Satellite-derived vegetation indices (VIs) have been extensively used in monitoring vegetation dynamics at local, regional, and global scales. While numerous studies have explored various factors influencing VIs, a remarkable knowledge gap persists concerning their applicability in mountain areas with complex topographic variations. Here we bridge this gap by conducting a comprehensive evaluation of the topographic effects on ten widely used VIs. We used three evaluation strategies, including: (i) an analytic radiative transfer model; (ii) a 3D ray-tracing radiative transfer model; and (iii) Moderate Resolution Imaging Spectroradiometer (MODIS) products. The two radiative transfer models provided theoretical evaluation results under specific terrain conditions, aiding in the first exploration of the interactions of both shadow and spatial scale effects on VIs. The MODIS-based evaluation quantified the discrepancies in VIs between MODIS-Terra and MODIS-Aqua over flat and rugged terrains, providing new insights into real satellite data across different temporal scales (i.e., from daily to multiple years). Our evaluation results were consistent across these three strategies, revealing three key findings. (i) The normalized difference vegetation index (NDVI) generally outperformed the other VIs, yet all VIs did not perform well in shadow areas (e.g., with a mean relative error (MRE) of 14.7% for NDVI in non-shadow areas and 26.1% in shadow areas). (ii) The topographic impacts exist at multiple spatiotemporal scales. For example, the MREs of NDVI reached 28.5% and 11.1% at 30 m and 3 km resolutions, respectively. The quarterly and annual VIs deviations between MODIS-Terra and MODIS-Aqua also increased with slope. (iii) We found the topography-induced interannual variations in multiple VIs both in simulated data and MODIS data. VIs trend deviations between MODIS-Terra and MODIS-Aqua over the Tibetan Plateau from 2003 to 2020 increased as the slope steepened (i.e., NDVI and enhanced vegetation index (EVI) trend deviations generally doubled). Overall, the sun-target-sensor geometry changes induced by topography, causing shadows in mountains along with obstructions in sensor observations, compromised the reliability of VIs in these terrains. Our study underscores the considerable impacts of topography, particularly shadow effects, on multiple VIs at various spatiotemporal scales, highlighting the imperative of cautious application of VIs-based trend calculation in mountains.

中文翻译：

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量化地形如何影响不同时空尺度的植被指数


卫星植被指数（VI）已广泛用于监测地方、区域和全球尺度的植被动态。尽管大量研究探索了影响 VI 的各种因素，但对于其在地形变化复杂的山区的适用性仍然存在显着的知识差距。在这里，我们通过对十种广泛使用的 VI 的地形影响进行综合评估来弥补这一差距。我们使用了三种评估策略，包括：（i）分析辐射传输模型； (ii) 3D 射线追踪辐射传输模型； (iii) 中分辨率成像光谱仪 (MODIS) 产品。这两种辐射传输模型提供了特定地形条件下的理论评估结果，有助于首次探索阴影和空间尺度效应对VI的相互作用。基于 MODIS 的评估量化了 MODIS-Terra 和 MODIS-Aqua 在平坦和崎岖地形上的 VI 差异，为不同时间尺度（即从每天到多年）的真实卫星数据提供了新的见解。我们的评估结果在这三种策略中是一致的，揭示了三个关键发现。 (i) 归一化植被指数 (NDVI) 一般优于其他 VI，但所有 VI 在阴影区域均表现不佳（例如，非阴影区域 NDVI 的平均相对误差 (MRE) 为 14.7%，NDVI 的平均相对误差 (MRE) 为 26.1%）阴影区域的百分比）。 (ii) 地形影响存在于多个时空尺度。例如，NDVI在30 m和3 km分辨率下的MRE分别达到28.5%和11.1%。 MODIS-Terra 和 MODIS-Aqua 之间的季度和年度 VI 偏差也随着斜率的增加而增大。 (iii) 我们在模拟数据和 MODIS 数据中发现了多个 VI 中地形引起的年际变化。 2003年至2020年青藏高原MODIS-Terra和MODIS-Aqua之间的VI趋势偏差随着坡度变陡而增大（即NDVI和增强植被指数（EVI）趋势偏差普遍翻倍）。总体而言，地形引起的太阳-目标-传感器几何形状的变化，导致山脉中的阴影以及传感器观测中的障碍物，损害了这些地形中VI的可靠性。我们的研究强调了地形（尤其是阴影效应）对不同时空尺度的多个 VI 的显着影响，强调了在山区谨慎应用基于 VI 的趋势计算的必要性。