基于多源遥感信息的河北省陆地植被生产力模拟分析  

Simulation of vegetation productivity in Hebei based on multiple sources of remote sensing data

作　　者：夏烨 陈敬华 王绍强 孙雷刚[4,5] 陈斌[2] 陈世亮 王钦艺[1] 赵紫祺 XIA Ye;CHEN Jinghua;WANG Shaoqiang;SUN Leigang;CHEN Bin;CHEN Shiliang;WANG Qinyi;ZHAO Ziqi(Hubei Key Laboratory of Regional Ecology and Environmental Change,School of Geography and Information Engineering,China University of Geosciences,Wuhan 430074,China;Key Laboratory of Ecosystem Network Observation and Modeling,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;Engineering Technology Innovation Center for Intelligent Monitoring and Spatial Regulation of Land Carbon Sinks,Ministry of Natural Resources,China University of Geosciences,Wuhan 430074,China;Institute of Geographical Sciences,Hebei Academy of Sciences,Shijiazhuang 050011,China;Hebei Technology Innovation Center for Geographic Information Application,Shijiazhuang 050011,China)

机构地区：[1]中国地质大学(武汉)地理与信息工程学院,区域生态与环境变化湖北省重点实验室,武汉430074 [2]中国科学院地理科学与资源研究所,生态系统网络观测与模拟重点实验室,北京100101 [3]自然资源部国土碳汇智能监测与空间调控工程技术创新中心,中国地质大学(武汉),武汉430074 [4]河北省科学院地理科学研究所,石家庄050011 [5]河北省地理信息开发应用技术创新中心,石家庄050011

出　　处：《生态学杂志》2024年第12期3735-3745,共11页Chinese Journal of Ecology

基　　金：河北省科学院科技计划项目(22A03);国家自然科学基金项目(42101479);中国地质大学(武汉)科研启动基金资助项目(162301192642)资助。

摘　　要：总初级生产力(GPP)是全球生态系统碳循环的关键要素,准确评估陆地生态系统GPP的时空动态对全球气候变化和碳循环研究具有重要意义。本研究采用日光诱导叶绿素荧光(SIF)、光化学植被指数(PRI)和植被近红外指数(NIRv)等多源卫星遥感数据和华北平原4个涡度相关通量站观测数据,分析了2003—2020年河北省陆地植被GPP的时空动态格局,并比较和探讨了BEPS、MODIS和GOSIF GPP产品之间的差异及其原因。结果表明:(1)基于SIF、PRI和NIRv构建的多元线性GPP模型能够很好地捕捉通量观测的GPP动态,在草地、灌丛和森林优于传统SIF⁃GPP线性模型(ΔR^(2)=0.02,0.04,0.10),但在农田则相反;(2)基于多源遥感数据的GPP估算模型模拟发现,2003—2020年河北省陆地植被GPP达205.63±14.29 Tg C·a^(-1),呈现西北低东南高的格局,且整体呈上升趋势,年均增长2.35 Tg C·a^(-1);(3)本研究估算的农田GPP高于其他模型模拟结果,通过与地面实测结果比较发现,传统模型对农田GPP可能存在不同程度的低估。本研究结果阐明了多源遥感数据精确估算华北平原植被生产力的潜力,提出了BEPS、MODIS和GOSIF GPP产品在农田生态系统存在低估的问题并分析了其原因,为GPP产品的改进优化提供了方向。Gross primary productivity(GPP)is a key element of global carbon cycle.Accurate assessment of the spatiotemporal dynamics of GPP in terrestrial ecosystems is essential for global climate change and carbon cycle research.In this study,we simulated and analyzed the spatiotemporal patterns of GPP in Hebei Province from 2003 to 2020 using multi⁃source satellite remote sensing data of solar⁃induced chlorophyll fluorescence(SIF),photo⁃chemical reflectance index(PRI)and near⁃infrared reflectance of vegetation(NIRv)and observations from four flux sites in the North China Plain,and compared the differences of results from BEPS,MODIS and GOSIF GPP.The results showed that:(1)The multivariate linear GPP models constructed based on SIF,PRI,and NIRv,can effectively capture the GPP dynamics of flux observations and outperformed the traditional SIF⁃GPP linear model in grassland,shrubland,and forest(ΔR^(2)=0.02,0.04,0.10),but not in cropland;(2)the GPP of terrestrial eco⁃systems in Hebei Province was 205.63±14.29 Tg C·a^(-1)during 2003-2020,with a spatial pattern of low in the northwest and high in the southeast,as well as an overall upward trend with an average annual growth at 2.35 Tg C·a^(-1);(3)The simulated GPP of cropland was significantly higher than the results of the other three models,indicating possible underestimations of crop productivity to varying degrees by these models.This study elucidated the potential of multi⁃source remote sensing data for accurate estimation of vegetation productivity in the North China Plain,and indicated the underestimation of BEPS,MODIS and GOSIF GPP products in croplands,providing directions for further improvement and optimization of GPP products.

关 键 词：总初级生产力 日光诱导叶绿素荧光 光化学植被指数 植被近红外指数 模型模拟 

分 类 号：Q948[生物学—植物学] TP79[自动化与计算机技术—检测技术与自动化装置]