Canopy Radiative Transfer Model Research Articles

A LiDAR-driven three-dimensional simulation model for far-red solar-induced chlorophyll fluorescence in forests

Solar-induced chlorophyll fluorescence (SIF) is a subtle but informative probe of plant photosynthesis. Quantifying the three-dimensional (3D) distribution of SIF benefits a better understanding of photosynthesis variations over heterogeneous canopies. Although radiative transfer models (RTMs) provide a solid theoretical basis for simulating the 3D SIF distribution, most RTMs use virtual scenes with complex reconstruction processes. This study aims to develop a 3D SIF simulation model (FluorLiDAR) directly driven by terrestrial light detection and ranging (LiDAR) data using leaf and canopy RTMs, including a 3D PAR (photosynthetically active radiation) simulation model, the Fluspect model, the atmosphere radiative transfer module in SCOPE, and the multiple scattering coefficients of sunlit and shaded leaves from the 4-scale model. The results show that (1) the simulated and measured SIF patterns were consistent, with R2 (RMSE) values of 0.73 (0.17 mW/nm/m2/sr) and 0.76 (0.12 mW/nm/m2/sr) for 1-min sampling and 10-min averages, respectively. Moreover, the R2 between FluorLiDAR and the DART simulation reached 0.94. The R2 of FluorLiDAR and DART were both higher than 1D mSCOPE using every 10-min sampling data. (2) Point density, denoted by average NPD (nearest point distance), influenced the performance of our model mainly when it was smaller than 0.1 m. Chlorophyll content had less influence on the model accuracy (R2 and rRMSE), and the bias between simulation and measurement decreased as chlorophyll content increased. (3) The simulated 3D SIF distribution pattern closely resembled PAR within the canopy. Besides, FluorLiDAR can simulate the hot spot effect like DART and mSCOPE though the effect was not as obvious as the other two models. This study highlights the potential of a LiDAR-driven SIF model for 3D SIF simulation over a heterogeneous canopy, which may benefit the understanding of the structural impacts on forest photosynthesis in real forest scenes.

Beyond the visible: Accounting for ultraviolet and far-red radiation in vegetation productivity and surface energy budgets.

Photosynthetically active radiation (PAR) is typically defined as light with a wavelength within 400-700 nm. However, ultra-violet (UV) radiation within 280-400 nm and far-red (FR) radiation within 700-750 nm can also excite photosystems, though not as efficiently as PAR. Vegetation and land surface models (LSMs) typically do not explicitly account for UV's contribution to energy budgets or photosynthesis, nor FR's contribution to photosynthesis. However, whether neglecting UV and FR has significant impacts remains unknown. We explored how canopy radiative transfer (RT) and photosynthesis are impacted when explicitly implementing UV in the canopy RT model and accounting for UV and FR in the photosynthesis models within a next-generation LSM that can simulate hyperspectral canopy RT. We validated our improvements using photosynthesis measurements from plants under different light sources and intensities and surface reflection from an eddy-covariance tower. Our model simulations suggested that at the whole plant level, after accounting for UV and FR explicitly, chlorophyll content, leaf area index (LAI), clumping index, and solar radiation all impact the modeling of gross primary productivity (GPP). At the global scale, mean annual GPP within a grid would increase by up to 7.3% and the increase is proportional to LAI; globally integrated GPP increases by 4.6 PgC year-1 (3.8% of the GPP without accounting for UV + FR). Further, using PAR to proxy UV could overestimate surface albedo by more than 0.1, particularly in the boreal forests. Our results highlight the importance of improving UV and FR in canopy RT and photosynthesis modeling and the necessity to implement hyperspectral or multispectral canopy RT schemes in future vegetation and LSMs.

Toward More Accurate Modeling of Canopy Radiative Transfer and Leaf Electron Transport in Land Surface Modeling

