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1982-2023年全球光合作用代理的长期重建

Jianing Fang¹, Xu Lian², Youngryel Ryu^3,4,5

¹Department of Earth and Environmental Engineering, Columbia University, New York, USA. jf3423@columbia.edu.

Scientific data

|March 3, 2025

概括

我们开发了一种新的方法,利用卫星数据估计长期光合作用,超越目前的局限性. 这种方法为几十年的陆地生态系统功能提供了更强大的代理.

科学领域:

地球系统科学地球系统科学
遥感遥感遥感遥感
生态生态学生态生态学

背景情况:

卫星观察到的太阳诱导的叶绿素光 (SIF) 对理解陆地生态系统光合作用至关重要.
直接的SIF观测仅限于过去十年,阻碍了长期生态系统功能分析.

研究的目的:

使用易于获得的卫星反射率数据开发光合作用的长期,连续的代理.
扩展生态系统光合作用动态的分析,超越有限的直接SIF观测期.

主要方法:

利用高级非常高分辨率射线仪 (AVHRR) 和中等分辨率成像光谱射线仪 (MODIS) 的表面反射频段 (1982-2023年).
校准和轨道纠正的AVHRR频段与MODIS数据相重叠.
训练了一个神经网络,使用偏差校正的反射率数据来模拟轨道碳观测站-2 SIF,创建一个长期连续的SIF信息光合作用代理 (LCSPP).

主要成果:

创建了一个跨越1982年至2023年的全球LCSPP数据集.
虽然LCSPP表现出与以前的SIF代理商相似的技能,但其时间延伸得更远.
与卫星SIF和现场水平的原始生产总值 (GPP) 估计相比,LCSPP的相关性高于NDVI,kNDVI和NIRv.

结论:

开发的LCSPP为评估长期陆地生态系统光合作用活动提供了有价值的工具.
这种方法克服了直接SIF测量的时间限制,使得更广泛的历史分析成为可能.
LCSPP提供了更好的能力,可以代表多种不同植被类型的长期光合作用活动.

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