发表机构
The University of Sydney; Helmholtz Centre for Environmental Research–UFZ; Leipzig University; City University of Hong Kong; University of California, Berkeley; Lawrence Berkeley National Laboratory（悉尼大学; 亥姆霍兹环境研究中心; 莱比锡大学; 香港城市大学; 加州大学伯克利分校; 劳伦斯伯克利国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究构建框架分析EOFM在生态水文学中的应用，发现其与生态水文学需求存在不匹配，推动建立过程感知框架以支撑对水、能量与碳耦合动态的可信监测与解释。
AI 中文摘要
地球观测基础模型（Earth observation foundation models, EOFMs）作为可复用的表示框架，正兴起于生态水文学领域，用于数据驱动的检索、预测与过程建模，整合地球观测（EO）、气象强迫数据及过程模型，刻画不同尺度下植被与土壤中水分、能量与碳的耦合动态。然而，目前尚无针对生态水文学的综合研究，评估在参考数据不确定、尺度不匹配及时间依赖条件下，EOFM的相关性、应用证据或评估要求。本文构建了确定EOFM何时支持可解释推理的框架，并指出EOFM与生态水文学需求间存在不匹配：其一，观测到推理的层级显示，相关性取决于目标特定的传感路径、时空支撑及可追溯的不确定性；其二，元分析表明，预训练以反射光学与主动微波数据为主，热覆盖稀疏，无被动微波发射源；其三，对生态水文学应用的综合分析发现，空间上下文、标签高效适配及混合工作流的支撑最强，随推理深度增加证据减少，通量、耦合动态、事件轨迹、校准不确定性及决策效益的独立验证仍较匮乏；其四，基准审计显示，通用EOFM套件在公平适配与可复现性上覆盖更强，生态水文学评估在过程目标、直接参考证据及分布偏移上覆盖更强，物理一致性与不确定性评估仍较薄弱。这些发现推动构建过程感知框架，使EOFM的设计与评估匹配目标变量、观测路径及过程时间尺度，支撑对水分、能量与碳耦合动态的可信监测与解释。
英文摘要
Earth observation foundation models (EOFMs) are emerging as reusable representation frameworks for data-driven retrieval, prediction and process modelling within ecohydrology, which integrate EO, meteorological forcing and process models to characterise coupled water, energy and carbon dynamics in vegetation and soil across scales. However, there is yet to be an ecohydrology-specific synthesis assessing the EOFM relevance, application evidence or evaluation requirements under uncertain reference data, scale mismatch and temporal dependence. Here, we develop a framework for determining when EOFMs support interpretable inference and identify a mismatch between EOFMs and ecohydrological requirements. Firstly, an observation-to-inference hierarchy shows that relevance depends on target-specific sensing pathways, spatial-temporal support and traceable uncertainty. Secondly, a meta-analysis shows that pretraining is dominated by reflected optical and active-microwave data, with sparse thermal coverage and no passive-microwave-emission sources. Thirdly, our synthesis of ecohydrological applications finds strongest support for spatial context, label-efficient adaptation and hybrid workflows. Evidence declines with inference depth; independent validation of fluxes, coupled dynamics, event trajectories, calibrated uncertainty and decision benefits remains sparse. Fourthly, our benchmark audit finds stronger coverage of fair adaptation and reproducibility in general EOFM suites, and of process targets, direct reference evidence and distribution shifts in ecohydrological evaluations; physical consistency and uncertainty remain weakly assessed. These findings motivate a process-aware framework aligning EOFM design and evaluation with the target variable, observation pathway and process timescale, supporting trustworthy monitoring and interpretation of coupled water, energy and carbon dynamics.
