Leaf pigment contents, such as chlorophylls a and b content (C ${}_{ab}$ ) or carotenoid content (Car), and the leaf area index (LAI) are recognized indicators of plants’ and forests’ health status that can be estimated through hyperspectral imagery. Their measurement on a seasonal and yearly basis is critical to monitor plant response and adaptation to stress, such as droughts. While extensively done over dense canopies, estimation of these variables over tree-grass ecosystems with very low overstory LAI (mean site LAI < 1 m ${}^2$ /m ${}^2$ ), such as woodland savannas, is lacking. We investigated the use of look-up table (LUT)-based inversion of a radiative transfer model to retrieve LAI and leaf C ${}_{ab}$ and Car from AVIRIS images at an 18 m spatial resolution at multiple dates over a broadleaved woodland savanna during the California drought. We compared the performances of different cost functions in the inversion step. We demonstrated the spatial consistency of our LAI, C ${}_{ab}$ , and Car estimations using validation data from low and high canopy cover parts of the site, and their temporal consistency by qualitatively confronting their variations over two years with those that would be expected. We concluded that LUT-based inversions of medium-resolution hyperspectral images, achieved with a simple geometric representation of the canopy within a 3D radiative transfer model (RTM), are a valid means of monitoring woodland savannas and more generally sparse forests, although for maximum applicability, the inversion cost functions should be selected using validation data from multiple dates. Validation revealed that for monitoring use: The normalized difference vegetation index (NDVI) outperformed other indices for LAI estimations (root mean square error (RMSE) = 0.22 m ${}^2$ /m ${}^2$ , $R^2$ = 0.81); the band ratio $\frac{{\rho }_{0.750\mathsf{\mu }\mathrm{m}}}{{\rho }_{0.550\mathsf{\mu }\mathrm{m}}}$ retrieved C ${}_{ab}$ more accurately than other chlorophylls indices (RMSE = 5.21 $\mathsf{\mu }$ g/cm ${}^2$ , $R^2$ = 0.73); RMSE over the 0.5–0.55 $\mathsf{\mu }$ m interval showed encouraging results for Car estimations.

The estimation and mapping of vegetation traits from satellite hyperspectral imagery is entering a new era, as multiple missions have recently started and more are currently in preparatory phase. With expected ground sampling distances (GSD) ranging from 8 to 30 m, these missions could complement each other, especially over spatially heterogeneous environments where the canopy cover (CC) is low. This study focused on the retrieval of five vegetation traits (gap fraction, leaf chlorophylls (C ${ }_{ab}$) and carotenoids (Car) contents, equivalent water thickness, and leaf mass per area) of two Mediterranean-climate forests from AVIRIS-Classic (AVIRIS-C), synthetic Biodiversity, and synthetic Surface Biology and Geology (SBG) missions with 18 m, 8 m, and 30 m GSD, respectively, using a hybrid method. The synthetic SBG images were provided by NASA, while the Biodiversity images were generated from airborne AVIRIS-Next Generation hyperspectral imagery. Partial least-square regressors were trained over the outputs of the DART model to estimates vegetation traits. Estimated accuracies were assessed, when possible, by comparison with in situ measurements. We showed that estimated accuracy of gap fraction was similar between AVIRIS-C and SBG (RMSE of 0.09, R ${ }^2$ of 0.8 and RMSE of 0.07, R ${ }^2$ of 0.59, respectively). Leaf traits estimated accuracies were also similar between these two sensors, but only acceptable for C ${ }_{ab}$ and Car ( $\sim$ 7.5 μg.cm ${ }^{-2}$ RMSE for C ${ }_{ab}$, $\sim$ 1.65 μg.cm ${ }^{-2}$ RMSE for Car), especially over the densest parts of the canopy. When comparing estimates obtained from Biodiversity and SBG imagery, it appeared that the denser the canopy, the more estimates from both sensors were in agreement for all leaf traits (for instance, C ${ }_{ab}$, R ${ }^2$ was 0.2 for 30% $\le$ CC $\le$ 50% and 0.48 for CC $\ge$ 80%). The results show that (i) SBG imagery should lead to estimated accuracies similar to AVIRIS-C, with acceptable performances over dense canopies, and that (ii) Biodiversity imagery has a high potential to map vegetation traits over any canopy no matter its sparsity, as individual tree crowns are mostly resolved at an 8 m GSD.

