Generality of leaf spectroscopic models for predicting key foliar functional traits across continents: A comparison between physically- and empirically-based approaches

Abstract

Leaf spectroscopy provides an efficient way of predicting foliar functional traits, commonly using physically- and empirically-based models. However, the generality of both models has not been fully investigated, and it is not clear if inversion strategies of physically-based models can be transferred across datasets. In this study, we evaluated the generality of leaf spectroscopic models for predicting key foliar functional traits and compared the performance of physically- and empirically-based approaches. Two extensive datasets compiling a total of 3861 foliar samples were collected from 24 field sites in eastern United States and south China. The leaf radiative transfer model PROSPECT was coupled with COSINE (PROCOSINE) to retrieve foliar traits from leaf bidirectional reflectance factor (BRF). A commonly used empirically-based model, partial least squares regression (PLSR) was performed as a comparison. Results showed that both PROSPECT and PROCOSINE can accurately estimate leaf mass per area (LMA) and equivalent water thickness (EWT). Inversion strategies including the selection of optimal spectral domains and the use of prior information (IS3) greatly improved the estimation accuracy of leaf nitrogen, leaf chlorophyll a + b and carotenoids. The estimation accuracies were similar when transferring inversion strategies across datasets, indicating a high level of transferability of physically-based models. PLSR and interval PLSR (iPLSR, via feature selection) could predict foliar traits with high accuracies when cross-validation was performed, and iPLSR achieved higher accuracies. But both the empirical approaches demonstrated low transferability when applied to an independent dataset. Our findings highlight the importance of generalized traits models with respect to development and calibration of leaf radiative transfer model, as well as incorporating representative samples in training empirical models. This study can help us to better understand the variation of foliar traits among and within species, their response to environmental change, as well as plant biodiversity.