White-Light Lidar: Combining Geometry and Spectral Information

For decades, lidar has been extraordinarily good at answering one question: where is it in three-dimensional space?

A pulse of laser light leaves a sensor, strikes a surface and returns. Measure the time between those events precisely enough and the result is an accurate three-dimensional representation of terrain, vegetation, buildings and infrastructure.

But today, researchers are increasingly asking more of that returning light.

A recent study led by researchers at Xiamen University, working with the Fujian Ocean Innovation Center, Ocean University of China and Stratospheres Inc., proposes using broadband “white-light” lidar to actively measure ocean color, potentially combining the ranging capability of lidar with spectral information more commonly associated with passive optical remote sensing. The work was published in the Journal of Remote Sensing on June 25, 2026. It remains theoretical. Researchers used Monte Carlo radiative-transfer simulations rather than an operational sensor, including a modeled spaceborne configuration at 500 kilometers altitude.

Still, the experiment points toward a much broader evolution already underway across lidar research: using the characteristics of the returned light to determine not only where a surface or material is located, but what it is, what condition it is in and, eventually, how that condition is changing.

That could significantly expand what a lidar measurement allows us to do.

Bringing an Artificial Sun to Ocean Color

Ocean-color science typically depends on sunlight. Instruments measure light leaving the water at different wavelengths and use its spectral characteristics to estimate properties including chlorophyll concentration, suspended particles and colored dissolved organic matter.

The approach has produced enormously valuable observations of ocean ecosystems, but passive sensing comes with familiar limitations. Sun angle, clouds, glint, and darkness impact the quality, viability, and comparability of data.

The white-light lidar proposed by the research team replaces sunlight with a controllable broadband laser spanning approximately 400 to 720 nanometers. Rather than illuminating the water with a single conventional lidar wavelength, the system would effectively provide its own broad-spectrum light source.

Pulsing that light also retains one of lidar’s defining advantages: range.

The researchers describe the concept as an “artificial sun” that could separate atmospheric returns from signals originating within the water while collecting spectral information comparable to conventional remote-sensing reflectance. Their simulations found mean absolute percentage error below 10% across the tested chlorophyll-a range at 3,000 metres, provided the receiver field of view exceeded 20 milliradians. The collection of multiple-scattered photons emerged as the principal factor controlling accuracy, and clear water and blue-green wavelengths required larger footprints because those photons travel farther and scatter more before returning. However, the model assumed calm seas and optically homogeneous water, leaving substantial work between simulation and an operational instrument.

The study is therefore less interesting as evidence of a finished new sensor than as an example of where active remote sensing is heading.

Conventional bathymetric lidar can tell us where the seafloor is. Oceanographic lidar can profile portions of the water column. Spectral measurements can reveal biological and optical characteristics. Combining those capabilities begins to answer a richer question: what is happening at a particular location and depth?

That information becomes useful when it changes a decision.

Improved observations of phytoplankton and water conditions could help fisheries managers identify emerging ecosystem changes before they show up in catch data. Earlier identification of harmful algal blooms could guide beach closures, aquaculture operations and drinking-water management. Better characterization of suspended material and sediment could direct post-storm hydrographic surveys toward ports and waterways most likely to have changed.

For defense and maritime security, water-column conditions can also influence how acoustic, optical and autonomous systems perform. Better environmental characterization can help determine where another sensor should look, where an autonomous vehicle should operate or where conditions make detection particularly difficult. That does not mean a white-light satellite suddenly makes the ocean transparent, but that another portion of the physical environment becomes measurable and machine-readable.

From Geometry to State: What White-Light Lidar Adds

The same concept is not limited to water.

Researchers have been experimenting with multispectral, hyperspectral and supercontinuum lidar on land for years. These systems measure the response of targets at multiple wavelengths while retaining three-dimensional ranging information.

That creates a fundamentally richer point.

Instead of recording only X, Y and Z coordinates, perhaps accompanied by an intensity value, a future measurement could attach a spectral response to that location. Geometry and material information would originate from the same active observation.

Much of modern remote sensing still depends on assembling an answer from several separate sensors. Lidar supplies structure, multispectral or hyperspectral imagery contributes spectral information, and thermal sensing adds yet another layer. Analysts or algorithms then have to register those datasets in space and time before deciding what they mean.

A spectrally richer lidar return could begin collapsing portions of that workflow into a single measurement.

In forestry, the distinction could move beyond measuring canopy height toward identifying vegetation type and physiological condition. In agriculture, structural differences could be considered alongside spectral indications of crop stress. For infrastructure, a sensor could potentially identify geometric deformation while simultaneously recording changes in the optical characteristics of the material itself.

That could make machine inspection considerably more discriminating.

A bridge, transmission corridor or industrial facility would no longer be represented only by its shape. An automated system could potentially compare both geometry and material signatures against previous observations, flag the portions showing abnormal change and direct human inspection toward the assets most likely to require intervention.

The important product is no longer simply a better point cloud. It is a measurement of state.

Space Adds Time to the Measurement

Spaceborne lidar itself is not new. Scientific missions including ICESat, ICESat-2 and GEDI have demonstrated the value of active laser measurements from orbit, and spaceborne lidar systems have been used for atmospheric, cryospheric, vegetation and elevation science for years.

What is changing is the scale and ambition of spaceborne collection.

Scientific lidar missions have traditionally been optimized around specific measurement objectives, often sampling along orbital tracks rather than attempting comprehensive high-resolution mapping. Advances in laser sources, detectors, spacecraft systems and commercial launch are now pushing the field toward denser observations, broader coverage and more frequent revisit.

That distinction matters because periodic collection fills in a critical next step.

One sufficiently rich lidar observation can describe where something is and perhaps what condition it is in. A second observation can tell us what changed. A sequence of observations can reveal the direction and rate of that change.

That creates a progression from geometry, to state, to trajectory.

Consider an infrastructure asset whose surface characteristics are gradually changing while its geometry begins to deform. Repeated measurements could potentially identify deterioration, quantify its rate and help predict when the asset is likely to cross a maintenance threshold.

In a forest, repeated observations might show a pocket of stress moving across a landscape. Along a coastline, they could measure where sediment is accumulating and how quickly a navigational channel is changing. In agriculture, a time series could distinguish a temporary anomaly from a persistent decline in crop condition.

The question then moves beyond “what changed?” toward “given the direction and rate of change, what is likely to happen next?”

That is especially important for machine analysis. Consistent, georeferenced measurements collected repeatedly create the training and inference environment needed for algorithms to recognize patterns without requiring a human analyst to reconstruct conditions from a collection of mismatched observations.

Watching the Direction of Travel

The Xiamen white-light lidar study is nowhere near delivering that complete capability. It addresses one narrow piece of the problem: whether an active broadband lidar could reproduce a foundational ocean-color measurement without relying on the sun.

But the direction of travel is worth watching.

Lidar began as an exceptionally precise way to measure distance. Increasingly sophisticated wavelengths, waveforms, polarization measurements and spectral techniques are extracting more information from the photons that return.

Put those capabilities in space, repeat the measurement regularly and lidar begins to describe something considerably richer than the shape of the world.

It begins to describe how the physical world is changing.

Learn More at Lidar News: Multispectral Imaging Maps Hidden Stratigraphy