Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method
2017
Song, Wanjuan | Mu, Xihan | Ruan, Gaiyan | Gao, Zhan | Li, Linyuan | Yan, Guangjian
Normalized difference vegetation index (NDVI) of highly dense vegetation (NDVIᵥ) and bare soil (NDVIₛ), identified as the key parameters for Fractional Vegetation Cover (FVC) estimation, are usually obtained with empirical statistical methods However, it is often difficult to obtain reasonable values of NDVIᵥ and NDVIₛ at a coarse resolution (e.g., 1km), or in arid, semiarid, and evergreen areas. The uncertainty of estimated NDVIₛ and NDVIᵥ can cause substantial errors in FVC estimations when a simple linear mixture model is used. To address this problem, this paper proposes a physically based method. The leaf area index (LAI) and directional NDVI are introduced in a gap fraction model and a linear mixture model for FVC estimation to calculate NDVIᵥ and NDVIₛ. The model incorporates the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) model parameters product (MCD43B1) and LAI product, which are convenient to acquire. Two types of evaluation experiments are designed 1) with data simulated by a canopy radiative transfer model and 2) with satellite observations. The root-mean-square deviation (RMSD) for simulated data is less than 0.117, depending on the type of noise added on the data. In the real data experiment, the RMSD for cropland is 0.127, for grassland is 0.075, and for forest is 0.107. The experimental areas respectively lack fully vegetated and non-vegetated pixels at 1km resolution. Consequently, a relatively large uncertainty is found while using the statistical methods and the RMSD ranges from 0.110 to 0.363 based on the real data. The proposed method is convenient to produce NDVIᵥ and NDVIₛ maps for FVC estimation on regional and global scales.
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