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

Remote Sensing

Jing Zhao¹  Jing Li¹  Qinhuo Liu¹  Wenjie Fan³  Bo Zhong¹  Shanlong Wu¹  Le Yang¹  Yelu Zeng¹  Baodong Xu¹  Gaofei Yin¹  Xin Li²  Yuei-An Liou²  Alfredo R. Huete² 

[1] State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; E-Mails:State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;;Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China; E-Mail:

关键词: multi-sensor dataset;    the middle reach of the Heihe River Basin;    leaf area index;    HJ1/CCD;    Landsat8/OLI;   

DOI  :  10.3390/rs70606862

来源: mdpi

【 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 (R²) 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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.