The latest Geostationary (GEO) Operational Environmental Satellite-16 (GOES-16) equipped with Advanced Baseline Imager (ABI) has comparable spectral and spatial resolution as low earth orbiting (LEO) sensors [i.e., the Moderate Resolution Imaging Spectroradiometer (MODIS)], but with up-to-the-minute image acquisition capability. This enables greater opportunities to generate two essential climate variables—Leaf area index (LAI) and the fraction of photosynthetically active radiation (FPAR) absorbed by vegetation with more cloud-free observations and at much higher frequency. The improved GEO LAI/FPAR products will increase the capacity for monitoring highly dynamic ecosystems in a timely manner. However, the radiative transfer (RT)-based MODIS operational algorithm cannot be directly applied to GOES-16 ABI data due to different sensor characteristics. Fortunately, it has been shown theoretically and practically, that the RT-based algorithm can be transplanted to any other optical sensors by optimizing the sensor-specific parameters—the single scattering albedo (SSA) and relative stabilized precision (RSP). We built the RT-based ABI-specific lookup tables (LUTs) using a global optimizing algorithm (SCE-UA) that can quickly find the optimal solution. SCE-UA optimizes the SSAs and RSPs in the LUTs by minimizing the difference between ABI and MODIS retrievals and maximizing the main algorithm execution rate. Our efforts indicate that these strategies of parametric optimization is able to decrease the discrepancy between the ABI and MODIS LAI/FPAR products. Comprehensive evaluations were conducted to evaluate ABI retrievals. These indirect inter-comparisons suggest a spatiotemporal consistency between ABI and the benchmark MODIS products, while direct validation with field measurements increases confidence in their accuracy. The proposed approach is applicable to any other optical sensors for LAI/FPAR estimation, especially, GEO sensors (i.e., Himawari-8, Geo-KOMPSAT-2A, FengYun-4 etc.).

中文翻译：

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使用全局优化算法从 GOES-16 高级基线成像仪数据中检索 LAI 和 FPAR 的原型

配备高级基线成像仪 (ABI) 的最新地球静止 (GEO) 运行环境卫星 16 (GOES-16) 具有与低地球轨道 (LEO) 传感器相当的光谱和空间分辨率 [即中分辨率成像光谱仪 (MODIS)] ，但具有最新的图像采集能力。这使得有更多的机会产生两个基本气候变量——叶面积指数 (LAI) 和植被吸收的光合有效辐射分数 (FPAR)，具有更多的无云观测和更高的频率。改进后的 GEO LAI/FPAR 产品将提高及时监测高度动态生态系统的能力。然而，由于不同的传感器特性，基于辐射传输 (RT) 的 MODIS 操作算法不能直接应用于 GOES-16 ABI 数据。幸运的是，理论和实践都表明，通过优化传感器特定参数——单散射反照率 (SSA) 和相对稳定精度 (RSP)，基于 RT 的算法可以移植到任何其他光学传感器。我们使用全局优化算法 (SCE-UA) 构建了基于 RT 的 ABI 特定查找表 (LUT)，该算法可以快速找到最佳解决方案。SCE-UA 通过最小化 ABI 和 MODIS 检索之间的差异并最大化主算法执行率来优化 LUT 中的 SSA 和 RSP。我们的努力表明，这些参数优化策略能够减少 ABI 和 MODIS LAI/FPAR 产品之间的差异。进行了综合评估以评估 ABI 检索。这些间接的相互比较表明 ABI 和基准 MODIS 产品之间的时空一致性，而现场测量的直接验证增加了对其准确性的信心。所提出的方法适用于任何其他用于 LAI/FPAR 估计的光学传感器，尤其是 GEO 传感器（即 Himawari-8、Geo-KOMPSAT-2A、FengYun-4 等）。而现场测量的直接验证增加了对其准确性的信心。所提出的方法适用于任何其他用于 LAI/FPAR 估计的光学传感器，尤其是 GEO 传感器（即 Himawari-8、Geo-KOMPSAT-2A、FengYun-4 等）。而现场测量的直接验证增加了对其准确性的信心。所提出的方法适用于任何其他用于 LAI/FPAR 估计的光学传感器，尤其是 GEO 传感器（即 Himawari-8、Geo-KOMPSAT-2A、FengYun-4 等）。