摘要：研究气候变化需要降低 CO2 和 CH4 排放估算的不确定性，从而更好地区分人为源与自然源；这推动了对具备更高重访频率和更大空间覆盖范围的天基观测的需求。在此背景下，Horizon Europe 的 SCARBOn 项目正在评估一种低成本卫星星座，其核心传感器为 NanoCarb 成像干涉仪，用于监测大气中的 CO2 和 CH4 排放。然而，以高重访频率和大空间覆盖范围估算 CO2 和 CH4 浓度面临重大挑战：常用的全物理反演算法依赖重复进行高分辨率辐射传输（RT）模拟，而使用逐线 RT 模型时，这类模拟的计算成本很高。作为替代方案，本研究提出了一种前馈多层感知机（MLP）代理模型，用于准确、高效地预测 CO2 弱吸收带的大气层顶辐亮度。该模型对辐亮度和 RT Jacobian 采用联合平均绝对误差（MAE）损失，以同时保留光谱精度及其对地球物理参数的敏感性。将基于 MLP 的 RT 代理模型与 NanoCarb 仪器响应相耦合，可为 NanoCarb 测量构建高效且精确的正演模型，并在 CO2 浓度反演中展现出良好前景。

Abstract:Studying climate change requires reducing uncertainties in CO2 and CH4 emission estimates to better distinguish anthropogenic from natural sources, which motivates spaceborne measurements with improved revisit frequency and spatial coverage. In this context, the Horizon Europe SCARBOn project assesses a low-cost satellite constellation featuring the NanoCarb imaging interferometer as its core sensor for monitoring CO2 and CH4 emissions in the atmosphere. However, estimating CO2 and CH4 concentrations with high revisit and spatial coverage poses significant challenges: full-physics retrieval algorithms commonly used rely on repeated high-resolution radiative transfer (RT) simulations, which are computationally expensive when using line-by-line RT models. As an alternative, we propose in this study a feedforward multilayer perceptron (MLP) surrogate designed to accurately and efficiently predict top-of-atmosphere radiances in the CO2 weak band, using a combined mean absolute error (MAE) loss on radiances and RT Jacobians to preserve both spectral accuracy and sensitivity to geophysical parameters. Coupling the MLP-based RT surrogate with the NanoCarb instrumental response yields an efficient and precise forward model for NanoCarb measurements, which shows promising results for CO2 concentration retrieval.