive."[1]

Beyond its optical hardware, the system integrates onboard artificial intelligence to process hyperspectral imaging data autonomously. Machine learning algorithms allow the instrument to optimize its own lidar beam targeting in real time, rather than waiting for delayed commands from ground controllers millions of miles away.[1][4]

Expanding Mission Profiles

This computational capacity also manages data volume reduction through spectral band subsetting. By classifying images and compressing products directly on the platform, CASALS can efficiently transmit high-resolution, three-dimensional maps back to Earth without overwhelming the limited orbital communication bandwidths available to scientific satellites.[1]

The continuous swath mapping provided by CASALS directly addresses targeted observables outlined for future Earth science missions. The data will improve models of the global carbon cycle by precisely quantifying above-ground biomass and tracking coastal topography changes over time with unprecedented accuracy.[1][2]

The engineering team is now working to widen the instrument's swath and refine its onboard processing software. A subsequent airborne testing phase, the Airborne Surface, Cryosphere, Ecosystem, and Nearshore Topography (ASCENT) campaign, is scheduled for the summer of 2027.[1][4]

"The team's really done a great job of sticking together and keeping on task and on schedule," Yorks said. "We are being funded to optimize and improve the performance, and we're also then looking at ways to take the technologies used in the airborne instrument and mature them."[1]

Transitioning the prototype into a fully realized space mission remains the primary objective. If successful, CASALS will become the first spaceborne lidar capable of mapping planetary surfaces as continuous three-dimensional swaths, fundamentally changing how scientists measure topographical evolution.[1][3]