Based on the physical models of PROSPECT and SAIL, hyperspectral data of different scenes were simulated. According to the simulated data, we built 7 spectral indexes highly linear correlated to vegetation canopy water content, and analyzed the relationship between spectral indexes and canopy water content. Then we built a multiple linear model of canopy water content with the spectral indexes that are highly correlated with FMC (Fuel Moisture Content). At last, using the model, the vegetation canopy water content from Hyperion was obtained. It shows that the spectral indexes: II, NDWI, Ratio1200, Ratio975, WI1, are highly correlated with canopy water content, and the multiple linear model built with them produces an effective result.

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Understory vegetation plays an important role in the structure and function of forest ecosystems. Light detection and ranging (LiDAR) can provide understory information in the form of either point cloud or full-waveform data. Point cloud data have a remarkable ability to represent the three-dimensional structures of vegetation, while full-waveform data contain more detailed information on the interactions between laser pulses and vegetation; both types have been widely used to estimate various forest canopy structural parameters, including leaf area index (LAI). Here, we present a new method for quantifying understory LAI in a temperate forest by combining the advantages of both types of LiDAR data. To achieve this, we first estimated the vertical distribution of the gap probability using point cloud data to automatically determine the height boundary between overstory and understory vegetation at the plot level. We then deconvolved the full-waveform data to remove the blurring effect caused by the system pulse to restore the vertical resolution of the LiDAR system. Subsequently, we decomposed the deconvolved data and integrated the plot-level boundary height to differentiate the waveform components returned from the overstory, understory, and soil layers. Finally, we modified the basic LiDAR equations introducing understory leaf spectral information to quantify the understory LAI. Our results, which were validated against ground-based measurements, show that the new method produced a good estimation of the understory LAI with an R2 of 0.54 and a root-mean-square error (RMSE) of 0.21. Our study demonstrates that the understory LAI can be successfully quantified through the combined use of point cloud and full-waveform LiDAR data.