作者:Yin, GF (Yin, Gaofei)[ 1 ] ; Li, J (Li, Jing)[ 2,3 ] ; Liu, QH (Liu, Qinhuo)[ 2,3 ] ; Zhong, B (Zhong, Bo)[ 2 ] ; Li, AN (Li, Ainong)[ 1 ]

REMOTE SENSING LETTERS

卷: 7  期: 8  页: 771-780

DOI: 10.1080/2150704X.2016.1182657

出版年: AUG 2016

摘要

Spatio-temporally continuous leaf area index (LAI) is required for surface process simulation, climate modelling and global change study. As a result of cloud contamination and other factors, the current LAI products are spatially and temporally discontinuous. A multi-sensor integration method was proposed in this paper to combine Terra-Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua-MODIS, FY (FengYun) 3A-MEdium Resolution Spectrum Imager (MERSI) and FY3B-MERSI data to improve LAI spatio-temporal continuity. It consists of a normalization algorithm to eliminate the difference between MODIS and MERSI data in spatial and spectral aspects, a daily LAI retrieval algorithm based on neural networks and a maximum value compositing algorithm. The feasibility of our LAI retrieval method to improve continuity was assessed at national scale (in China). Results show that (1) the combination of multi-sensor data can significantly improve LAI temporal continuity, especially for mountainous regions which are characterized by high frequency of cloud coverage; (2) the improvement in spatial continuity is obvious as can be seen from the increase of retrieval ratio, defined as the ratio of the number of retrieved pixels to the total number of pixels, from 0.78 for GEOV1 LAI product, and 0.88 for MOD15A2 LAI product to 0.98 for multi-sensor LAI product.

通讯作者地址: Li, J; Liu, QH (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu, Peoples R China

[ 2 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

 [ 3 ] JCGCS, Beijing, Peoples R China