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Assessment of vegetation biochemical and biophysical variables is useful when developing indicators for biodiversity monitoring and climate change studies. Here, we compared a radiative transfer model (RTM) inversion by merit function and five machine learning algorithms trained on an RTM simulated dataset predicting the three plant traits leaf chlorophyll content (LCC), canopy chlorophyll content (CCC), and leaf area index (LAI), in a mixed temperate forest. The accuracy of the retrieval methods in predicting these three plant traits with spectral data from Sentinel-2 acquired on 13 July 2017 over Bavarian Forest National Park, Germany, was evaluated using in situ measurements collected contemporaneously. The RTM inversion using merit function resulted in estimations of LCC (R2 = 0.26, RMSE = 3.9 µg/cm2), CCC (R2 = 0.65, RMSE = 0.33 g/m2), and LAI (R2 = 0.47, RMSE = 0.73 m2/m2), comparable to the estimations based on the machine learning method Random forest regression of LCC (R2 = 0.34, RMSE = 4.06 µg/cm2), CCC (R2 = 0.65, RMSE = 0.34 g/m2), and LAI (R2 = 0.47, RMSE = 0.75 m2/m2). Several of the machine learning algorithms also yielded accuracies and robustness similar to the RTM inversion using merit function. The performance of regression methods trained on synthetic datasets showed promise for fast and accurate mapping of plant traits accross different plant functional types from remote sensing data. [ABSTRACT FROM AUTHOR]

Leaf traits at canopy level (hereinafter canopy traits) are conventionally expressed as a product of total canopy leaf area index (LAI) and leaf trait content based on samples collected from the exposed upper canopy. This traditional expression is centered on the theory that absorption of incident photosynthetically active radiation (PAR) follow a bell-shaped function skewed to the upper canopy. However, the validity of this theory has remained untested for a suite of canopy traits in a temperate forest ecosystem across multiple seasons using multispectral imagery. In this study, we examined the effect of canopy traits expression in modelling canopy traits using Sentinel-2 multispectral data across the growing season in Bavaria Forest National Park (BFNP), Germany. To achieve this, we measured leaf mass per area (LMA), chlorophyll (C ab), nitrogen (N) and carbon content and LAI from the exposed upper and shaded lower canopy respectively over three seasons (spring, summer and autumn). Subsequently, we estimated canopy traits using two expressions, i.e. the traditional expression-based on the product of LAI and leaf traits content of samples collected from the sunlit upper canopy (hereinafter top-of-canopy expression) and the weighted expression - established on the proportion between the shaded lower and sunlit upper canopy LAI and their respective leaf traits content. Using a Random Forest machine-learning algorithm, we separately modelled canopy traits estimated from the two expressions using Sentinel-2 spectral bands and vegetation indices. Our results showed that dry matter related canopy traits (LMA, N and carbon) estimated based on the weighted canopy expression yield stronger correlations and higher prediction accuracy (NRMSE CV < 0.19) compared to the top-of-canopy traits expression across all seasons. In contrast, canopy chlorophyll estimated from the top-of-canopy expression demonstrated strong fidelity with Sentinel-2 bands and vegetation indices (RMSE < 0.48 µg/cm2) compared to weighted canopy chlorophyll (RMSE > 0.48 µg/cm2) across all seasons. We also developed a generalized model that explained 52.57–67.82% variation in canopy traits across the three seasons. Using the most accurate Random Forest model for each season, we demonstrated the capability of Sentinel-2 data to map seasonal dynamics of canopy traits across the park. Results presented in this study revealed that canopy trait expression can have a profound effect on modelling the accuracy of canopy traits using satellite imagery throughout the growing seasons. These findings have implications on model accuracy when monitoring the dynamics of ecosystem functions, processes and services. [ABSTRACT FROM AUTHOR]

