文献类型：期刊文献

英文题名：A Kernel-Driven BRDF Approach to Correct Airborne Hyperspectral Imagery over Forested Areas with Rugged Topography

作者：Jia, Wen[1] Pang, Yong[1] Tortini, Riccardo[2] Schlaepfer, Daniel[3] Li, Zengyuan[1] Roujean, Jean-Louis[4]

第一作者：荚文

通信作者：Pang, Y[1]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Univ Calif Los Angeles, Dept Geog, 1255 Bunche Hall, Los Angeles, CA 90095 USA;[3]ReSe Applicat LLC, Langeggweg 3, CH-9500 Wil Sg, Switzerland;[4]Univ Paul Sabatier, CNRS, IRD, INRA,Ctr Etud Spatiales BIOsphere CESBIO, F-31401 Toulouse 9, France

年份：2020

卷号：12

期号：3

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20201008275038);Scopus(收录号：2-s2.0-85080883485);WOS:【SCI-EXPANDED(收录号:WOS:000515393800091)】；

基金：This work was funded by the National Natural Science Foundation of China (31570546), Central Public-interest Scientific Institution Basal Research Found (No. CAFYBB2016ZD004) and the National High Technology Research and Development Program of China (No. 2012AA12A306).

语种：英文

外文关键词：airborne hyperspectral image; BRDF correction; rugged topography; kernel-driven; remote sensing; MODIS

摘要：Airborne hyper-spectral imaging has been proven to be an efficient means to provide new insights for the retrieval of biophysical variables. However, quantitative estimates of unbiased information derived from airborne hyperspectral measurements primarily require a correction of the anisotropic scattering properties of the land surface depicted by the bidirectional reflectance distribution function (BRDF). Hitherto, angular BRDF correction methods rarely combined viewing-illumination geometry and topographic information to achieve a comprehensive understanding and quantification of the BRDF effects. This is in particular the case for forested areas, frequently underlaid by rugged topography. This paper describes a method to correct the BRDF effects of airborne hyperspectral imagery over forested areas overlying rugged topography, referred in the reminder of the paper as rugged topography-BRDF (RT-BRDF) correction. The local viewing and illumination geometry are calculated for each pixel based on the characteristics of the airborne scanner and the local topography, and these two variables are used to adapt the Ross-Thick-Maignan and Li-Transit-Reciprocal kernels in the case of rugged topography. The new BRDF model is fitted to the anisotropy of multi-line airborne hyperspectral data. The number of pixels is set at 35,000 in this study, based on a stratified random sampling method to ensure a comprehensive coverage of the viewing and illumination angles and to minimize the fitting error of the BRDF model for all bands. Based on multi-line airborne hyperspectral data acquired with the Chinese Academy of Forestry's LiDAR, CCD, and Hyperspectral system (CAF-LiCHy) in the Pu'er region (China), the results applying the RT-BRDF correction are compared with results from current empirical (C, and sun-canopy-sensor (SCS) adds C (SCS+C)) and semi-physical (SCS) topographic correction methods. Both quantitative assessment and visual inspection indicate that RT-BRDF, C, and SCS+C correction methods all reduce the topographic effects. However, the RT-BRDF method appears more efficient in reducing the variability in reflectance of overlapping areas in multiple flight-lines, with the advantage of reducing the BRDF effects caused by the combination of wide field of view (FOV) airborne scanner, rugged topography, and varying solar illumination angle over long flight time. Specifically, the average decrease in coefficient of variation (CV) is 3% and 3.5% for coniferous forest and broadleaved forest, respectively. This improvement is particularly marked in the near infrared (NIR) region (i.e., >750 nm). This finding opens new possible applications of airborne hyperspectral surveys over large areas.