研究目的
To select wavelengths for the multispectral lidar (MSL) system through the radiative transfer model PROSPECT for estimating leaf chlorophyll and water contents, aiming to maximize the potential of the MSL system in vegetation monitoring.
研究成果
The study demonstrated the potential of the MSL system in vegetation monitoring by selecting a combination of five wavelengths (680, 716, 1104, 1882, and 1920 nm) for estimating leaf chlorophyll and water contents through the PROSPECT model. The selected wavelengths were validated using both synthetic and experimental datasets, showing high accuracy in retrieval. This study can serve as a guide in the design of new MSL systems for vegetation detection.
研究不足
The study is limited by the calibration error of the PROSPECT model and the assumption of several stacking homogeneous layers in a leaf, which does not apply to conifer needle leaves. Additionally, noise was not considered in analyzing the R2 of the simulated hyperspectral reflectance.
1:Experimental Design and Method Selection:
The study proposed to select wavelengths through the radiative transfer model PROSPECT. A five-wavelength combination was established to estimate leaf chlorophyll and water contents.
2:Sample Selection and Data Sources:
Two synthetic datasets were simulated by running the PROSPECT-4, 5 and D model in the forward mode. Three public experimental datasets (ANGERS, LOPEX, and JR) were used for validation.
3:List of Experimental Equipment and Materials:
The study utilized the PROSPECT model for simulation and validation.
4:Experimental Procedures and Operational Workflow:
Sensitivity analysis and correlation analysis were conducted to identify the most significant wavelengths. Model inversion was performed to test the performance of different wavelength combinations.
5:Data Analysis Methods:
The performance of model inversion was assessed using the coefficient of determination (R2), the root-mean-square error (RMSE), and the normalized root-mean-square error (NRMSE).
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