Evaluation of the Vegetation-Index-Based Dimidiate Pixel Model for Fractional Vegetation Cover Estimation
Evaluation of the Vegetation-Index-Based Dimidiate Pixel Model for Fractional Vegetation Cover Estimation
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基于植被指数的二元像素模型的植被覆盖度估计评估
DOI：
10.1109/tgrs.2020.3048493
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发表时间：
2022-01-01
影响因子：
8.2
通讯作者：
Yan, Guangjian
中科院分区：
文献类型：
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作者：
Yan, Kai;Gao, Si;Yan, Guangjian
基于植被指数（维斯）的二分像元模型（DIMIDIATE LIKE MODEL）遥感估算是植被覆盖度（FVC）制图的常用方法。该方法的主要缺点是没有考虑土壤和植被之间真实的端元条件和多次散射。对这些模型缺陷引起的FVC不确定性的分析仍然缺乏。本文首先根据不确定度传播定律（LPU）计算了由反射率不确定度引起的FVC理论不确定度。然后，我们使用6个维斯在3-D森林场景上测试了该算法的性能。我们模拟了Aqua-MODIS和Landsat-OLI表面反射率（SR）在其相应的空间分辨率和光谱响应函数（SRF）使用一个经过验证的3-D辐射传输（RT）模型，这有助于分离模型和输入的不确定性。我们发现，比率植被指数（RVI）和增强植被指数（EVI）为基础的模型受传感器的影响最大，其次是归一化差异植被指数（NDVI），增强植被指数2（EVI 2），重正化差异植被指数（RDVI），和差异植被指数（DVI）为基础的模型。在不考虑SR不确定性的情况下，基于DVI的模型表现最好（FVC绝对差< 0.1）;然而，常用的NDVI模型达到了0.35的最大差异。同时，输入的不确定性增加了FVC反演的不确定性。我们注意到，太阳天顶角（SZA）的增加导致了显着增加的FVC下的均匀分布，这可以解释为增加的阴影比例。此外，在低（高）植被覆盖区，土壤（植被）端元的纯度决定了模型的精度。本研究为基于阈值的FVC最佳VI的选择提供了参考。
Remote sensing estimation based on the dimidiate pixel model (DPM) using vegetation indices (VIs) is a common approach for mapping fractional vegetation cover (FVC). The major drawback of DPM is that it does not consider real endmember conditions and multiple scattering between soil and vegetation. An analysis of FVC uncertainties caused by these model deficiencies is still lacking. Here, we first calculated the FVC theoretical uncertainty caused by reflectance uncertainties based on the law of prapagation of uncertainty (LPU). Then, we tested the performance of DPM using six VIs over 3-D forest scenes. We simulated both Aqua-MODIS and Landsat-OLI surface reflectance (SR) at their corresponding spatial resolutions and spectral response functions (SRFs) using a well-validated 3-D radiative transfer (RT) model which helps to separate the model and input uncertainties. We found that ratio vegetation index (RVI)- and enhanced vegetation index (EVI)-based models were most affected by sensors, followed by the normalized difference vegetation index (NDVI)-, enhanced vegetation index 2 (EVI2)-, renormalized difference vegetation index (RDVI)-, and difference vegetation index (DVI)-based models. Without considering SR uncertainties, the DVI-based model performed best (FVC absolute difference < 0.1); however, the commonly used NDVI model reached a maximum difference of 0.35. At the same time, input uncertainty increased the uncertainty of FVC retrieval. We noticed that the increase of solar zenith angle (SZA) resulted in a clear increase of retrieved FVC under the uniform distribution, which can be explained by the increased shadow proportion. Besides, model accuracy was dominated by the purity of soil (vegetation) endmember in low (high) vegetation cover area. This study provides a reference for the selection of the optimal VI for FVC retrieval based on the DPM.