As hyperspectral LiDAR (HSL) combines the advantages of hyperspectral remote sensing and LiDAR, it has the potential of estimating vegetation biochemical contents at any three-dimensional (3-D) location. We investigate the capability of HSL to monitor leaf chlorophyll and nitrogen contents at a distance of 7.5 m. Using full-waveform LiDAR data obtained by HSL, the performance of the partial least square regression (PLSR) model using three strategies (only wavelength bands, only vegetation indices, and both wavelength bands and vegetation indices) is explored based on the correlation analysis of 16 wavelength bands and 16 vegetation indices. The result shows that the PLSR model can make full use of various wavelength bands and vegetation indices, having a strong ability to monitor leaf chlorophyll and nitrogen contents using full-waveform LiDAR data. The analysis can be applied for predicting leaf biochemical components of other vegetation and provides the basis for monitoring 3-D distribution of biochemical contents using HSL in the future.

Gross primary productivity (GPP) is a significant indicator of terrestrial system carbon cycle and is one of the most important parameters of global climate change. Accurate modeling of GPP is important for understanding the changes of carbon dioxide in the atmosphere. Although many studies have been conducted to explore methods for estimating GPP, large uncertainties still exist among different models. The combination of remote sensing data with flux data is a feasible approach to determine site-based GPP. Using multisource remote sensing data, meteorological data obtained from Google Earth Engine, and GPP estimates from eddy covariance measurement, we present a model, based on the random forest regression (RFR) algorithm, to predict site-scale GPP. First, we train and tune the RFR model for 16 global deciduous broadleaf forest (DBF) flux sites by remote sensing-based drivers. Then we evaluate the RFR model’s performance by predicting GPP for the other eight DBF sites. Our results show that, compared with the MOD17A2H GPP product, this GPP prediction method improves modeling of GPP, with an R² of 0.82 and an RMSE of 1.93  g C m^−  2 d^−  1. Contribution analysis shows that the enhanced vegetation index is the most important driver among input variables. Our study may provide a reliable method to predict GPP in global DBF, which is promising for different-scale evaluations of carbon sequestration.