基于日光诱导荧光叶绿素估算的全球陆地植被总初级生产力  

Estimation of Gross Primary Productivity(GPP)of Global Terrestrial Vegetation Based on Solar-Induced Chlorophyll Fluorescence

作　　者：黄跃飞 夏中帅[1] 宋天华 张硕 陈世鎏[2] HUANG Yuefei;XIA Zhongshuai;SONG Tianhua;ZHANG Shuo;CHEN Shiliu(Key Laboratory of Ecological Protection and High Quality Development in the Upper Yellow River,Key Laboratory of Water Ecological Remediation and Protection at Headwater Regions of Big Rivers,Ministry of Water Resources,State Key Laboratory of Plateau Ecology and Agriculture,Qinghai University,Xining 810016,China;State Key Laboratory of Hydrosphere Science and Engineering,Tsinghua University,Beijing 100084,China;Preparatory Work Center for the Hydraulic Project at Daliushu,Yinchuan 75002,China)

机构地区：[1]青海省黄河上游生态保护与高质量发展重点实验室,水利部江河源区水生态治理与保护重点实验室,青海大学省部共建三江源生态与高原农牧业国家重点实验室,青海西宁810000 [2]清华大学水圈科学与水利水电工程国家重点实验室,北京100084 [3]宁夏大柳树水利枢纽工程前期工作中心,宁夏银川750002

出　　处：《应用基础与工程科学学报》2025年第1期87-102,共16页Journal of Basic Science and Engineering

基　　金：清华大学-宁夏水联网数字治水联合研究院专项统筹重点项目(SKL-IOW-2023TC2309);青海省科技重大专项(2021-SF-A7-1-3)。

摘　　要：全球尺度陆地植被总初级生产力(GPP)估算的过程机理模型参数复杂、难以确定,数据驱动模型和光能利用效率模型对数据的依赖性强,陆地生态系统显著的空间差异使得已有的3大类GPP估算模型在地面观测数据相对缺乏的地区存在明显的应用局限性.通过日光诱导叶绿素(SIF)的连续观测数据和土地覆盖数据,对不同植被类型分别构建了GPP-SIF经验关系,并验证了GPP-SIF关系在相同植被类型中的稳定性和不同植被类型之间的差异.基于建立的GPP-SIF经验关系,利用最新的RTSIF数据,生产了全球陆地生态系统8d、0.05°空间分辨率的GPP_(SIF)产品.GPP_(SIF)与其他GPP估算模型的产品以及站点观测GPP进行交叉验证结果表明,建立的全球尺度GPP估算方法具有计算简单和结果可靠的优势,并在数据缺乏地区估算的GPP_(SIF)表现优异.Estimating global terrestrial Gross Primary Productivity(GPP)through process-based models is challenging due to complex parameterization,while data-driven models and light-use efficiency models rely heavily on extensive datasets.The significant spatial heterogeneity of terrestrial ecosystems also limits the application of these three major GPP models in regions lacking ground-based observations.This study developed empirical GPP-SIF(Solar-Induced Chlorophyll Fluorescence)relationships specific to various vegetation types using continuous SIF observation data and land cover information.The stability of these relationships within identical vegetation types and their variation across different types were validated.Based on the established GPP-SIF relationships and the latest RTSIF data,we generated an 8-day,0.05-degree spatial resolution global GPP_(SIF) product for terrestrial ecosystems.Cross-validation with other GPP estimation models and site-measured GPP demonstrated that the proposed global GPP estimation method is computationally efficient with reliable results and exceptional performance in data-scarce regions.

关 键 词：陆地生态系统 总初级生产力 日光诱导荧光叶绿素 遥感 通量观测站网 全球尺度 

分 类 号：TV11[水利工程—水文学及水资源]