摘要
定量监测全球、区域和局地植被参数是对地观测的重要课题。植被冠层的中低分辨率定量遥感产品不能满足局地生态系统模型和智慧农林业精细化管理的需求,植被定量遥感产品的尺度亟待提升到高空间分辨率。本文首先讨论了针对植被监测的高分平台和高分被动光学数据特征,然后分析了经典植被BRDF模型在高分尺度的适用性,并总结了针对高空间分辨率遥感的植被BRDF建模、植被参数反演和真实性检验等方面的研究成果和进展。在此基础上,展望了高空间分辨率植被定量遥感今后发展方向。若将植被定量遥感应用的尺度拓展到米级,迫切需要创新定量遥感的理论和方法,这些理论和方法创新将支撑陆地生态系统全新监测管理模式。
Quantitative monitoring of global,regional,and local vegetation parameters is an important topic in Earth observation.While the acquisition and processing techniques for medium-and low-resolution remote sensing data are well-established,quantitative remote sensing products based on these data have been developed to meet the needs of large-scale monitoring.However,these medium-and low-resolution products are unable to effectively capture the fine-scale variations within the vegetation canopy,which limits their application in ecosystem modeling and the management of precision agriculture and forestry.Therefore,enhancing the spatial resolution of vegetation quantitative remote sensing products is essential.Since the early 21st century,numerous high-resolution sensor platforms have been deployed,acquiring vast amounts of high-resolution imagery.Extensive research has been conducted on vegetation classification,canopy extraction,and other attribute or spatial geometric information.Moving forward,the quantification of vegetation parameters will continue to drive advancements in Earth observation technologies,providing more refined support for ecological monitoring and intelligent agriculture.This paper first discusses the characteristics of high-resolution platforms and high-resolution passive optical data for vegetation monitoring,then analyzes the applicability of classic vegetation BRDF(Bidirectional Reflectance Distribution Function)models at high spatial scales.The paper further summarizes the research achievements and progress related to the representation of vegetation structure,BRDF modeling,vegetation parameter retrieval,and accuracy validation for high spatial resolution remote sensing.Based on these findings,the paper also outlines the future directions for the development of high spatial resolution vegetation quantitative remote sensing.To extend the application scale of vegetation quantitative remote sensing to the meter level,there is an urgent need for innovations in the theories and methods of quantitative remote sensing,which will support new monitoring and management models for terrestrial ecosystems.The highresolution platforms include high-resolution satellite platforms,airborne remote sensing platforms,and Unmanned Aerial Vehicle(UAV)remote sensing platforms,all of which provide essential support for acquiring high-resolution remote sensing images.High-resolution passive optical data have significantly increased in geometric and spectral complexity,placing higher demands on processing technologies.Vegetation remote sensing physical models can generally be classified into Radiative Transfer Models(RT),Geometric-Optical Models(GO),mixed models,and computer simulation models.Each model has its own characteristics and applicable scope.Under different spatial resolution conditions,the assumptions about the vegetation canopy vary,requiring different methods for characterizing vegetation structure.Research on vegetation BRDF models has increasingly explored the relationship between high-resolution canopy remote sensing images and the canopy's three-dimensional structure.However,this area of vegetation BRDF modeling has lagged behind the rapid advancements in high-resolution sensor technologies and image processing techniques.Vegetation parameter inversion involves extracting structural and physiological parameters from remote sensing images.High-resolution data are affected by factors such as adjacent and mixed pixels,which necessitates the use of specialized inversion algorithms.Validation serves as a critical method for assessing the quality,reliability,and applicability of remote sensing products,and high-resolution data requires measurement techniques that align with its resolution.This paper reviews recent research on high-resolution optical vegetation quantitative remote sensing,highlighting the characteristics of high-resolution data,quantitative vegetation models,parameter inversion methods,and validation techniques.It is intended to provide insights and support for the future advancement of high-resolution vegetation remote sensing technologies.
作者
范闻捷
彭乃杰
曹彪
穆西晗
杨斯棋
贺群超
翟德超
任华忠
崔要奎
阎广建
FAN Wenjie;PENG Naijie;CAO Biao;MU Xihan;YANG Siqi;HE Qunchao;ZHAI Dechao;REN Huazhong;CUI Yaokui;YAN Guangjian(Institution of Remote Sensing and Geographical Information System,Peking University,Beijing 100871,China;Beijing Key Laboratory of Spatial Information Integration and Its Applications,Peking University,Beijing 100871,China;Advanced Interdisciplinary Institute of Satellite Applications,Beijing Normal University,Beijing 100875,China;State Key Laboratory of Remote Sensing and Digital Earth,Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China;Beijing Engineering Research Center for Global Land Remote Sensing Products,Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China)
出处
《遥感学报》
北大核心
2025年第6期1365-1387,共23页
NATIONAL REMOTE SENSING BULLETIN
基金
国家自然科学基金(编号:42130104,42090013)。
作者简介
第一作者:范闻捷,研究方向为植被定量遥感、生态遥感和热红外遥感。E-mail:fanwj@pku.edu.cn。
