Modeling three-dimensional forest structures to drive canopy radiative transfer simulations of bidirectional reflectance factor
Modeling three-dimensional forest structures to drive canopy radiative transfer simulations of bidirectional reflectance factor
复制标题
DOI：
10.1080/17538947.2017.1353146
复制
发表时间：
2018-10
影响因子：
5.1
通讯作者：
Wei Yang;Hideki Kobayashi;Xuehong Chen;K. Nasahara;R. Suzuki;A. Kondoh
中科院分区：
文献类型：
--
作者：
Wei Yang;Hideki Kobayashi;Xuehong Chen;K. Nasahara;R. Suzuki;A. Kondoh
基于蒙特卡罗的三维辐射传输（MCRT）模型通常用于树冠辐射传输（RT）模拟相互比较的基准测试。然而，三维MCRT模型很少用于开发估算森林基本气候变量的遥感算法，主要原因是难以在区域到全球尺度上获得不同森林生物群落的真实林分结构。幸运的是，一些重要的树木结构参数，如冠层高度和树木密度分布已经在全球范围内可用。这使得可以运行3-D MCRT模型的中间复杂性。因此，我们开发了一种统计方法来生成具有中等复杂性的森林结构，这取决于冠层高度和树木密度的输入。它旨在促进三维MCRT模型的应用，以开发遥感检索算法。对提议的方法进行了评价，分别对爱沙尼亚和美国的两个北方林分进行了实地测量。结果表明，基于实测森林结构的双向反射系数（BRF）模拟结果与基于生成结构的双向反射系数（BRF）吻合较好，均方根误差（RMSE）和相对RMSE （rRMSE）分别在0.002 ~ 0.006和0.7% ~ 19.8%之间。将计算的BRF与相应的MODIS反射率数据进行比较，RMSE和rRMSE分别小于0.03%和20%。虽然目前的研究结果仅限于两个北方森林林分，但我们的方法有可能为不同的森林生物群系生成林分结构。
ABSTRACT Three-dimensional (3-D) Monte Carlo-based radiative transfer (MCRT) models are usually used for benchmarking in intercomparisons of the canopy radiative transfer (RT) simulations. However, the 3-D MCRT models are rarely applied to develop remote sensing algorithms to estimate essential climate variables of forests, due mainly to the difficulties in obtaining realistic stand structures for different forest biomes over regional to global scales. Fortunately, some of important tree structure parameters such as canopy height and tree density distribution have been available globally. This enables to run the intermediate complexities of the 3-D MCRT models. We consequently developed a statistical approach to generate forest structures with intermediate complexities depending on the inputs of canopy height and tree density. It aims at facilitating applications of the 3-D MCRT models to develop remote sensing retrieval algorithms. The proposed approach was evaluated using field measurements of two boreal forest stands at Estonia and USA, respectively. Results demonstrated that the simulations of bidirectional reflectance factor (BRF) based on the measured forest structures agreed well with the BRF based on the generated structures from the proposed approach with the root mean square error (RMSE) and relative RMSE (rRMSE) ranging from 0.002 to 0.006 and from 0.7% to 19.8%, respectively. Comparison of the computed BRF with corresponding MODIS reflectance data yielded RMSE and rRMSE lower than 0.03 and 20%, respectively. Although the results from the current study are limited in two boreal forest stands, our approach has the potential to generate stand structures for different forest biomes.