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The composite drought index incorporated solar-induced chlorophyll fluorescence enhances the monitoring capability of short-term drought

叶绿素荧光 环境科学 期限(时间) 叶绿素a 索引(排版) 叶绿素 大气科学 遥感 植物 地质学 生物 物理 计算机科学 量子力学 万维网
作者
Hangxing Ren,Lin Du,Chuanjing Peng,Jian Yang,Wei Gao
出处
期刊:Journal of Hydrology [Elsevier BV]
卷期号:637: 131361-131361 被引量:5
标识
DOI:10.1016/j.jhydrol.2024.131361
摘要

Understanding the response of vegetation to drought is significant for socio-economic development and biodiversity conservation. However, due to the complexity of drought, relying only on one approach, such as the surrounding physical environmental conditions or specific vegetation characteristics, does not yield precise results. To address this challenge, composite drought indices incorporating normalized difference vegetation index (NDVI) and other multiple variables, have been developed and shown promising effectiveness in drought detection. Compared to NDVI, solar-induced chlorophyll fluorescence (SIF) decreases due to the increased non-photochemical quenching and reduced photosynthesis during short-term drought stress. This indicates a direct association between SIF and vegetation photosynthesis, offering advantages over NDVI in capturing drought effects. Nevertheless, SIF has not yet been fully integrated into composite drought indices. Therefore, this study focuses on assessing the advantages of SIF for short-term drought monitoring by constructing a new composite drought index (CPDI) using the Principal Component Analysis (PCA) method. Subsequently, CPDI is employed to forecast future drought conditions. Overall, CPDI performs exceptionally well as a composite drought index in drought trend analysis and drought event identification, indeed advancing the monitoring capability for short-term drought. Furthermore, the incorporation of SIF in CPDI, obtained from various data sources provides more timely monitoring of drought events, while the CPDI with NDVI reflects the cumulative effect of drought conditions over a longer period. In the future, it is potential to utilize the benefits of SIF in constructing drought indices or combine it with NDVI for comprehensive drought characterization.
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