用物理引导的元学习，跨区域精准反演近海生物地球化学参数。
Retrieval of Coastal Biogeochemical Parameters From Near-Surface Hyperspectral Remote Sensing Reflectance Using Physics-Aware Meta-Learning
- 先用物理模型生成合成数据预训练通用模型，再用本地数据微调。
- 在澳大利亚5个不同海域测试，反演精度优于5种基准模型。
- 适合需要跨区域水体监测的环境科研与管理部门使用。
高光谱原位观测在低成本监测近海水质方面展现出潜力，可反演总悬浮物、溶解有机碳和总叶绿素-a等生物地球化学（BGC）参数。然而，由于不同区域环境条件和生物地球化学差异导致的BGC范围与光学特性变化，使得算法难以跨水域泛化。本文提出一种两阶段物理感知元学习框架：第一阶段基于澳大利亚近海生物光学光谱库，利用生物光学正向模型生成大规模合成数据集，预训练一个不依赖特定区域的通用基模型，使其学习基本物理关系；第二阶段用本地样本对基模型进行微调。研究收集了澳大利亚五个地理各异近海站点的原位高光谱反射率（Rrs）与BGC参数数据。实验结果表明：(1) 各站点的BGC参数及其对应的高光谱Rrs特征存在明显区域差异；(2) 合成数据在参数分布和参数间相关性上均与真实样本高度一致，具备物理合理性；(3) 该方法在所有测试站点的BGC反演精度均优于五种基准模型；(4) 原位测量值与模型预测值在量级和时间动态上高度吻合。
原文摘要 · Abstract (English)
Hyperspectral in situ sensing has shown promise in retrieving aquatic biogeochemical (BGC) parameters, such as total suspended solids, dissolved organic carbon, and total chlorophyll-a, for cost-effective monitoring of coastal water quality. However, generalising such retrieval algorithms across water bodies remains challenging, as the relationship between remote sensing reflectance (Rrs) and BGC parameters can vary considerably from one region to another due to regional distinctions in environmental conditions and biogeochemistry that lead to different BGC ranges and bio-optical properties. In this study, we propose a two-stage physics-aware meta-learning framework for retrieving coastal BGC parameters from near-surface Rrs observations. In the first stage, a bio-optical forward model is used to generate a large synthetic dataset based on an in situ bio-optical spectral library with broad representativeness of Australian coastal waters. This dataset is then used to pretrain a region-agnostic base model with meta-learning, allowing the model to learn fundamental physical relationships. In the second stage, the pretrained base model is fine-tuned for specific regions with local samples. We collected in situ hyperspectral Rrs and BGC measurements from five geographically distinct sites in Australian coastal waters. Our experimental results suggest: (1) the BGC parameters and their corresponding hyperspectral Rrs signatures exhibited clear regional distinctions among the experimental sites; (2) the synthetic dataset was physically plausible and closely aligned with real-world samples in both parameter distributions and inter-parameter correlations; (3) the proposed approach outperformed five benchmark models in BGC retrieval; and (4) time series of in situ measured and model-predicted BGC parameters showed good agreement in both magnitude and temporal dynamics.
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