| 摘要: |
| 浮游藻类广泛分布于海洋和内陆水体生态系统中，其生长和发育过程具有明显的时空异质性，对气候变化的响应也极为复杂。藻类物候描述了其在长期适应气候、水质和人类干预等因素下的周期性变化，从而形成一种与环境条件相适应的生长发育节律。它主要包括藻类的出现时间、增长高峰以及减少或消亡的时间等特征。遥感技术通过高时空分辨率持续获取叶绿素a浓度数据（浮游藻类生物量的估算指标），实现对藻类物候的长期监测。本文详细地回顾了近年来遥感藻类物候监测和提取方法的进展，指出目前存在的问题与不足并对未来的发展趋势进行展望。首先，回顾了现有卫星遥感提供大范围时空连续的藻类生长信息。其次，总结了浮游藻类物候阶段的监测，以及估计藻类特定物候阶段的方法。同时，介绍了用于遥感时间序列估算藻类物候的常用数据处理方法，探讨了浮游藻类物候特性的变化趋势。最后，探索了可能影响藻类物候变化的因素和驱动机制。基于以上的分析，本文指出未来藻类物候遥感的研究应重点关注：（1）开发并验证适用于不同水域环境的通用算法，结合机器学习等智能算法改进物候模型，以提高物候监测精度和算法的业务化应用水平。（2）结合数值模型和生态系统动态模型，深入研究浮游藻类物候背后的驱动机制。 |
| 关键词: 藻类 物候 叶绿素a 海洋和内陆水体 |
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| 基金项目:国家自然科学基金资助项目（U2243230，42101366，41971322）和吉林省自然科学基金（YDZJ202401474ZYTS）联合资助。 |
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| Advances in Remote Sensing Monitoring of Phytoplankton Phenology |
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Yan Zhaojiang1, Fang Chong1, Song Kaishan1, Wang Xiangyu1, Lyu Yunfeng2
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1.：Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences;2.：Changchun Normal University,School of Geographic Science
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| Abstract: |
| Phytoplankton are widely distributed in marine and freshwater ecosystems. Their growth and development exhibit significantly spatial and temporal variations, and their response to climate change are complex. Algal phenology describes the cyclic changes of phytoplankton over long-term adaptation to factors such as climate, water quality, and human interventions, establishing a growth rhythm attuned to environmental conditions. It primarily includes characteristics such as the timing of algal appearance, peak growth, and decline or disappearance. Remote sensing technology continuously acquires high spatiotemporal resolution data on chlorophyll-a concentrations (an indicator of phytoplankton biomass), enabling long-term monitoring of algal phenology. This paper provides a detailed review of recent advances in remote sensing methods for monitoring and extracting algal phenology, identifies current issues and limitations, and looks ahead to future trends. Firstly, it reviews how existing satellite remote sensing provides extensive spatiotemporally continuous information on algal growth. Secondly, it summarizes the monitoring of phytoplankton phenological stages and methods for estimating specific phenological phases of algae. It also introduces common data processing methods used for estimating algal phenology from remote sensing time series and discusses the changing trends in phytoplankton phenological characteristics. Finally, it explores the factors and mechanisms that may influence changes in algal phenology. Based on this analysis, future research on remote sensing of algal phenology should focus on the following aspects: (1) Developing and validating general algorithms suitable for various aquatic environments, integrating machine learning and other intelligent algorithms to improve phenological models and enhance the accuracy of phenological monitoring and operational application. (2) Combining numerical models with ecosystem dynamic models to investigate the driving mechanisms behind phytoplankton phenology. |
| Key words: Algae Phenology Chlorophyll-a Oceans and inland waters |
