Continuous estimation of leaf area index using in-situ albedo data

Abstract:

Continuous leaf area index (LAI) data are important to model and monitor ecological systems. Over the past decades, various direct and indirect measurement methods have been developed and widely-applied, however, high-quality time-series site-level data are still scarce. This study presents a method to estimate temporal LAI based on in-situ albedo observations using a forest radiative transfer (FRT) model. The estimation of LAI from FRT model inversion is based on a look-up table (LUT) approach. We first simulate diurnal blue-sky albedos to build the LUT by sampling the space of the FRT input variables. Then, to select the feasible solutions from the LUT, the simulated diurnal albedo in LUT are compared to measured data for each day, and a semi-mechanistic LAI dynamics model is served as a temporal constraint for the entire growth season. We also define and provide uncertainties of model inversion. Finally, estimated LAI time-series are compared to multi-year field measurements. We expect this method can be applied for the sites where continuous albedo data are available.