AbstractModeling leaf photosynthesis is essential for quantifying the carbon, water, and energy fluxes of the terrestrial biosphere. However, due to the lack of simultaneous measurements of leaf light absorption and gas exchange, canopy radiative transfer (RT) and photosynthesis modeling often rely on simplified assumptions about light absorption and electron transport. These assumptions ignore variations in leaf biophysical traits and environmental conditions. In this study, we utilized a next‐generation land surface model (LSM)—CliMA Land, which incorporates hyperspectral canopy RT and provides a more accurate representation of trait variations. We evaluated the potential bias in electron transport estimates introduced by the broadband RT schemes used in traditional LSMs. Additionally, we explored the impact of different leaf electron transport parameterization schemes on global‐scale photosynthesis and fluorescence modeling. We showed that (a) traditional LSMs that disregard the impacts of leaf temperature and leaf traits on electron transport tend to overestimate electron transport rates. (b) Photosynthesis and fluorescence within a grid can exhibit biases exceeding 20%, with these biases demonstrating contrasting seasonality. (c) Global estimates of integrated photosynthesis and fluorescence differ by 8.1% and 8.8%, respectively. These results underscore the importance of adopting more sophisticated and accurate modeling schemes, such as hyperspectral canopy RT, in future LSMs and Earth system modeling to enhance the reliability of modeling outcomes.

Estimation of Forest Canopy Fuel Moisture Content in Dali Prefecture by Combining Vegetation Indices and Canopy Radiative Transfer Models from MODIS Data

Forest canopy fuel moisture content (FMC) is a critical factor in assessing the vulnerability of a specific area to forest fires. The conventional FMC estimation method, which relies on look-up tables and loss functions, cannot to elucidate the relationship between FMC and simulated data from look-up tables. This study proposes a novel approach for estimating FMC by combining enhanced vegetation index (EVI) and normalized difference moisture index (NDMI). The method employs the PROSAIL + PROGeoSAIL two-layer coupled radiation transfer model to simulate the vegetation index, the water index, and the FMC value, targeting the prevalent double-layer structure in the study area’s vegetation distribution. Additionally, a look-up table is constructed through numerical analysis to investigate the relationships among vegetation indices, water indices, and FMC. The results reveal that the polynomial equations incorporating vegetation and water indices as independent variables exhibit a strong correlation with FMC. Utilizing the EVI–NDMI joint FMC estimation method enables the direct estimation of FMC. The collected samples from Dali were compared with the estimated values, revealing that the proposed method exhibits superior accuracy (R2 = 0.79) in comparison with conventional FMC estimation methods. In addition, we applied this method to estimate the FMC in the Chongqing region one week before the 2022 forest fire event, revealing a significant decreasing trend in regional FMC leading up to the fire outbreak, highlighting its effectiveness in facilitating pre-disaster warnings.

Estimating leaf photosynthetic capacity using hyperspectral reflectance: Model variability and transferability

Leaf photosynthetic capacity is a crucial parameter for characterizing plant growth status and global nitrogen-carbon cycles. While leaf trait and reflectance models have been widely applied to assess leaf photosynthetic capacity across various plant species, the variability and transferability of these estimation models remain unclear. Thus, this study investigated the variability in estimating leaf maximum carboxylation rate of Rubisco (Vcmax, μmol m−2 s−1) using hyperspectral reflectance across seven plant species datasets. To improve model transferability, we proposed model updating by adding new samples and developed a stacking model to integrate multiple regression model results to reduce variability in the predictions. The PROSPECT model, coupled with spectral derivatives and similarity metrics, was used to retrieve leaf structural and biochemical traits. Our results showed that Vcmax was significantly correlated with the contents of leaf chlorophyll (Cab) and protein (Prot), and other traits such as leaf structure, carotenoid content, and water content also influenced Vcmax. However, the strength of these correlations varied among different datasets due to differences in vegetation types, growth periods, and the number of species. Leaf trait relationships also varied with datasets, with Cab proving to be a good proxy of photosynthesis across all datasets. Leaf traits were superior to leaf reflectance in characterizing the differences between datasets. While leaf reflectance performed well in estimating Vcmax for most datasets, leaf traits were more suitable for constructing transferable estimation models of Vcmax between different datasets. Model transferability was affected by differences in datasets, such as data range and plant species. Model updating by adding 10% new samples significantly improved the assessment of Vcmax, with leaf reflectance yielding better estimation results. Our data also revealed that different models produced inconsistent results, and a stacking model combining multiple models optimized the estimation of Vcmax using leaf traits and reflectance, with the cross-validation coefficient of determination and relative root mean square error of 0.88 and 19.92%, respectively. These findings offer new methods and ideas for assessing leaf photosynthetic capacity across different agro-ecosystems. Further recalibration of the proposed model with canopy radiative transfer models and datasets would enable monitoring of plant photosynthesis at large scales.

