董亚东等：Influence of Snow on the Magnitude and Seasonal Variation of the Clumping Index Retrieved from MODIS BRDF Products

作者:Dong, YD (Dong, Yadong)[ 1,2 ] ; Jiao, ZT (Jiao, Ziti)[ 1,3 ] ; Yin, SY (Yin, Siyang)[ 1,3 ] ; Zhang, H (Zhang, Hu)[ 4 ] ; Zhang, XN (Zhang, Xiaoning)[ 1,3 ] ; Cui, L (Cui, Lei)[ 1,3 ] ; He, DD (He, Dandan)[ 1,3 ] ; Ding, AX (Ding, Anxin)[ 1,3 ] ; Chang, YX (Chang, Yaxuan)[ 1,3 ] ; Yang, ST (Yang, Shengtian)[ 2 ]

REMOTE SENSING

卷: 10  期: 8

文献号: 1194

DOI: 10.3390/rs10081194

出版年:AUG 2018

摘要

The foliage Clumping Index (CI) is an important vegetation structure parameter that allows for the accurate separation of sunlit and shaded leaves in a canopy. The CI and its seasonality are critical for global Leaf Area Index (LAI) estimating and ecological modelling. However, the cover of snow tends to reduce the reflectance anisotropy of the vegetation canopy and thus probably influences CI estimates. In this paper, we investigate the influence of snow on the magnitude and seasonal variation of the CI retrieved from Moderate-resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) products based on field-measured CI and statistics from the global MODIS CI product. We find that the backup algorithm can effectively correct abnormally large CI values and obtain more reasonable CI retrievals than the main algorithm without any constraints in snow-covered areas. Validation indicates that the time-series CI product shows the potential in investigating the trajectories of the clumping effect in snow seasons. For evergreen forests, the clumping effect is relatively stable throughout the year; however, for deciduous vegetation types, CI values tend to display significant seasonal variations. This study suggests that the latest version of the global MODIS CI product, in which the backup algorithm is used to process the snow-covered pixels, has improved accuracy for CI retrievals in snow-covered areas and thus is probably more suitable as the input parameter for ecological and meteorological models.

通讯作者地址: Jiao, ZT (通讯作者)

Chinese Acad Sci, State Key Lab Remote Sensing Sci, Jointly Sponsored Beijing Normal Univ & Inst Remo, Beijing 100875, Peoples R China.

通讯作者地址: Jiao, ZT (通讯作者)

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

地址:

[ 1 ] Chinese Acad Sci, State Key Lab Remote Sensing Sci, Jointly Sponsored Beijing Normal Univ & Inst Remo, Beijing 100875, Peoples R China

[ 2 ] Beijing Normal Univ, Coll Water Sci, Beijing 100875, Peoples R China

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

[ 4 ] Tianjin Normal Univ, Coll Urban & Environm Sci, Tianjin 300387, Peoples R China