Predicting Foliar Nutrient Concentrations across Geologic Materials and Tree Genera in the Northeastern United States Using Spectral Reflectance and Partial Least Squares Regression Models

Abstract

Spectral data can potentially offer a rapid assessment of nutrients in leaves and reveal information about the geologic history of the soil. This study evaluated the capability of the partial least squares regression (PLSR) for estimating foliar macro- and micronutrients (Ca, Mg, K, P, Mn, and Zn) using spectral data (400 to 2,450 nm). First, filter-based wavelength selection was conducted to reduce the independent variables. PLSR performance was then assessed across 4 geologic materials (coarse glacial till, glaciofluvial, melt-out till, and outwash) and 4 dominant tree genera ( Acer , Betula , Fagus , and Quercus ) in the northeastern United States. The spectral ranges 400 to 500 nm and 1,800 to 2,450 nm were found to be the most important spectral regions for estimating foliar nutrient concentrations. The developed PLSR model predicted 6 foliar nutrients with moderate to high accuracy (adjusted R 2 from 0.60 to 0.75). Foliar macronutrient concentrations were estimated with higher accuracy (mean adj. R 2 = 0.69) than micronutrient concentrations (mean adj. R 2 = 0.635). The prediction for the individual tree genera group and the individual geologic materials group outperformed the combined group; for instance, the adj. R 2 for estimating Ca and P was 39% higher for American beech ( Fagus grandifolia ) than all tree genera combined. Spectral measurements combined with wavelength selection and PLSR models can potentially be used to quantify foliar macro- and micronutrients at regional scales, and taking into account geologic materials and tree genera will improve this prediction.