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. Both LAI and CI are important parameters in vegetation photosynthesis and evapotranspiration models. They also 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, as well as global change studies. However, due to the limitation of observational methods, the understanding on the seasonal variation and the relationship between LAI and CI still remains unclear.   

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, a research team led by Prof. FANG Hongliang from the Institute of Geographic Sciences and Natural Resources Research (IGSNRR) of the Chinese Academy of Sciences carried out a seasonal LAI and CI measurements over maize, soybean, and sorghum fields in northeast China in 2016.   

They obtained the first seasonal concurrent LAI and CI data for broadleaf crops. Analysis of the data suggested that during the growing season the crop CI showed an S-shaped seasonal variation, in which the CI decreased during the vegetative growth stage, increased during the reproductive stage, and slightly decreased during the senescent stage. CI showed 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 captured 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 were 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.