生态环境学报2025，Vol.34Issue(9)：1473-1482,10.DOI:10.16258/j.cnki.1674-5906.2025.09.014
植被物候监测技术与遥感反演方法研究进展
Progress of Vegetation Phenology Monitoring Technology and Remote Sensing Inversion Method
摘要
Abstract
Phenology is a subdiscipline of biology that focuses on the timing of periodic biological events in organisms and their relationships with environmental factors,particularly climatic variables,such as temperature,precipitation,and photoperiod.It primarily investigates the temporal patterns of biological processes,such as growth,development,reproduction,dormancy,and senescence in plants,animals,and microorganisms across different timeframes.Among its branches,vegetation phenology,which refers specifically to plants,is a key component that deals with the timing of physiological stages in plant life cycles throughout the growing season.Research on vegetation phenology is essential for understanding how ecosystems function,particularly in relation to the carbon and water cycles.It plays a critical role in quantifying the exchange of energy and matter between the biosphere and atmosphere and in evaluating the ecological consequences of climate variability and long-term climate change.Phenological shifts serve as sensitive bioindicators of environmental change;thus,vegetation phenology has become a key focus in global change research.It is now widely used to track the effects of climate anomalies on terrestrial ecosystems,agricultural productivity,and biodiversity patterns.Vegetation phenology can be further divided into structural phenology,which reflects the visible morphological changes in plants,such as leaf-out,flowering,and senescence,and functional phenology,which describes the dynamic physiological and metabolic processes underlying plant growth and ecological functions of plants.Although distinct,these two aspects are closely interrelated and together provide a comprehensive picture of plant-environment interactions.The combination of both perspectives enhances our ability to monitor vegetation health,detect stress,and assess species'adaptive responses to environmental variability.With the accelerating pace of global climate change,there is an increasing demand for high-resolution,multi-scale monitoring of vegetation phenology.Accurate and timely phenological data are required to model ecosystem responses,improve the parameterization of climate and ecological models,and inform management strategies in agriculture,forestry,and conservation sectors.The ability to detect changes in the timing of phenological events allows for early warnings of ecological disruption and can inform decisions regarding resource allocation and the protection of biodiversity.Currently,phenology monitoring is conducted using various approaches,each with its own advantages and limitations.Ground-based phenological observations offer detailed and accurate measurements at the canopy and individual plant levels.These methods provide high temporal fidelity and can capture subtle biological changes.However,the spatial coverage of ground stations is often limited by human,material,and financial resources,making it difficult to scale up to regional or global assessments of the data.Near-surface phenology monitoring using automatic time-lapse digital cameras(phenocams),eddy covariance technology,and unmanned aerial vehicles(UAVs)has emerged as a valuable extension of human observation.These tools can bridge the gap between plot-level ground observations and broad-scale satellite-based observations.They enhance spatial representativeness while retaining fine temporal and spatial resolutions.Moreover,near-surface methods support the development of robust retrieval algorithms and phenological models by providing dense time series data for various ecosystems.In recent years,they have been increasingly deployed for long-term ecological research and agricultural applications.Remote sensing-based phenological inversion offers a unique macroscopic perspective.Satellite platforms provide repeated and consistent observations over large spatial extents and long periods.This makes it possible to analyze cumulative phenological trends and interannual variability on regional and global scales.Remotely sensed phenological data are particularly valuable for detecting shifts in phenological changes under the influence of global warming.However,the trade-off between spatial and temporal resolution remains a key challenge.Higher-resolution imagery often has a lower revisit frequency,whereas sensors with frequent coverage may lack sufficient spatial detail.In this context,this study reviews the foundational applications of ground-based observations,near-surface sensing,and satellite remote sensing for vegetation phenology monitoring.It compiles and discusses the available phenological data from national ground-based monitoring networks in China and outlines the inversion principles,technical advantages,and practical applications of widely used observation tools,including phenocams,eddy covariance towers and UAVs.This review further introduces commonly used satellite platforms,such as MODIS,Sentinel-2,and the Landsat series,along with associated vegetation indices,such as the Normalized Difference Vegetation Index(NDVI),Enhanced Vegetation Index(EVI),and Leaf Area Index(LAI).These indices are essential proxies for characterizing vegetation status and extracting phenological metrics.Based on these tools and data,this study examined the latest methodological advances and representative case studies for extracting phenology parameters from different remote sensing sources.This study also discusses the data processing approaches used for remote-sensing time-series analysis in phenology monitoring.Classical methods,such as Savitzky-Golay filtering,asymmetric Gaussian fitting,and logistic curve modeling are described in terms of their principles,strengths,and limitations.In addition,more recent techniques,such as the wWHd method,a spatially weighted Whittaker smoother with a dynamic regularization parameter λ,and Gaussian Process Regression(GPR),are highlighted.These advanced methods allow for a more accurate representation of multi-season growth patterns and improve curve fitting under noisy or uncertain data.New research progress helps broaden innovative thinking in this field.Furthermore,this study introduced a variety of software tools designed for vegetation phenology extraction.These include open-source code libraries and graphical user interface-based software applications.Several tools can handle dual growing seasons or extract multiple phenological phases within a single growing season.The comparison covers their core features,ease of use,compatibility with different data formats,and supported application domains.The increasing availability of modular and user-friendly tools has significantly lowered the barriers to entry for researchers in multiple disciplines.In the final section,we present a forward-looking perspective on the future trends in vegetation phenology monitoring.This emphasizes the critical role of multisource data fusion,which combines ground-based,near-surface,and satellite observations to enhance spatial coverage,accuracy,and consistency.Additionally,this study explored the potential of unconventional data sources,such as mobile phone applications and social media,to complement formal monitoring systems and offer novel insights into vegetation changes from the perspective of citizen-science.Finally,this review highlights the increasing importance of machine and deep learning in phenological modeling and inversion.These techniques offer powerful tools for identifying patterns in high-dimensional data and simulating complex ecological processes.In particular,under the backdrop of big data,artificial intelligence based approaches hold great promise for intelligent phenology recognition,spatiotemporal prediction,and ecosystem response modeling.The progress of future vegetation phenological information inversion may rely on multi-source data fusion and the application of machine learning,particularly deep learning.关键词
植被物候/遥感技术/物候反演/机器学习/多尺度监测Key words
vegetation phenology/remote sensing technology/phenology inversion/machine learning/multi-scale monitoring分类
资源环境引用本文复制引用
赵文琪,张佳华,张鹏,白林燕,姚凤梅..植被物候监测技术与遥感反演方法研究进展[J].生态环境学报,2025,34(9):1473-1482,10.基金项目
国家重点研发计划项目(2023YFF1303802) （2023YFF1303802）
