The PROSPECT model has been widely used to estimate leaf biochemical constituents, but retrieval of leaf mass per area (LMA) in fresh leaves has proved to be difficult due to the predominant water absorption in the infrared spectral region. At wavelengths where water absorption is low, both LMA absorption and light scattering are relatively high. Therefore, the uncertainty in scattering simulation at these wavelengths will lead to a relatively large error in LMA estimation. In this paper, we introduce a wavelength-independent factor to represent the first-order effect of anisotropic scattering in the elementary layer in the modified model PROSPECT-g, aiming at appropriately simulating leaf optical properties in spectral regions with high scattering and thus reducing the uncertainty in LMA estimation. In order to avoid introducing a new variable to be retrieved in model inversion, this factor is an intermediate variable derived from measured near infrared region spectral data and other existing model parameters. Results show that about 30%-40% of the tested samples are well simulated using PROSPECT-5, while for the rest of the samples simulation is greatly improved with PROSPECT-g. Leaf reflectance and transmittance reconstructions using PROSPECT-g are improved, especially at wavelengths with high scattering such as 750-1400 and 1500-1850 nm. LMA retrieval is significantly improved, with the average root-mean-square error decreasing from 38.7 (PROSPECT-5) to 16.6 g/m² (PROSPECT-g) for 628 leaves after considering anisotropic scattering in the elementary layer. Improvements are particularly noticeable for leaves with extremely high LMA contents.

Nitrogen is an essential nutrient in many terrestrial ecosystems because it affects vegetation’s primary production. Due to the variety of nitrogen-containing substances and the differences in their composition across species, statistical approaches are now dominant in remote sensing retrieval of leaf nitrogen content. Many studies remove spectral regions characterized by strong water absorptions before retrieving nitrogen content, because water is believed to mask the absorption features of nitrogen. The objectives of this study are to discuss the necessity of this practice and to explore how water absorption affects leaf nitrogen estimation. Spectral measurements and chemical analyses for Maize, Sawtooth Oak, and Sweetgum leaves were carried out in 2014. The leaf optical properties model PROSPECT5 was used to eliminate the influences of water on the measured reflectance spectra. The inversion accuracy of PROPECT5 for chlorophyll, carotenoid, water, and dry matter of Maize was also discussed. Measured, simulated, and water-removed spectra were used to: (1) find the optimal nitrogen-related spectral index; and (2) regress with the area-based leaf nitrogen concentration (LNC) using the partial least square regression technique (PLSR). Two types of spectral indices were selected in this study: Normalized Difference Spectral Index (NDSI) and Ratio Spectral Index (RSI). Additionally, first-order derivative forms of measured, simulated, and water-removed spectra were devised to search for the optimal spectral indices. Finally, species-specific optimal indices and cross-species optimal indices, as well as their root mean square errors (RMSE) and coefficients of determination (R²), were obtained. The Ending Top Percentile (ETP), an indicator of the performance of cross-species optimal indices, was also calculated. PLSR was combined with leave-one-out cross validation (LOOCV) for each species. The predicted root mean square errors (RMSEP) and predicted R² were finally calculated. The results showed that chlorophyll, carotenoid, and water contents could be estimated with R² of 0.75, 0.59, and 0.69, respectively, which were acceptable for fresh leaves. The dry matter was retrieved with a relatively lower accuracy because of the fixed absorption coefficients adopted by PROSPECT5. The performances of species-specific optimal indices using water-free spectra were comparable to or worse than the corresponding indices derived with measured or simulated spectra. Compared with measured spectra, ETP did not change much after the effects of water were removed, and the R² between cross-species optimal spectral indices and area-based LNC for Sawtooth Oak and Sweetgum decreased while it remained almost the same for Maize, suggesting that the water-removed cross-species optimal indices were inferior to the corresponding optimal indices found without water removal. ETP was larger than 30% for all spectra, demonstrating the non-existence of common optimal NDSI or RSI for the three species. After water removal, the accuracy of PLSR for Sawtooth Oak and Sweetgum decreased and increased negligibly for Maize. The results suggest that water absorption has limited effects on reducing the accuracy of leaf nitrogen estimation. On the contrary, the accuracy may decrease due to the loss of spectral information caused by the removal of water-sensitive spectral regions.

Leaf chlorophyll content plays a vital role in plant photosynthesis. The PROSPECT model has been widely used for retrieving leaf chlorophyll content from remote sensing data over various plant species. However, despite wide variations in leaf surface reflectance across different plant species and environmental conditions, leaf surface reflectance is assumed to be the same for different leaves in the PROSPECT model. This work extends the PROSPECT model by taking into account the variation of leaf surface reflection. In the modified model named PROSPECT-Rsurf, an additional surface layer with a variable refractive index is bounded on the N elementary layers. Leaf surface reflectance (Rs) is characterized by the difference between the refractive indices of leaf surface and interior layers. The specific absorption coefficients of the leaf total chlorophyll and carotenoids were recalibrated using a cross-calibration method and the refractive indices of leaf surface and interior layers were obtained during model inversion. Chlorophyll content (C_ab) retrieval and spectral reconstruction in the visible spectral region (VIS, 400–750 nm) were greatly improved using PROSPECT-Rsurf, especially for leaves covered by heavy wax or hard cuticles that lead to high surface reflectance. The root mean square error (RMSE) of chlorophyll estimates decreased from 11.1 µg/cm² to 8.9 µg/cm² and the Pearson’s correlation coefficient (r) increased from 0.81 to 0.88 (p < 0.01) for broadleaf samples in validation, compared to PROSPECT-5. For needle leaves, r increased from 0.71 to 0.89 (p < 0.01), but systematic overestimation of C_ab was found due to the edge effects of needles. After incorporating the edge effects in PROSPECT-Rsurf, the overestimation of C_ab was alleviated and its estimation was improved for needle leaves. This study explores the influence of leaf surface reflectance on C_ab estimation at the leaf level. By coupling PROSPECT-Rsurf with canopy models, the influence of leaf surface reflectance on canopy reflectance and therefore canopy chlorophyll content retrieval can be investigated across different spatial and temporal scales.

