Abstract The objective of atmospheric correction is to retrieve surface reflectance from the top of atmosphere (TOA) reflectance. However, estimating surface reflectance from the TOA reflectance satellite data requires knowledge about the state of the atmosphere (e.g., water vapor and ozone) and the contribution of aerosols to the atmospheric path radiance. Thus, obtaining precise measurements of these parameters, which is very difficult, is crucial for accurate estimation of surface reflectance. The SREM (Simplified and Robust Surface Reflectance Estimation Method) is a physical-based atmospheric correction method based on the Radiative transfer (RT) equations of the second simulation of the Satellite Signal in Solar Spectrum (6SV). Essentially the SREM is a simplified version of 6SV which does not require Aerosol Optical Depth (AOD), aerosol type, water vapor, and ozone. An initial study showed accuracy comparable to the Landsat operational Surface Reflectance Products (SRProd) which is generated through different RT models using AOD, water vapor, and ozone data. To further validate the SREM under varying atmospheric conditions and at different spatial resolutions, an independent Reference Surface Reflectance (SRRef) dataset was generated using the AERONET (Aerosol Robotic Network) measurements as input to the 6SV RT model. The surface reflectances estimated by SREM (SRSREM) and SRProd from Planet Scope (PS, at 3 m spatial resolution), Sentinel-2 AB (S2AB) Multi-spectral Instrument (MSI, at 10 to 60 m spatial resolution), and Landsat-8 (L8) operational Land Imager (OLI, at 30 m spatial resolution) were validated against SRRef. Results showed that SRSREM performed similar to the SRProd of PS, S2AB MSI, and L8 OLI against SRRef. An inferior performance (R of 0.35 and 0.57) of L8 OLI's SRProd in the coastal blue (SB1) and blue (SB2) bands was observed, compared to SREM. The comparison of SRSREM with SRProd reveals the robustness of SREM, without using AOD, water vapor, and ozone data, for estimation of surface reflectance for all RT models tested. For some dates, SRRef and the SRProd under-corrected and produced higher values than the TOA reflectance, even when the atmosphere was clear but this was not the case for SREM. Analysis of surface reflectance estimation in shadowed areas revealed that the SRRef and SRProd had mainly negative values in coastal blue and blue bands for L8 OLI, while no negative SR value was observed for SREM in any band. These results recommend the utilization of SREM for the provision of surface reflectance products across a range of sensors

中文翻译：

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低分辨率到高分辨率卫星遥感数据大气校正方法的评价

摘要 大气改正的目的是从大气顶部反射率（TOA）反演地表反射率。然而，根据 TOA 反射率卫星数据估算表面反射率需要了解大气状态（例如，水蒸气和臭氧）以及气溶胶对大气路径辐射的贡献。因此，获得这些参数的精确测量非常困难，对于准确估计表面反射率至关重要。SREM（Simplified and Robust Surface Reflectance Estimation Method）是一种基于物理的大气校正方法，它基于太阳光谱（6SV）中卫星信号第二次模拟的辐射传递（RT）方程。本质上，SREM 是 6SV 的简化版本，它不需要气溶胶光学深度 (AOD)，气溶胶类型、水蒸气和臭氧。一项初步研究表明，其精度可与 Landsat 业务表面反射产品 (SRProd) 相媲美，后者是通过使用 AOD、水蒸气和臭氧数据的不同 RT 模型生成的。为了在不同的大气条件和不同的空间分辨率下进一步验证 SREM，使用 AERONET（气溶胶机器人网络）测量结果作为 6SV RT 模型的输入生成了一个独立的参考表面反射 (SRRef) 数据集。SREM (SRSREM) 和 SRProd 从 Planet Scope（PS，3 m 空间分辨率）、Sentinel-2 AB (S2AB) 多光谱仪器（MSI，10 至 60 m 空间分辨率）和 Landsat- 8 (L8) 操作陆地成像仪（OLI，空间分辨率为 30 m）已针对 SRRef 进行了验证。结果表明，SRSREM 的性能类似于 PS、S2AB MSI 和 L8 OLI 对 SRRef 的 SRProd。与 SREM 相比，观察到 L8 OLI 的 SRProd 在沿海蓝色 (SB1) 和蓝色 (SB2) 波段的性能较差（R 为 0.35 和 0.57）。SRSREM 与 SRProd 的比较揭示了 SREM 的稳健性，不使用 AOD、水蒸气和臭氧数据，用于估计所有测试的 RT 模型的表面反射率。对于某些日期，SRRef 和 SRProd 校正不足并产生比 TOA 反射率更高的值，即使在大气清晰的情况下也是如此，但 SREM 的情况并非如此。阴影区域的表面反射率估计分析表明，对于 L8 OLI，SRRef 和 SRProd 主要在沿海蓝色和蓝色波段具有负值，而在任何波段中都没有观察到 SREM 的负 SR 值。