Gap Fraction, leaf pigment contents (content of chlorophylls a and b (${\mathrm{C}}_{ab}$) and carotenoids content (Car)), Leaf Mass per Area (LMA), and Equivalent Water Thickness (EWT) are considered relevant indicators of forests’ health status, influencing many biological and physical processes. Various methods exist to estimate these variables, often relying on the extensive use of Radiation Transfer Models (RTMs). While 3D RTMs are more realistic to model open canopies, their complexity leads to important computation times that limit the number of simulations that can be considered; 1D RTMs, although less realistic, are also less computationally expensive. We investigated the possibility to approximate the outputs of a 3D RTM (DART) from a 1D RTM (PROSAIL) to generate in very short time numerous extensive Look-Up Tables (LUTs). The intrinsic error of the approximation model was evaluated through comparison with DART reference values. The model was then used to generate LUTs used to estimate Gap Fraction, ${\mathrm{C}}_{ab}$, Car, EWT, and LMA of Blue Oak-dominant stands in a woodland savanna from AVIRIS-C data. Performances of the approximation model for estimation purposes compared to DART were evaluated using Wilmott’s index of agreement ($d_r$), and estimation accuracy was measured with coefficients of determination ($R^2$) and Root Mean Squared Error (RMSE). The low approximation error of the proposed model demonstrated that the model could be considered for canopy covers as low as 30%. Gap Fraction estimations presented similar performances with either DART or the approximation ($d_r$ 0.78 and 0.77, respectively), while ${\mathrm{C}}_{ab}$ and Car showed improved performances ($d_r$ increasing from 0.65 to 0.77 and 0.34 to 0.65, respectively). No satisfying estimation methods were found for LMA and EWT using either models, probably due to the high sensitivity of the scene’s reflectance to Gap Fraction and soil modeling at such low LAI. Overall, estimations using the approximated reflectances presented either similar or improved accuracy. Our findings show that it is possible to approximate DART reflectances from PROSAIL using a minimal number of DART outputs for calibration purposes, drastically reducing computation times to generate reflectance databases: 300,000 entries could be generated in 1.5 h, compared to the 12,666 total CPU hours necessary to generate the 21,840 calibration entries with DART.

The advent of new spaceborne imaging spectrometers offers new opportunities for ecologists to map vegetation traits at global scales. However, to date most imaging spectroscopy studies exploiting satellite spectrometers have been constrained to the landscape scale. In this paper we present a new method to map vegetation traits at the landscape scale and upscale trait maps to the continental level, using historical spaceborne imaging spectroscopy (Hyperion) to derive estimates of leaf mass per area, nitrogen, and carbon concentrations of forests in Québec, Canada. We compare estimates for each species with reference field values and obtain good agreement both at the landscape and continental scales, with patterns consistent with the leaf economic spectrum. By exploiting the Hyperion satellite archive to map these traits and successfully upscale the estimates to the continental scale, we demonstrate the great potential of recent and upcoming spaceborne spectrometers to benefit plant biodiversity monitoring and conservation efforts.

Equivalent water thickness (EWT) and leaf mass per area (LMA) are important indicators of plant processes, such as photosynthetic and potential growth rates and health status, and are also important variables for fire risk assessment. Retrieving these traits through remote sensing is challenging and often requires calibration with in situ measurements to provide acceptable results. However, calibration data cannot be expected to be available at the operational level when estimating EWT and LMA over large regions. In this study, we assessed the ability of a hybrid retrieval method, consisting of training a random forest regressor (RFR) over the outputs of the discrete anisotropic radiative transfer (DART) model, to yield accurate EWT and LMA estimates depending on the scene modeling within DART and the spectral interval considered. We show that canopy abstractions mostly affect crown reflectance over the 0.75–1.3 $\mathsf{\mu }$m range. It was observed that excluding these wavelengths when training the RFR resulted in the abstraction level having no effect on the subsequent LMA estimates (RMSE of 0.0019 g/cm${}^2$ for both the detailed and abstract models), and EWT estimates were not affected by the level of abstraction. Over AVIRIS-Next Generation images, we showed that the hybrid method trained with a simplified scene obtained accuracies (RMSE of 0.0029 and 0.0028 g/cm${}^2$ for LMA and EWT) consistent with what had been obtained from the test dataset of the calibration phase (RMSE of 0.0031 and 0.0032 g/cm${}^2$ for LMA and EWT), and the result yielded spatially coherent maps. The results demonstrate that, provided an appropriate spectral domain is used, the uncertainties inherent to the abstract modeling of tree crowns within an RTM do not significantly affect EWT and LMA accuracy estimates when tree crowns can be identified in the images.

Surface reflectance is an essential product from remote sensing Earth observations critical for a wide variety of applications, including consistent land cover mapping and change, and estimation of vegetation attributes. From 2000 to 2017 the Earth Observing-1 Hyperion instrument acquired the first satellite based hyperspectral image archive from space resulting in over 83,138 publicly available images. Hyperion imagery however requires significant preprocessing to derive surface reflectance. SUREHYP is a Python package designed to process batches of Hyperion images, bringing together a number of published algorithms and methods to correct at sensor radiance and derive surface reflectance. In this paper, we present the SUREHYP workflow and demonstrate its application on Hyperion imagery. Results indicate SUREHYP produces flat terrain surface reflectance results comparable to commercially available software, with reflectance values for the whole spectral range almost entirely within 10% of the software’s over a reference target, yet it is publicly available and open source, allowing the exploitation of this valuable hyperspectral archive on a global scale.