Seasonality of vegetation greenness in Southeast Asia unveiled by geostationary satellite observations

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
Jiaqi Tian , Xiangzhong Luo , Weile Wang , Liyao Yu , Diane Tan Ting Ng , Kazuhito Ichii , Yao Zhang , Xiaoyang Zhang
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引用次数: 0

Abstract

Tropical forests in the Amazon are characterized by a dry-season green-up, indicating a light-dominated regime in the seasonal variation of ecosystem functions. Southeast Asia, which hosts some of the most carbon-dense and diverse ecosystems in the world, is also expected to green up in dry seasons, however, recent in-situ evidence suggests otherwise. Here, we utilized high-frequency observations from the Himawari-8 geostationary satellite to examine the seasonality of vegetation greenness across Southeast Asia and to investigate potential factors driving this seasonality. We found that evergreen forests in maritime Southeast Asia green up in dry seasons, which is linked to positive anomaly in incoming radiation, similar to the Amazon forests. However, deciduous forests, croplands, and evergreen forests in continental Southeast Asia tend to green up in wet seasons, which is associated with the changes in climate sensitivities of vegetation greenness between dry and wet seasons. Our study highlights seasonal differences in tropical vegetation across biomes and sheds light on the underlying mechanisms of climate-vegetation interactions in Southeast Asia.
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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