山地生态系统是陆地重要的贮碳库，准确估算山地植被总初级生产力（Gross Primary Productivity，GPP）可进一步认知陆地植被对全球气候变化的反馈作用。然而，现有植被GPP遥感产品常常忽略了地形对光合作用过程的控制作用和亚像元空间异质性，分别导致了“地形误差”和“空间尺度误差”。本文以四川王朗国家级自然保护区为例，重点解析高（30 m）、中（480 m）、低（960 m）空间分辨率山地植被GPP遥感估算中的误差来源。结果表明，不同地形条件模拟下的多尺度植被GPP呈现出明显的空间差异（区域均值差距高达198 gC m-2 yr-1），说明忽略山地环境的特殊性将对GPP遥感估算结果造成较大偏差。植被GPP遥感估算地形误差随着空间分辨率的降低呈现减小的趋势，高空间分辨率下的地形误差不容忽视（区域偏差高达200 gC m-2 yr-1）。其中，未考虑水分重分配导致山地植被GPP空间分布存在较大不确定性（均方根误差为402 gC m-2 yr-1），因此土壤水分模拟精度的提升将有助于进一步改善山地高空间分辨率植被GPP遥感产品。从植被GPP遥感估算空间尺度误差的角度来说，研究发现其随着空间分辨率的降低呈现增大的趋势，中、低空间分辨率下的尺度误差均不容忽视（分别为161和210 gC m-2 yr-1）。本文建议在多尺度山地植被GPP遥感估算中，高空间分辨率产品应该重点关注地形效应，中、低空间分辨率产品应该进一步消除空间尺度误差。研究结果可为生产山地植被GPP遥感产品提供有益认知，丰富山地定量遥感理论体系，助力“双碳”目标。

Objective: Mountain ecosystems with coverage of approximately 24% of the terrestrial surface，are the key component of earth’s carbon cycle in terrestrial ecosystems. Vegetation in mountain ecosystems can regulate the energy budget via mediating the exchange of energy and substance, and thus has been regarded as an essential bio-indicator for the global climate change over the past decades. Accurate estimation of mountain vegetation gross primary productivity (GPP) plays a vital role in understanding the function of mountain ecosystems and characterizes the ecosystem responses to climate change. Due to the effect of complex mountainous conditions and the limitations from the spatial resolutions, there are obvious topographic errors and spatial scaling errors in mountain vegetation GPP estimates. Thus, it is crucial to evaluate the error sources in estimating mountain vegetation GPP across multiple spatial scales. Method: In this paper, we selected the Wanglang National Nature Reserve - a typical mountainous ecosystem of southwest China as the study area. This study used an eco-hydrological model called Boreal Ecosystem Productivity Simulator (BEPS)-TerrainLab to obtain the vegetation GPP and analyze the topographic errors and spatial scaling errors at the fine, medium, and coarse spatial scales (i.e., 30 m, 480 m, and 960 m). At the fine, medium, and coarse spatial scales, the topographic errors in estimating vegetation GPP were evaluated across four scenarios that characterize the effects of different topographic features. Spatial scaling errors were illustrated at the scales of 480 m and 960 m, respectively. Finally, the agreement index (d), determination coefficient (R2), root mean square error (RMSE), and mean bias error (MBE) were used to evaluate the topographic errors and spatial scaling errors in modeling mountain vegetation GPP at the fine, medium, and coarse spatial scales. Result: Results showed that the multi-scale vegetation GPP estimates across different simulation conditions presented obvious spatial differences (the difference among regional mean value upped to 198 gC m-2 yr-1). The topographic errors of vegetation GPP estimates showed a decreasing trend with the decrease of spatial resolution, suggestting that more attention should be paid to high spatial resolution (the MBE value is 200 gC m-2 yr-1). Specifically, the error caused by ignoring the redistribution of soil water was observed to be the largest source of topographic errors. As for the spatial scaling errors, an increasing trend with the decrease of spatial resolution was found, highlight the necessity of reducing the spatial scaling errors in middle and coarse spatial resolution GPP estimates (161 and 210 gC m-2 yr-1). Conclusion: During the process of generating the multi-scale mountain vegetation GPP products, it was necessary to remove the topographic effects on high spatial resolution GPP estimation. Simultaneously, attention should also be given to the spatial scaling errors of GPP products at middle and coarse spatial resolutions. Considering the obvious topographic errors caused by ignoring the water redistribution, accurate estimation of soil moisture would improve the quality of GPP products over mountainous areas, especially these products at high spatial resolution.