Wildfires result in forest loss or degradation and release substantial emissions into the atmosphere. Forest regrowth following these fires allows for ecosystem repair and carbon replenishment. However, there is a lack of datasets explicitly characterizing the forest regrowth after fires. Here we employed multiple remotely sensed datasets to generate the first global maps of forest structure regrowth including forest height, aboveground biomass (AGB), leaf area index (LAI), and fraction of photosynthetically active radiation (FPAR) following wildfires at a 30 m spatial resolution. The regrowth index for each structural parameter includes regrowth ratio and rate at 5-year intervals, primarily from 2000 to 2020. The dataset developed in this study provides detailed insights into the characteristics of global forest regrowth following forest fires in both spatial and temporal dimensions, contributing to the assessment of forest ecology equilibrium and the quantification of forest carbon dynamics.

With the rapid advancement of unmanned aerial vehicles (UAVs) in recent years, UAV-based remote sensing has emerged as a highly efficient and practical tool for environmental monitoring. In vegetation remote sensing, UAVs equipped with hyperspectral sensors can capture detailed spectral information, enabling precise monitoring of plant health and the retrieval of physiological and biochemical parameters. A critical aspect of UAV-based vegetation remote sensing is the accurate acquisition of canopy reflectance. However, due to the mobility of UAVs and the variation in flight altitude, the data are susceptible to scale effects, where changes in spatial resolution can significantly impact the canopy reflectance. This study investigates the spatial scale issue of UAV hyperspectral imaging, focusing on how varying flight altitudes influence atmospheric correction, vegetation viewer geometry, and canopy heterogeneity. Using hyperspectral images captured at different flight altitudes at a Chinese fir forest stand, we propose two atmospheric correction methods: one based on a uniform grey reference panel at the same altitude and another based on altitude-specific grey reference panels. The reflectance spectra and vegetation indices, including NDVI, EVI, PRI, and CIRE, were computed and analyzed across different altitudes. The results show significant variations in vegetation indices at lower altitudes, with NDVI and CIRE demonstrating the largest changes between 50 m and 100 m, due to the heterogeneous forest canopy structure and near-infrared scattering. For instance, NDVI increased by 18% from 50 m to 75 m and stabilized after 100 m, while the standard deviation decreased by 32% from 50 m to 250 m, indicating reduced heterogeneity effects. Similarly, PRI exhibited notable increases at lower altitudes, attributed to changes in viewer geometry, canopy shadowing and soil background proportions, stabilizing above 100 m. Above 100 m, the impact of canopy heterogeneity diminished, and variations in vegetation indices became minimal (<3%), although viewer geometry effects persisted. These findings emphasize that conducting UAV hyperspectral observations at altitudes above at least 100 m minimizes scale effects, ensuring more consistent and reliable data for vegetation monitoring. The study highlights the importance of standardized atmospheric correction protocols and optimal altitude selection to improve the accuracy and comparability of UAV-based hyperspectral data, contributing to advancements in vegetation remote sensing and carbon estimation.

Solar‐induced chlorophyll fluorescence (SIF) provides remotely sensible signals for monitoring gross primary production (GPP). Ground‐based multiangle observations of both red and far‐red SIF above wheat and maize canopies were conducted to examine angular effects on SIF. With these new measurements, we were able for the first time to refine and apply an algorithm developed for angular normalization of both red and far‐red SIF measurements. The angular normalization improved the correlation of SIF with GPP derived from eddy covariance measurements at the instantaneous scale (1 min), with increases of the diurnal coefficients of determination (of sunlit SIF with GPP) up to 0.21 for far‐red SIF and 0.3 for red SIF based on analysis on 6 sunny days. The improvement was slightly smaller for far‐red SIF than for red SIF, attributing to that the observed angular variation of SIF in the red band was greater than that in the far‐red band due to weaker multiple scattering in the red band in the canopy. In addition, at the hourly time scale, far‐red sunlit SIF shows its advantage to track GPP for heterogonous canopies, while angular normalization of red SIF is effective for homogeneous canopies. In comparison with another angular normalization method based on the escape ratio using datasets over both wheat and maize canopies, the two kinds of method show similar ability to improve the correlation between SIF and GPP, while the results suggest a limitation of SIF in estimating GPP for dense canopies where the fraction of shaded leaves are large.