作者:Wu, QL (Wu, Qiaoli)[ 1,2,3 ] ; Song, CH (Song, Conghe)[ 4 ] ; Song, JL (Song, Jinling)[ 1,2,3 ] ; Wang, JD (Wang, Jindi)[ 1,2,3 ] ; Chen, SY (Chen, Shaoyuan)[ 1,2,3 ] ; Yu, B (Yu, Bo)[ 1,2,3 ]

REMOTE SENSING

卷: 10  期: 2

文献号: 262

DOI: 10.3390/rs10020262

出版年: FEB 2018

文献类型:Article

摘要

Significant gaps exist in our knowledge of the impact of leaf aging on canopy signal variability, which limits our understanding of vegetation status based on remotely sensed data. To understand the effects of leaf aging at the leaf and canopy scales, a combination of field, remote-sensing and physical modeling techniques was adopted to assess the canopy spectral signals of evergreen Cunninghamia forests. We observed an approximately 10% increase in Near-Infrared (NIR) reflectance for new leaves and a 35% increase in NIR transmittance for mature leaves from May to October. When variations in leaf optical properties (LOPs) of only mature leaves, or both new and mature leaves were considered, the Geometric Optical and Radiative Transfer (GORT) model-simulated canopy reflectance trajectory was more consistent with Landsat observations (R-2 increased from 0.37 to 0.82 similar to 0.89 for NIR reflectance, and from 0.35 to 0.67 similar to 0.88 for EVI2, with a small RMSE (0.01 to 0.02)). This study highlights the importance of leaf age on leaf spectral signatures, and provides evidence of age-dependent LOPs that have important impacts on canopy reflectance in the NIR band and EVI2, which are used to monitor canopy dynamics and productivity, with important implications for RS and forest ecosystem ecology.

通讯作者地址: Song, JL (通讯作者)

Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

通讯作者地址: Song, JL (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100875, Peoples R China.

通讯作者地址: Song, JL (通讯作者)

Beijing Normal Univ, Inst Remote Sensing Sci & Engn, Beijing Engn Res Ctr Global Land Remote Sensing P, Fac Geog Sci, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100875, Peoples R China

[ 3 ] Beijing Normal Univ, Inst Remote Sensing Sci & Engn, Beijing Engn Res Ctr Global Land Remote Sensing P, Fac Geog Sci, Beijing 100875, Peoples R China

[ 4 ] Univ North Carolina Chapel Hill, Dept Geog, Chapel Hill, NC 27599 USA