Seasonal changes of the relationships between photochemical reflectance index and light use efficiency in broadleaf forest

• Published in: 2010 18th International Conference on Geoinformatics pp. 1 - 4
• Main Authors: Zhaocong Wu, Yuanyuan Deng, Xiaolei Yu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2010
• Subjects: Biological system modeling; Correlation; correlation analysis; Ecosystems; Indexes; light use efficiency; MODIS; photochemical reflectance index; Poles and towers; Reflectivity; seasonal changes
• ISBN: 1424473012; 9781424473014
• ISSN: 2161-024X
• DOI: 10.1109/GEOINFORMATICS.2010.5567837

In current net primary production (NPP) models based on light use efficiency (LUE), the realized LUE is often estimated by considering changes of environment conditions such as temperature and precipitation to compute stress related restrictions to the potential LUE. The accuracy of this approach however, is limited by a lack of detailed knowledge of stressors at landscape and global scales. As an alternative to determine the realized LUE from environmental stresses, the photochemical reflectance index (PRI) introduced by Gamon shows strong correlation with LUE at leaf, canopy and region levels. In this paper, the relationships between ecosystem-level LUE obtained from an eddy covariance flux tower and MODIS-derived photochemical reflectance index (PRI) were investigated in Morgan-Monroe State Forest. We tried to detect seasonal changes of their relationships throughout the growing season from June to October in 2004. Only the backscatter reflectance data which was demonstrated to have strong correlation with PRI in cloud-free days were picked for our research. All statistical analysis was conducted in MATLAB (version 7.1) software to establish linear regression model of the PRI-LUE relationship. The monthly correlation showed notable variations in the four months except July in which there was not enough valid data for cloud coverage. In June the correlation was negative. For the following two months the values of LUE and PRI became positively correlated and the slope of the regression line increased obviously. Then in late October the slope of the positive relation decreased to below that in August. Potential environmental factors were analyzed to explain the seasonal variations of PRI-LUE relationships. To validate the effectiveness of the line regression model, ground micrometeorological data of the same tower site in 2005 were used to calculate ground measured LUE. We compared the predicted LUE from MODIS data using PRI-LUE relationship in August of 2004 with ground measured LUE and found the line model successfully predicted the true realized LUE.