Hyperspectral lidar (HSL), an emerging active remote sensing technology, integrates the three-dimensional (3D) spatial detection capability of traditional lidar with the rich spectral information of hyperspectral imaging, addressing the long-standing limitation of separate spatial and spectral information acquisition in conventional remote sensing. Unlike passive hyperspectral imaging (which lacks 3D perception) and single-wavelength lidar (which lacks spectral discrimination), HSL simultaneously captures high-resolution 3D coordinates and spectral reflectance characteristics of targets, generating four-dimensional spatial-spectral point clouds. This unique capability is pivotal for advancing precision applications such as forest resource surveys (quantifying vertical structure and biochemical components), land cover classification (enhancing accuracy via spectral-spatial synergy), urban 3D modeling (distinguishing material properties), and target detection (penetrating obscurations). By enabling “one-stop” acquisition of both physical structure and chemical composition, HSL revolutionizes how we perceive and analyze complex environments, making it indispensable for addressing global challenges like sustainable resource management, and smart urban development.

Over the past two decades, HSL has evolved from dual-wavelength prototypes to sophisticated systems with tens even hundred spectral channels, driven by advancements in supercontinuum laser sources and spectral detection technologies. Key progress includes:

1) System architectures: Two dominant spectral splitting schemes have been developed: spatial splitting (using gratings for simultaneous multi-wavelength detection, suitable for airborne large-area scanning) and wavelength scanning [using acousto-optic tunable filter/liquid crystal tunable filter (AOTF/LCTF) for high spectral resolution, ideal for fine spectral analysis]. Representative systems, such as the 56-channel airborne HSL (Wuhan University) have achieved detection ranges up to 500 m and 101-channel ground-based HSL (Anhui Jianzhu University), and spectral resolution as high as 5 nm.

2) Waveform processing: To extract accurate spatial-spectral information from overlapping echoes, methods like multi-spectral waveform decomposition (MSWD), multi-channel interconnection waveform decomposition (MIWD), and range resolution enhanced method with spectral properties (RREM) have been proposed. These techniques will enhance range resolution for lidar signals by leveraging cross-channel spectral correlations, overcoming the limitations of single-wavelength decomposition.

3) Radiometric correction: Strategies to mitigate distance effect (via piecewise fitting), incidence angle effect (using Lambertian-Beckmann models), and sub-footprint effect (through spectral ratio and area-weighted correction) have been developed, ensuring reliable spectral reflectance retrieval across diverse targets (vegetation, minerals, building materials).

4) Spatial-spectral point cloud applications: Techniques for point cloud generation (enabling true-color imaging without passive data), classification (combining machine/deep learning with spatial-spectral features), and feature extraction (e.g., crop nitrogen content, mineral identification) have been validated, with classification accuracies exceeding 90% in vegetation and mineral scenarios.

HSL has demonstrated significant potential in various applications, including vegetation monitoring, mineral exploration, and urban modeling, by providing detailed spatial and spectral information. However, challenges remain: limited detection range (mostly <100 m for ground systems), slow multi-channel data processing (lagging behind acquisition rates), and high system complexity hindering commercialization. Future research should focus on: 1) System advancement: developing miniaturized, multi-platform (airborne, satellite-borne, underwater) systems via high-power supercontinuum lasers and low-loss spectral splitters to extend detection range and reduce cost; 2) Information processing: enhancing real-time performance through hardware acceleration (FPGA/ASIC) and deep learning-based multi-channel waveform decomposition, and improving radiometric correction for non-Lambertian targets; 3) Application expansion: exploring new frontiers such as defense reconnaissance (obscured target identification) and smart agriculture (3D biochemical mapping), supported by open datasets and standardized processing workflows. As these challenges are addressed, HSL is poised to become a cornerstone technology in high-precision remote sensing, enabling unprecedented insights into Earth systems and beyond.