Chlorophyll content, as the primary pigment driving photosynthesis, is directly affected by many natural and anthropogenic disturbances and stressors. Accurate and timely estimation of canopy chlorophyll content (CCC) is essential for effective ecosystem monitoring to allow for successful management interventions to occur. Hyperspectral remote sensing offers the possibility to accurately estimate and map canopy chlorophyll content. In the past, research has predominantly focused on the use of hyperspectral data on canopy chlorophyll content retrieval of crops and grassland ecosystems. Therefore, in this study, a temperate mixed forest, the Bavarian Forest National Park in Germany, was chosen as the study site. We compared different statistical models (narrowband vegetation indices (VIs), partial least squares regression (PLSR) and random forest (RF)) in their accuracy to predict CCC using airborne hyperspectral data. The airborne hyperspectral imagery was acquired by the AisaFenix sensor (623 bands; 3.5 nm spectral resolution in the visible near-infrared (VNIR) region, and 12 nm spectral resolution in the shortwave infrared (SWIR) region; 3 m spatial resolution) on July 6, 2017. In situ leaf chlorophyll content and leaf area index measurements were sampled from the upper canopy of coniferous, mixed, and deciduous forest stands in July and August 2017. The study yielded the highest retrieval accuracies with PLSR (root mean square error (RMSE) = 0.25 g/m2, R2 = 0.66). It further indicated specific spectral regions within the visible (390–400 nm and 470–540 nm), red edge (680–780 nm), near-infrared (1050–1100 nm) and shortwave infrared regions (2000–2270 nm) that were important for CCC retrieval. The results showed that forest CCC can be mapped with relatively high accuracies using image spectroscopy. [ABSTRACT FROM AUTHOR]

• The effect of leaf position on the performance of the PROSPECT model is examined. • C ab , EWT and LMA were significantly higher for upper canopy compared to lower canopy leaf samples. • Reflectance of leaf samples from the lower canopy closely matched PROSPECT simulated reflectance compared to upper canopy leaf samples. • Lower canopy C ab and EWT retrieved with higher accuracy compared to upper canopy leaf samples. • Upper canopy LMA retrieved with higher accuracy compared to lower canopy leaf samples. • Variations in leaf biochemistry and morphology through the vertical canopy profile potentially affects the performance of the PROSPECT model. Leaf traits and subsequently leaf spectral properties depend on the leaf phenological stage and light conditions within a canopy. The PROSPECT radiative transfer model has been extensively and successfully used to retrieve leaf traits for mature, sunlit leaves at peak vegetation growth, i.e. summer. However, research on the quantification of leaf traits using PROSPECT across the canopy vertical profile throughout the growing season is still lacking. Therefore, this study aims at examining the effect of leaf position on the performance of the PROSPECT model in modelling leaf optical properties and retrieving leaf chlorophyll content (C ab), equivalent water thickness (EWT), and leaf mass per area (LMA) throughout the growing season. To achieve this objective, we collected 588 leaf samples from the upper and lower canopies of deciduous stands over three seasons (i.e., spring, summer and autumn) in Bavaria Forest National Park, Germany. Leaf traits including C ab , EWT and LMA, were measured for all the samples, and their reflectance spectra were obtained using an ASD FieldSpec-3 Pro FR spectroradiometer coupled with an Integrating Sphere. We initially assessed the performance of the PROSPECT model by comparing reflectance spectra generated in forward mode against reflectance spectra measured on leaf samples collected in the field. We subsequently inverted the PROSPECT model to retrieve C ab , EWT and LMA using the look-up-table (LUT) approach. Our results consistently demonstrated that the measured reflectance of leaf samples collected from the lower canopy had a stronger match with PROSPECT simulated reflectance spectra, especially in the NIR spectrum compared to leaf samples collected from the upper canopy throughout the growing season. This observation concurred with the pattern of C ab and EWT retrieval accuracies across the canopy i.e. the retrieval accuracy for the lower canopy was consistently higher (NRMSE = 0.1-0.2 for C ab ; NRMSE = 0.125-0.16 for EWT) when compared to the upper canopy (NRMSE = 0.122 - 0.269 for C ab ; NRMSE = 0.162 -0.0.258 for EWT) across all seasons. In contrast, LMA retrieval accuracies for the upper canopy (NRMSE = 0.146 - 0.184) were higher compared to the lower canopy (NRMSE = 0.162 - 0.239) for all seasons except for the spring season. For all the leaf traits examined in this study, the range in retrieval accuracy between the upper and lower canopy was greater in summer (compared to other seasons). We report for the first time that although the PROSPECT model provides reasonable retrieval accuracy of C ab , EWT and LMA, variations in leaf biochemistry and morphology through the vertical canopy profile affects the performance of the model over the growing season. Findings of this study have important implications on field sampling protocols and upscaling leaf traits to canopy and landscape level using multi-layered physical models coupled with PROSPECT. [ABSTRACT FROM AUTHOR]