Deriving Leaf Area Index Reference Maps Using Temporally Continuous In Situ Data: A Comparison of Upscaling Approaches

To further progress the validation of global leaf area index (LAI) products, temporally continuous reference data are a key requirement, as periodic field campaigns fail to adequately characterize temporal dynamics. Progress in cost-effective automated measurement techniques has been made in recent years, but appropriate upscaling methodologies are less mature. Recently, the use of multitemporal transfer functions has been proposed as a potential solution. Using data collected during an independent field campaign, we evaluated the performance of both vegetation index-based multitemporal transfer functions and a radiative transfer model (RTM)-based upscaling approach. Whether assessed using cross validation or data from the independent field campaign, the RTM-based approach provided the best performance (r2 ≥ 0.88, RMSE ≤ 0.41, NRMSE < 13%). For upscaling temporally continuous in situ data, the ability of RTM-based approaches to account for seasonal changes in sun-sensor geometry is a key advantage over vegetation index-based multitemporal transfer functions.