Earth Observation Foundation Models for Terrestrial Ecohydrology: From Representation Learning to Process Inference

2026-08-15

AI Summary

This study addresses scale mismatches and insufficient inference credibility in Earth observation foundation models for ecohydrology by proposing a process-aware framework that integrates multi-source remote sensing with process-based models to establish an observation-inference hierarchy. Through meta-analysis and benchmark auditing to identify model gaps, the research employs hybrid workflows and label-efficient adaptation techniques for multi-scale representation learning. The findings delineate model applicability boundaries, revealing pre-training data biases and critical spectral band deficiencies. Furthermore, the work validates the effectiveness of spatial context and hybrid processing pipelines, significantly enhancing both the credibility and interpretability of monitoring coupled water-energy-carbon dynamics.

Abstract

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.

Problem

• Earth Observation Foundation Models
• Ecohydrology
• Process Inference
• Scale Mismatch
• Uncertainty

Innovation

• Earth Observation Foundation Models
• Process-aware Framework
• Ecohydrology
• Representation Learning
• Uncertainty Quantification