仿真作为采样Radiative Transfer码插值的精确替代
Emulation as an Accurate Alternative to Interpolation in Sampling Radiative Transfer Codes
摘要：
计算上昂贵的辐射传输模型（RTMs）被广泛用于真实地再现光与地球表面和大气的相互作用。由于这些模型需要较长的处理时间，通常的做法是首先生成稀疏查找表（LUT），然后利用插值方法对多维LUT输入变量空间进行采样。然而，常见的插值方法是否执行最准确的问题出现。作为插值的替代方案，这项工作提出使用仿真，即，通过统计学习来近似RTM输出。进行了两个实验，以评估使用插值和仿真提供光谱输出的准确性：（1）在冠层水平，使用PROSAIL;（2）在大气层顶部水平，使用MODTRAN。各种插值（最近邻，反距离加权，分段线性）和仿真（高斯过程回归（GPR），核岭回归，神经网络）的方法进行了评估对密集的参考LUT。在所有的实验中，仿真方法显然比经典插值方法产生更准确的输出光谱。GPR仿真的精度比最佳插值方法高出10倍，并且速度与更快的插值方法相比具有竞争力。它的结论是，仿真可以作为一个快速和更准确的替代常用的插值方法重建RTM光谱数据。
英文摘要：
Computationally expensive Radiative Transfer Models (RTMs) are widely used} to realistically reproduce the light interaction with the Earth surface and atmosphere. Because these models take long processing time, the common practice is to first generate a sparse look-up table (LUT) and then make use of interpolation methods to sample the multi-dimensional LUT input variable space. However, the question arise whether common interpolation methods perform most accurate. As an alternative to interpolation, this work proposes to use emulation, i.e., approximating the RTM output by means of statistical learning. Two experiments were conducted to assess the accuracy in delivering spectral outputs using interpolation and emulation: (1) at canopy level, using PROSAIL; and (2) at top-of-atmosphere level, using MODTRAN. Various interpolation (nearest-neighbour, inverse distance weighting, piece-wice linear) and emulation (Gaussian process regression (GPR), kernel ridge regression, neural networks) methods were evaluated against a dense reference LUT. In all experiments, the emulation methods clearly produced more accurate output spectra than classical interpolation methods. GPR emulation performed up to ten times more accurately than the best performing interpolation method, and this with a speed that is competitive with the faster interpolation methods. It is concluded that emulation can function as a fast and more accurate alternative to commonly used interpolation methods for reconstructing RTM spectral data.
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