多尺度山地植被总初级生产力遥感估算中的误差来源解析  

Analysis of error sources in the multi-scale remote sensing estimation of mountain vegetation gross primary productivity

作　　者：谢馨瑶 李爱农[1,2] 田洁 邬昌林[1] XIE Xinyao;LI Ainong;TIAN Jie;WU Changlin(Research Center for Digital Mountain and Remote Sensing Application,Institute of Mountain Hazards and Environment,Chinese Academy of Sciences,Chengdu 610041,China;Wanglang Mountain Remote Sensing Field Observation and Research Station of Sichuan Province,Mianyang 621000,China)

机构地区：[1]中国科学院水利部成都山地灾害与环境研究所数字山地与遥感应用中心,成都610041 [2]王朗山地遥感四川省野外科学观测研究站,绵阳621000

出　　处：《遥感学报》2025年第1期203-218,共16页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家重点研发计划(编号:2020YFA0608702);国家自然科学基金(编号:42471429,42201418);中国科学院、水利部成都山地灾害与环境研究所科研项目(编号:IMHE-ZYTS-05);四川省科技计划(编号:2024NSFSC0794);中国博士后项目(编号:2021M700139,2023T160627);中国科学院青年促进会项目(编号:2023390)。

摘　　要：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GPP(Gross Primary Productivity)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遥感产品常忽略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)空间分辨率下山地植被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 g(/m^(2)·a)),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 g(/m^(2)·a))。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 g(/m^(2)·a)),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 g(/m^(2)·a)和210 g(/m^(2)·a))。因此,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。本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。Mountain ecosystems,covering 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 bioindicator 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.Owing to the effect of complex mountainous conditions and the limitations from spatial resolutions,obvious topographic errors and spatial scaling errors in mountain vegetation GPP estimates occur.Thus,evaluating the error sources in the estimation of mountain vegetation GPP across multiple spatial scales is crucial.In this study,we selected the Wanglang National Nature Reserve—a typical mountainous ecosystem in southwest China—as the study area.An eco-hydrological model called Boreal Ecosystems Productivity Simulator-TerrainLab was used to obtain the vegetation GPP and analyze the topographic and spatial scaling errors at fine,medium,and coarse spatial scales(i.e.,30,480,and 960 m).First,the topographic errors in estimating vegetation GPP were evaluated across four scenarios that characterized the effects of different topographic features at the fine,medium,and coarse spatial scales.Then,spatial scaling errors were illustrated at the scales of 480 and 960 m.Finally,the agreement index(d),determination coefficient(R2),Root-Mean-Square Error(RMSE),and Mean Bias Error(MBE)were used to evaluate the topographic and spatial scaling errors in modeling mountain vegetation GPP at the fine,medium,and coarse spatial scales.Results showed that the multiscale vegetation GPP estimates across different simulation conditions presented obvious spatial differences(the difference among regional mean values upped to 198 g/(m^(2)·a)).The topographic errors of veg

关 键 词：遥感 植被总初级生产力 山地生态系统 多尺度 地形误差 空间尺度误差 

分 类 号：P2[天文地球—测绘科学与技术]