The spatial scaling effect of the discrete-canopy effective leaf area index retrieved by remote sensing

The leaf area index (LAI) is a critical biophysical variable that describes canopy geometric structures and growth conditions. It is also an important input parameter for climate, energy and carbon cycle models. The scaling effect of the LAI has always been of concern. Considering the effects of the clumping indices on the BRDF models of discrete canopies, an effective LAI is defined. The effective LAI has the same function of describing the leaf density as does the traditional LAI. Therefore, our study was based on the effective LAI. The spatial scaling effect of discrete canopies significantly differed from that of continuous canopies. Based on the directional second-derivative method of effective LAI retrieval, the mechanism responsible for the spatial scaling effect of the discrete-canopy LAI is discussed and a scaling transformation formula for the effective LAI is suggested in this paper. Theoretical analysis shows that the mean values of effective LAIs retrieved from high-resolution pixels were always equal to or larger than the effective LAIs retrieved from corresponding coarse-resolution pixels. Both the conclusions and the scaling transformation formula were validated with airborne hyperspectral remote sensing imagery obtained in Huailai County, Zhangjiakou, Hebei Province, China. The scaling transformation formula agreed well with the effective LAI retrieved from hyperspectral remote sensing imagery.

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