Abstract The vegetation index-based (VI-based) mixture model is widely used to derive green fractional vegetation cover (FVC) from remotely sensed data. Two critical parameters of the model are the vegetation index values of fully-vegetated and bare soil pixels (denoted Vx and Vn hereafter). These are commonly empirically set according to spatial and/or temporal statistics. The uncertainty and difficulty of accurately determining Vx and Vn in many ecosystems limits the accuracy of resultant FVC estimates and hence reduces the utility of VI-based mixture model for FVC estimation. Here, an improved method called MultiVI is developed to quantitatively estimate Vx and Vn from angular VI acquired at two viewing angles. The directional VI is calculated from the MODIS Bidirectional Reflectance Distribution Function (BRDF)/Albedo product (MCD43A1) data. The results of simulated evaluation with 10% added noise show that the root mean square deviation (RMSD) of FVC is approximately 0.1 (the valid FVC range is [0, 1]). Direct evaluation against 34 globally-distributed FVC measurements from VAlidation of Land European Remote sensing Instruments (VALERI) sites during 2000 to 2014 demonstrated that the accuracy of MultiVI FVC (R2 = 0.866, RMSD = 0.092) exceeds than from SPOT/VEGETATION bioGEOphysical product version 1 (GEOV1) FVC (R2 = 0.795, RMSD = 0.159). MultiVI FVC also exhibits higher correlation to the VALERI reference FVC than does the MODIS fraction of photosynthetically active radiation product (MCD15A2H; R2 is 0.696). A key advantage of the MultiVI method is obvious in areas where fully-vegetated and/or bare soil pixels do not exist in moderate-coarse spatial resolution imagery when compared to the conventional VI-based mixture modelling. The MultiVI method can be flexibly implemented over regional or global scales to monitor FVC, with maps of Vx and Vn generated as two important byproducts.

中文翻译：

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基于多角度植被指数的植被覆盖度估计

摘要 基于植被指数（VI-based）的混合模型被广泛用于从遥感数据中导出绿色植被覆盖率（FVC）。该模型的两个关键参数是完全植被和裸土像素的植被指数值（以下表示为 Vx 和 Vn）。这些通常是根据空间和/或时间统计经验设置的。在许多生态系统中，准确确定 Vx 和 Vn 的不确定性和难度限制了所得 FVC 估计的准确性，因此降低了基于 VI 的混合模型对 FVC 估计的效用。在这里，开发了一种称为 MultiVI 的改进方法，用于根据在两个视角获得的角度 VI 定量估计 Vx 和 Vn。方向 VI 是根据 MODIS 双向反射分布函数 (BRDF)/反照率积 (MCD43A1) 数据计算得出的。添加 10% 噪声的模拟评估结果表明，FVC 的均方根偏差 (RMSD) 约为 0.1（有效 FVC 范围为 [0, 1]）。对 2000 年至 2014 年期间欧洲陆地遥感仪器 (VALERI) 站点验证的 34 个全球分布的 FVC 测量值的直接评估表明，MultiVI FVC（R2 = 0.866，RMSD = 0.092）的准确性超过了 SPOT/VEGETATION 生物地球物理产品版本1 (GEOV1) FVC（R2 = 0.795，RMSD = 0.159）。MultiVI FVC 与 VALERI 参考 FVC 的相关性也比光合有效辐射产物的 MODIS 分数更高（MCD15A2H；R2 为 0.696）。与传统的基于 VI 的混合建模相比，MultiVI 方法的一个关键优势在中等粗糙空间分辨率图像中不存在完全植被和/或裸土像素的区域中是显而易见的。MultiVI 方法可以在区域或全球范围内灵活实施以监测 FVC，生成的 Vx 和 Vn 地图作为两个重要的副产品。