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Abstract
The amount of leaf area and canopy absorbed photosynthetically active radiation (APAR) (often expressed as leaf area index (LAI) and the fraction of APAR (fAPAR)) are important in determining canopy photosynthesis and stomatal conductance rates and, thus, are important for modeling these processes. Inversion of canopy radiative transfer models is a physically-based method of estimating canopy attributes from remotely-sensed bidirectional reflectance factors (BRFs); the need of simple models for this purpose has been suggested in the literature. The objectives of this research are to: (1) develop a simple canopy radiative transfer model, (2) invert the model to retrieve LAI from canopy BRF for various canopies, and (3) estimate fAPAR using the model. A simple model was developed which requires input values of leaf and soil optical properties, leaf angle distribution, leaf spatial distribution parameter, LAI, viewing and illumination geometry and sky diffuse irradiance fraction. Canopy BRFs, fAPAR and input parameters from field experiments in prairie grassland, alfalfa, soybean, and corn under various canopy LAI and illumination and view conditions were used in the analysis. Simulated corn canopy BRFs agreed on average within 0.9% and 1.7% (red and near-infrared (NIR) wavebands, respectively) of observed values. Corn fAPAR values were within $\pm$0.01 fAPAR units on average. Model output compared well with a detailed model for an alfalfa canopy under a single canopy and illumination condition. Model inversions of observed BRFs from all canopies in the study using NIR BRFs and red and NIR BRFs together yielded LAI estimates within $\pm$0.1 LAI units. Inversions using red BRFs alone yielded LAI estimates within 1.2 LAI units. fAPAR estimated using inversion-retrieved LAI values were within 0.05 fAPAR units, on average, of observed values. The model has the potential to be used in an operational setting to estimate LAI and fAPAR from remotely-sensed BRF data in the shortwave spectrum. Suggestions for improving canopy attribute inference from remotely-sensed data using the simple model are presented.
