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从遥感太阳诱导叶绿素荧光到生态系统结构、功能与服务:第二部分——数据利用

From remotely-sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II-Harnessing data.

作者信息

Sun Ying, Wen Jiaming, Gu Lianhong, Joiner Joanna, Chang Christine Y, van der Tol Christiaan, Porcar-Castell Albert, Magney Troy, Wang Lixin, Hu Leiqiu, Rascher Uwe, Zarco-Tejada Pablo, Barrett Christopher B, Lai Jiameng, Han Jimei, Luo Zhenqi

机构信息

School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, New York, USA.

Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.

出版信息

Glob Chang Biol. 2023 Jun;29(11):2893-2925. doi: 10.1111/gcb.16646. Epub 2023 Mar 14.

Abstract

Although our observing capabilities of solar-induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency of SIF datasets are still in an active stage of research and development. As a result, there are considerable inconsistencies among diverse SIF datasets at all scales and the widespread applications of them have led to contradictory findings. The present review is the second of the two companion reviews, and data oriented. It aims to (1) synthesize the variety, scale, and uncertainty of existing SIF datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities laid out in (Sun et al., 2023) may impact process interpretation of various applications and contribute to inconsistent findings. We emphasize that accurate interpretation of the functional relationships between SIF and other ecological indicators is contingent upon complete understanding of SIF data quality and uncertainty. Biases and uncertainties in SIF observations can significantly confound interpretation of their relationships and how such relationships respond to environmental variations. Built upon our syntheses, we summarize existing gaps and uncertainties in current SIF observations. Further, we offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change, including enhancing in-situ SIF observing capability especially in "data desert" regions, improving cross-instrument data standardization and network coordination, and advancing applications by fully harnessing theory and data.

摘要

尽管我们对太阳诱导叶绿素荧光(SIF)的观测能力一直在迅速提高,但SIF数据集的质量和一致性仍处于积极的研发阶段。因此,不同尺度的各种SIF数据集之间存在相当大的不一致性,并且它们的广泛应用导致了相互矛盾的结果。本综述是两篇配套综述中的第二篇,以数据为导向。其目的是:(1)综合现有SIF数据集的种类、尺度和不确定性;(2)综合在生态、农业、水文、气候和社会经济领域的各种应用;(3)阐明这种数据不一致性与(Sun等人,2023年)中阐述的理论复杂性叠加后,如何可能影响各种应用的过程解释并导致结果不一致。我们强调,准确解释SIF与其他生态指标之间的功能关系取决于对SIF数据质量和不确定性的全面理解。SIF观测中的偏差和不确定性会显著混淆对其关系的解释以及这些关系如何响应环境变化。基于我们的综合分析,我们总结了当前SIF观测中存在的差距和不确定性。此外,我们就帮助改善气候变化下生态系统结构、功能和服务信息所需的创新提出了我们的观点,包括增强现场SIF观测能力,特别是在“数据荒漠”地区,改善跨仪器数据标准化和网络协调,以及通过充分利用理论和数据推进应用。

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