Accurate solution of the SAIL model by leaf inclination angle calculation based on laser point clouds

ABSTRACT The canopy radiative transfer model enables quantitative inversion of vegetation biophysical parameters by rapidly simulating spectral information. However, structural input parameters, such as the leaf inclination angle distribution (LAD), are often set to empirical values in classic models, leading to simulation errors. Laser point cloud technology has developed rapidly in recent years, and the 3D structure of vegetation can be detailed using laser sensors and processing software, providing the necessary conditions for accurate solutions of radiative transfer models of the canopy. However, the mechanisms for improving existing models of canopy radiative transfer by introducing laser point clouds have not yet been theoretically analyzed in detail. In this study, a precise solution was proposed by incorporating the accurate LAD obtained from laser point clouds into the intermediate function of the traditional Scattering by Arbitrarily Inclined Leaves (SAIL) model, which is a widely used multiangle radiative transfer model. The calculation of the leaf inclination angle and the derivation of the improved model were detailed in this paper. Experiments from different observation angles and bands compared the similarity between the actual measured spectra and the simulated spectra for both the traditional SAIL model and the proposed model with accurate structural parameters. The overall similarity of the proposed model with respect to the conventional SAIL model was improved, indicating that introducing laser point clouds improved the simulation accuracy. The proposed model achieved more significant improvements in the blue 450 nm and red 670 nm bands. Moreover, the similarity improvements differed between different angles. Finally, we analyzed the sensitivity of the structural parameters of the SAIL model and the proposed model using local and global sensitivity analysis methods.

Mapping 3D plant chlorophyll distribution from hyperspectral LiDAR by a leaf-canopyradiative transfer model

The three-dimensional (3D) plant chlorophyll distribution plays an essential role in plant physiological and ecological analysis. The hyperspectral LiDAR (HSL) technology, as a new active technology, has the advantage of directly and simultaneously obtaining plant hyperspectral and 3D spatial information. It provides an alternative opportunity for fast and nondestructive 3D plant chlorophyll estimation. Current estimation models are developed by statistical relationships between HSL spectra and plant chlorophyll content, which perform limited robustness and lack physical mechanisms. The leaf-canopy radiative transfer models (RTMs) could describe physical responses between spectra and chlorophyll content, and show great robustness from optical hyperspectral data, which have the potential to overcome the limitation of current estimation models. However, it is unclear whether leaf-canopy RTMs could be applied to HSL spectra for 3D chlorophyll mapping. Therefore, this study explored the potential of a leaf-canopy RTM with HSL spectra for 3D plant chlorophyll estimation. Firstly, an optimal leaf RTM was selected from a series of PROSEPCT models, and determined by estimating leaf-scale chlorophyll content across multi-species and multi-state leaves. Then, the 4SAIL model, a canopy RTM, was optimized to meet the active detection mechanism of the HSL technology. Next, coupling the best PROSPECT with optimized 4SAIL models was used as the leaf-canopy RTM. The 3D chlorophyll distribution was estimated by the leaf-canopy RTM with a lookup table (LUT) algorithm under six cost functions. Finally, the performance of 14 vegetation indice (VI) models was tested as a comparison. Results demonstrated that the PROSPECT-5 model was the best leaf RTM with the best R2 of 0.60. The combination of PROSPECT-5 and optimized 4SAIL model could successfully achieve 3D plant chlorophyll estimation. This leaf-canopy estimation RTM with the best cost function (Least square estimator) had the best accuracy with an R2 of 0.73. Its accuracy was the same as the median accuracy of the best VI model (MTCI model), and higher than that of other VI models. The estimated and actual 3D chlorophyll distributions were consistent. The application of the leaf-canopy RTM with HSL technology offers an alternative approach to plant physiological and ecological research.

Daily monitoring of Effective Green Area Index and Vegetation Chlorophyll Content from continuous acquisitions of a multi-band spectrometer over winter wheat

