Retrieval of canopy biophysical variables from bidirectional reflectance -: Using prior information to solve the ill-posed inverse problem
Retrieval of canopy biophysical variables from bidirectional reflectance -: Using prior information to solve the ill-posed inverse problem
复制标题
DOI：
10.1016/s0034-4257(02)00035-4
复制
发表时间：
2003-01-01
影响因子：
13.5
通讯作者：
Wang, L
中科院分区：
文献类型：
--
作者：
Combal, B;Baret, F;Wang, L
研究了利用辐射传输模型反演从遥感数据中提取冠层生物物理变量的方法。测量和模型的不确定性使反问题变得不适定，导致求解过程中的困难和不准确。这项研究的重点是利用先验信息来减少辐射传输模型反演过程中与冠层生物物理变量估计相关的不确定性。为此，采用了查找表(LUT)、拟牛顿算法(QNT)和神经网络(NNT)求逆技术来考虑先验信息。通过模拟反射率数据集对结果进行评估，这些数据集允许对测量和模型不确定性的影响进行详细分析。结果表明，先验信息的使用显著改善了冠层生物物理变量的估计。LUT和QNT对模型不确定性很敏感。相反，NNT技术通常不太准确。然而，在我们的条件下，其精度几乎不显著地依赖于建模或测量误差。我们还观察到，由于校准错误而导致的反射率测量中的偏差并不会对生物物理估计的准确性产生太大影响。(C)2002 Elsevier Science Inc.保留所有权利。
Estimation of canopy biophysical variables from remote sensing data was investigated using radiative transfer model inversion. Measurement and model uncertainties make the inverse problem ill posed, inducing difficulties and inaccuracies in the search for the solution. This study focuses on the use of prior information to reduce the uncertainties associated to the estimation of canopy biophysical variables in the radiative transfer model inversion process. For this purpose, lookup table (LUT), quasi-Newton algorithm (QNT), and neural network (NNT) inversion techniques were adapted to account for prior information. Results were evaluated over simulated reflectance data sets that allow a detailed analysis of the effect of measurement and model uncertainties. Results demonstrate that the use of prior information significantly improves canopy biophysical variables estimation. LUT and QNT are sensitive to model uncertainties. Conversely, NNT techniques are generally less accurate. However, in our conditions, its accuracy is little dependent significantly on modeling or measurement error. We also observed that bias in the reflectance measurements due to miscalibration did not impact very much the accuracy of biophysical estimation. (C) 2002 Elsevier Science Inc. All rights reserved.