The leaf area index (LAI) indicates the amount of leaf area per unit of ground area and the clumping index (CI) describes the spatial distribution of leaves in the canopy.
When leaves are randomly distributed, the CI equals to 1.0. When leaves are clustered, the CI is less than 1.0. Most of the leaves in nature show an aggregated distribution with CI less than 1.0. Both LAI and CI are important parameters in vegetation photosynthesis and evapotranspiration models. They play an important role in material exchange and energy flow between soil, vegetation, and atmosphere.
An accurate estimate of the canopy LAI and CI is vital for ecology, hydrology, carbon and nutrient cycling, and global change studies. However, due to the limitation of observational methods, there has been a lack of understanding on the seasonal variation and the relationship between the LAI and CI.
Moreover, previous LAI and CI measurement methods have largely relied on handheld optical instruments, which are labor intensive and impractical for long-term measurements.
Using an automatic instrument and two smartphone applications, Prof. FANG Hongliang and his team, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences carried out a seasonal LAI and CI measurements over maize, soybean, and sorghum fields in northeast China in 2016.
Throughout the study, they obtained the first seasonal concurrent LAI and CI data for broadleaf crops. Analysis of the data suggests that during the growing season, the crop CI shows an S-shaped seasonal variation in which the CI decreases during the vegetative growth stage, increases during the reproductive stage, and slightly decrease during the senescent stage. CI shows a negative relationship with LAI in the first stage, but a positive relationship in the second stage.
The automatic instrument (PASTIS-57) investigated in this study accurately captures the seasonal variation of LAI in broadleaf crops, with a relative error of about 15% compared with the conventional optical instruments. However, the data obtained by smartphone applications are less reliable, with errors typically exceeding 20%.
The study provides new insights on the structural characteristics of vegetation and guidance for the proper use of optical instruments. The data obtained in the study are useful for the validation of remote sensing LAI and CI products.
This study was supported by the National Natural Science Foundation of China and the National Key Research and Development Program of China. The studies have been published in a recent issue of Agricultural and Forest Meteorology.