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在CONUS NEON地点协调陆地碳循环观测:评估多个数据约束的信息贡献

Dongchen Zhang¹, Qianyu Li^2,3, Alexis Helgeson⁴

¹Department of Earth & Environment, Boston University, Boston, Massachusetts, USA.

Global change biology

|February 20, 2026

概括

准确的陆地碳库存至关重要. 这项研究融合了数据源,以减少不确定性,发现土壤碳仍然是最大的不确定性,因为它的规模和有限的观察.

科学领域:

陆地生态系统科学地球生态系统科学
气候变化研究研究气候变化研究
碳循环建模的模型

背景情况:

准确的陆地碳库存对于生态系统的理解,气候变化跟踪和国际报告至关重要.
整合基于过程的模型,现场数据和遥感,比单个方法提供了更高的准确性.
越来越多的数据约束可能会导致跨空间,时间和过程的复杂相互作用.

研究的目的:

进行信息价值分析,评估不同的观察如何减少碳循环的不确定性.
评估数据同化系统中的协同数据约束的贡献.
确定改善陆地碳库存的见解.

主要方法:

使用基于区块的Tobit Gamma Ensemble过器进行数据同化.
综合MODIS叶面积指数,Landtrendr地表生物质,SMAP土壤水分和SoilGrids土壤有机碳数据.
在美国连续地区的39个国家生态观测网络站点 (2012-2021) 应用了SIPNET生态系统模型.

主要成果:

土壤和木材碳池对总碳不确定性贡献最大.
数据同化显著降低了直接受约束池中的不确定性和剩余错误.
通过观察,跨变量和位置共享信息,大大减少不确定性和错误.
间接约束突出了数据流的协同作用和冲突.

结论:

土壤碳仍然是陆地碳预算中不确定性的主要来源.
有效的数据融合策略可以提高碳库存的准确性和精度.
了解数据协同作用和冲突对于未来的碳监测工作至关重要.

关键词:

虹是什么意思虹模型-数据融合模型基于自然的气候解决方案基于过程的建模.远程传感是一种遥感技术.不确定性的传播.信息信息的价值信息的价值.

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