Leaf Area Index retrieval from remotely sensed data: Scaling effect and propagation mechanisms

This paper makes an attempt to address the scaling problem of Leaf Area Index (LAI) and to analyze the propagation of scaling effect of LAI. On the basis of the Taylor series expansion and following the general scaling procedure, it is demonstrated that the magnitude of the scaling effect is the product of the degree of the non-linearity of the retrieval model and the spatial heterogeneity of input variables involved in this model. Finally, a scaling correction model is proposed to correct for the scaling effect of LAI. The validation using the simulated data indicates that the proposed scaling correction model of LAI gives promising accuracy if the spatial heterogeneity is well characterized by its wavelet variance. The RMSE and relative error of retrieved LAI induced by the scale effect can be greatly reduced after scaling correction. The scaling propagation analysis of LAI reveals that the scaling effects caused by several non-linear components may compensate for each other, which would enhance our confidence in using LAI product over heterogeneity areas.