The fraction of green vegetation is a widely-used indicator of vegetation abundance at regional and/or global scales. The pixel mixture model, especially the dimidiate pixel model (DPM, also referred to as two-endmember model) based on the normalized difference vegetation index (NDVI), plays an important role in the accurate estimation of fractional vegetation cover (FVC) remote sensing. The two components in the traditional DPM are vegetation and soil (both sunlit and shaded). However, to date, the influence of shaded vegetation and shaded soil has not been fully considered in the NDVI-based DPM. Herein we analyze the necessity and feasibility of processing shaded components separately. The shaded soil was found to largely affect the canopy NDVI and can be combined with the vegetation (both sunlit and shaded) as one of the two components in DPM due to the high NDVI of shaded soil under a small percentage of diffuse sky radiation (< 10 % of the total hemispherical radiation in red band in this study). This finding partially explains why the canopy NDVI is oversensitive to background. The DPM was then improved with the solar and view angles to account for the fraction of shaded soil. We performed simulation and field measurements to validate the proposed models to varying factors including the vegetation structure, soil background, solar and view geometry, and slope gradient. The improved DPMs outperformed the traditional DPM (, where no effect of shaded soil is considered) when estimating the NDVI and FVC of the mixed pixel. The FVC estimated with traditional DPM results in the RMSE from 0.14 to 0.31, and that with the improved DPMs range from 0.04 to 0.13. The decrease of uncertainty by using the improved DPMs was generally over 50 % when compared to the output from a traditional DPM. The proposed DPM maintains the advantage of an easy-of-use two-component mixture model yet is more accurate than traditional ones and thus expected to improve the FVC estimation from satellite data.

中文翻译：

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考虑遮荫植被和土壤成分的植被覆盖度估计 NDVI 混合模型的改进


绿色植被比例是区域和/或全球尺度植被丰度的广泛使用的指标。像元混合模型，特别是基于归一化差分植被指数（NDVI）的二元像元模型（DPM，也称两端元模型），在植被覆盖率（FVC）遥感的准确估算中发挥着重要作用。 。传统 DPM 的两个组成部分是植被和土壤（阳光照射和阴影）。然而，迄今为止，基于NDVI的DPM尚未充分考虑遮荫植被和遮荫土壤的影响。这里我们分析一下对阴影部分进行单独处理的必要性和可行性。研究发现，遮荫土壤在很大程度上影响了冠层 NDVI，并且可以与植被（阳光照射和遮荫）结合起来，作为 DPM 的两个组成部分之一，因为遮荫土壤在少量漫射天空辐射下的 NDVI 较高（% 3C 本研究中红波段总半球辐射的 10%）。这一发现部分解释了为什么冠层 NDVI 对背景过度敏感。然后根据太阳和视角改进 DPM，以考虑阴影土壤的比例。我们进行了模拟和现场测量，以验证所提出的模型对不同因素的影响，包括植被结构、土壤背景、太阳和视图几何形状以及坡度。在估计混合像素的 NDVI 和 FVC 时，改进的 DPM 优于传统的 DPM（不考虑阴影土壤的影响）。 FVC 估计传统 DPM 结果的 RMSE 为 0.14 至 0.31，而改进 DPM 的 FVC 估计范围为 0.04 至 0.13。 与传统 DPM 的输出相比，使用改进的 DPM 的不确定性降低通常超过 50%。所提出的 DPM 保持了易于使用的二组分混合模型的优点，但比传统模型更准确，因此有望改进卫星数据的 FVC 估计。