The detection of vertical heterogeneity in vegetation has attracted an increasing attention as it has a great significance for precise agriculture. The hyperspectral light detection and ranging (LiDAR) (HSL) can obtain the spectral and spatial information simultaneously. However, its ability to monitor the vertical distribution of biochemical parameters in plants has not been fully explored. In this article, the applicability of empirical ratio and normalized spectral indices for HSL channels in chlorophyll (Chl) detection was investigated using three data sets: the PROSPECT-5 synthetic data set, the ANGERS public data set, and an HSL-measured data set. A linear regression model of the best performing index against measured Chl values was constructed so as to build 3-D Chl point clouds of maize. The performance of HSL in Chl detection at the upper and lower layers was also tested based on the selected spectral index. The result showed that the CIred edge index was most compatible with the HSL channels. The estimated Chl concentrations of the upper and lower layers showed the close relationships with HSL measurements (R² = 0.73 and 0.91, respectively). The vertical Chl profiles in maize were also presented, indicating that the HSL system has a strong ability to monitor the vertical distribution of maize Chl concentrations. This article provides a basis for the vertical detection of vegetation biochemical parameters directly from HSL measurements.

Advanced remote sensing techniques for estimating crop nitrogen (N) are crucial for optimizing N fertilizer management. Hyperspectral LiDAR (HSL) data, with both spectral and spatial information of the targets, can extract more plant properties than traditional LiDAR and hyperspectral imaging systems. In this study, we tested the ability of HSL in terms of estimating maize N concentration at the leaf-level by using spectral indices and partial least squares regression (PLSR) methods. Subsequently, the N estimation was scaled up to the plant-level based on HSL point clouds. Biomass, extracted with structural proxies, was utilized to exhibit its supplemental effect on N concentration. The results show that HSL has the ability to extract N concentrations at both the leaf-level and the canopy-level, and PLSR showed better performance (R² > 0.6) than the single spectral index (R² > 0.4). In comparison to the stem height and maximum canopy width, the plant height had the strongest ability (R² = 0.88) to estimate biomass. Future research should utilize larger datasets to test the viability of using HSL to monitor the N concentration of crops, which is beneficial for precision agriculture.

Hyperspectral light detection and ranging (LiDAR) (HSL) can be used to acquire backscattered full-waveform data with abundant spectral information, providing a new technique for the remote sensing of plant 3-D properties monitoring. HSL 3-D point cloud of two Kniphofia uvaria plants was acquired by HSL scanning and data processing. Then, chlorophyll concentration could be retrieved at any 3-D position using a constructed partial least squares regression (PLSR) model. At the same time, the vegetation canopy structural parameters (canopy height, maximum canopy width, and projected canopy area) were retrieved based on the geometric information of the HSL data. The results of this study showed that the HSL system could be used to realize the simultaneous extraction of both the 3-D biochemical and structural parameters, which has great potential in the field of quantitative remote sensing.

High-throughput measurement of plant photosynthesis ability presents a challenge for the breeding process aimed to improve crop yield. As a novel technique, hyperspectral lidar (HSL) has the potential to characterize the spatial distribution of plant photosynthesis traits under less confounding factors. In this paper, HSL reflectance spectra of maize leaves were utilized for estimating the maximal velocity of Rubisco carboxylation (V_cmax) and maximum rate of electron transport at a specific light intensity (J) based on both reflectance-based and trait-based methods, and the results were compared with the commercial Analytical Spectral Devices (ASD) system. A linear combination of the Lambertian model and the Beckmann law was conducted to eliminate the angle effect of the maize point cloud. The results showed that the reflectance-based method (R² ≥ 0.42, RMSE ≤ 28.1 for J and ≤4.32 for V_cmax) performed better than the trait-based method (R² ≥ 0.31, RMSE ≤ 33.7 for J and ≤5.17 for V_cmax), where the estimating accuracy of ASD was higher than that of HSL. The Lambertian–Beckmann model performed well (R² ranging from 0.74 to 0.92) for correcting the incident angle at different wavelength bands, so the spatial distribution of photosynthesis traits of two maize plants was visually displayed. This study provides the basis for the further application of HSL in high-throughput measurements of plant photosynthesis.

Accurate segmentation of individual leaves of sugar beet plants is of great significance for obtaining the leaf-related phenotypic data. This paper developed a method to segment the point clouds of sugar beet plants to obtain high-quality segmentation results of individual leaves. Firstly, we used the SFM algorithm to reconstruct the 3D point clouds from multi-view 2D images and obtained the sugar beet plant point clouds after preprocessing. We then segmented them using the multiscale tensor voting method (MSTVM)-based region-growing algorithm, resulting in independent leaves and overlapping leaves. Finally, we used the surface boundary filter (SBF) method to segment overlapping leaves and obtained all leaves of the whole plant. Segmentation results of plants with different complexities of leaf arrangement were evaluated using the manually segmented leaf point clouds as benchmarks. Our results suggested that the proposed method can effectively segment the 3D point cloud of individual leaves for field grown sugar beet plants. The leaf length and leaf area of the segmented leaf point clouds were calculated and compared with observations. The calculated leaf length and leaf area were highly correlated with the observations with R² (0.80–0.82). It was concluded that the MSTVM-based region-growing algorithm combined with SBF can be used as a basic segmentation step for high-throughput plant phenotypic data extraction of field sugar beet plants.