Leaf Area Index Retrieval Combining HJ1/CCD and Landsat8/OLI Data in the Heihe River Basin, China

作者:Zhao, J (Zhao, Jing)[ 1,2 ] ; Li, J (Li, Jing)[ 1,2 ] ; Liu, QH (Liu, Qinhuo)[ 1,2 ] ; Fan, WJ (Fan, Wenjie)[ 3 ] ; Zhong, B (Zhong, Bo)[ 1,2 ] ; Wu, SL (Wu, Shanlong)[ 1,2 ] ; Yang, L (Yang, Le)[ 1,2 ] ; Zeng, YL (Zeng, Yelu)[ 1,2,4 ] ; Xu, BD (Xu, Baodong)[ 1,2,4 ] ; Yin, GF (Yin, Gaofei)[ 1,2,4 ]

REMOTE SENSING

卷: 7  期: 6  页: 6862-6885

DOI: 10.3390/rs70606862

出版年: JUN 2015

摘要

The primary restriction on high resolution remote sensing data is the limit observation frequency. Using a network of multiple sensors is an efficient approach to increase the observations in a specific period. This study explores a leaf area index (LAI) inversion method based on a 30 m multi-sensor dataset generated from HJ1/CCD and Landsat8/OLI, from June to August 2013 in the middle reach of the Heihe River Basin, China. The characteristics of the multi-sensor dataset, including the percentage of valid observations, the distribution of observation angles and the variation between different sensor observations, were analyzed. To reduce the possible discrepancy between different satellite sensors on LAI inversion, a quality control system for the observations was designed. LAI is retrieved from the high quality of single-sensor observations based on a look-up table constructed by a unified model. The averaged LAI inversion over a 10-day period is set as the synthetic LAI value. The percentage of valid LAI inversions increases significantly from 6.4% to 49.7% for single-sensors to 75.9% for multi-sensors. LAI retrieved from the multi-sensor dataset show good agreement with the field measurements. The correlation coefficient (R-2) is 0.90, and the average root mean square error (RMSE) is 0.42. The network of multiple sensors with 30 m spatial resolution can generate LAI products with reasonable accuracy and meaningful temporal resolution.

通讯作者地址: Li, J (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 Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

       [ 2 ] Joint Ctr Global Change Studies, Beijing 100875, Peoples R China

       [ 3 ] Peking Univ, Inst Remote Sensing & GIS, Beijing 100871, Peoples R China

       [ 4 ] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China