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.

Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. Most of vegetated land surface (93.18%) showed one intra-annual cycle whereas double and triple cycles were found in 5.58% of the land surface, mainly in tropical and arid regions along with agricultural areas. In only 1.24% of the pixels, the seasonality was not statistically significant. The highest values of amplitude and stability were found at high latitudes in the northern hemisphere whereas lowest values corresponded to tropical and arid regions, with the latter showing more pluri-annual cycles. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change.

From 2011 to 2016, California experienced a millennial‐intensity drought, generating high levels of tree mortality. Remote sensing has been used to monitor the long‐term impacts of drought; however, discriminating dead from live trees in arid and semiarid deciduous woodlands is challenging. The goals of this study were to assess and map the spatial patterns of drought‐induced tree mortality in a blue oak (Quercus douglasii) woodland, a highly drought‐tolerant species forming savannas along the lower foothills surrounding California's Central Valley. Airborne hyperspectral imagery was used to identify the most important wavelength regions predicting drought‐induced blue oak mortality. The best metric to predict canopy stress was a normalized ratio using the spectral bands 937.53 and 1100.08 nm with a correlation with tree mortality of R² = 0.83. The image prediction of mortality for nine field plots found that 16 of 98 trees died (17.9%) during the drought. We further evaluated tree mortality in 82 independent plots, and we found the greatest image predictive accuracy for tree mortality between 1% and 10%. When applied at the landscape level, the regression‐based index found mortality ranged from 1% to more than 51% in oak stands with an average mortality of 10% over the entire study region. In addition, tree mortality at landscape level showed higher tree mortality in blue oaks on south‐facing aspects probably because of higher insolation rates and in sites with low potential for accumulated drainage.

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.