Green area index (GAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) are key variables that are closely related to crop growth. Concurrent and continuous monitoring of GAI, LCC and CCC is critical to keep consistency among variables and make decisions for field precision managements. Previous studies have developed several instruments and algorithms to monitor continuous GAI, while the autonomous monitoring of three variables simultaneously has been lacking. This study presents a novel algorithm to retrieve daily GAI, LCC and CCC from continuous directional observations acquired by a fixed and economic affordable multi-band spectrometer (6 bands covering red, red-edge and near infrared domains) and a photosynthetically active radiation (PAR) sensor in the field. It is composed of three main steps, corresponding to three crucial questions when retrieving variables under natural environments using multi-band spectrometer installed on a near-surface platform: diffuse fraction in each spectral band, radiometric calibration and diurnal sun variation of daily acquisitions. First, we estimated diffuse fraction in each spectral band from the relationship with PAR diffuse fraction based on simulations of the 6S atmospheric radiative transfer model. Second, we computed the relative value of each band to the reference of mean of measurements on all six bands from near-surface measurements, in place of absolute radiometric calibration to limit the influence of changing illumination conditions. In the third step, we combined PROSAIL canopy radiative transfer model and kernel-driven models to retrieved GAI, LCC and CCC from artificial neural network using above spectral diffuse fraction and diurnal multi-angle relative observations. The algorithm was evaluated over 43 IoTA (Internet of things for Agriculture) systems that were installed in 29 wheat fields in France from March to May 2019. Results showed that our method provides good estimates of GAI with root mean square error (RMSE) of 0.54, relative RMSE (RRMSE) of 26.95%, R2 of 0.86, LCC (RMSE = 12.06 μg/cm2, RRMSE = 33.34%, R2 = 0.52) and CCC (RMSE = 0.23 g/m2, RRMSE = 24.58%, R2 = 0.93). This study shows great potentials for concurrent estimates of GAI, LCC and CCC from continuous ground measurements. It will be useful over other vegetations or other near-surface platforms for simultaneous estimations of biophysical variables.

Novel spectral indices for enhanced estimations of 3-dimentional flavonoid contents for Ginkgo plantations using UAV-borne LiDAR and hyperspectral data

Leaf flavonoid content (LFC) is a marked indicator of the protection signals from biotic and abiotic stresses, as well as the potential in the recovery of phenolic compounds from plants for producing potent antioxidants. LFC has been non-destructively retrieved from leaf reflectance spectra in recent studies. However, the LFC estimation from canopy-level spectra remains poorly understood and challenging arise from the confounding effects of other pigments and canopy structure. To address this limitation, this study proposed a suite of new 3-Dimentional spectral indices (SIs), in which the leaf-level standard flavonoid indices (FIs) are normalized by structure indices or chlorophyll indices. The hypothesis investigated is that these new SIs, derived from UAV-based hyperspectral point cloud data (fused by canopy hyperspectral images and LiDAR point cloud data), can enhance detecting LFC distribution within the canopies of Ginkgo plantations, by mitigating the effects of canopy structure and chlorophyll absorption. The results demonstrated that most chlorophyll-based normalized indices (CV-R2 = 0.56–0.65) outperformed the structure-based normalized indices (CV-R2 = 0.44–0.57) and the standard FIs (CV-R2 = 0.19–0.54). In specific, FI420,710/SR800,710 (CV-R2 = 0.65) out of chlorophyll-based normalized indices performed better than other indices. With the use of FI420,710/SR800,710, the 3-Dimentional distribution of LFC within Ginkgo canopies can be well mapped. In summary, this study indicates marked potentials of the developed normalized indices for mapping LFC distribution, as well as providing new insight into alleviating the confounding effects of chlorophyll and structure on LFC estimation of Ginkgo plantations, with simulations conducted by the canopy radiative transfer model.

Towards practical semi-empirical models for the estimation of leaf and canopy water contents from hyperspectral reflectance

The knowledge of crop water status provides valuable information for agricultural water management. Most remote sensing methods for estimating water contents are based on statistical models and spectral features in the shortwave infrared region where water absorption dominates. However, the effects of leaf structure (Ns) and dry matter (expressed as leaf mass per area, LMA) on the spectral features in this region are not fully considered when estimating leaf water content (LWC) and canopy water content (CWC). This issue would lead to the data-specific empirical model, which lacks generality and is not transferable among different conditions. To fill this gap, this study for the first time evaluated the Ns and LMA effects on the spectral features, including the widely used normalized difference (ND)-type vegetation index (VI) and wavelet feature (WF) from wavelet transform. The leaf and canopy radiative transfer model simulations showed that the ND-type VI was sensitive to Ns at both leaf and canopy levels, whereas WF was sensitive to LMA at the canopy level. For example, the relationships between WF and CWC varied clearly with the level of LMA, especially for high values of CWC. Next, the optimal water index (OWI = ND1200,1500) and optimal WF (OWF = WF1600,8) were determined by minimizing their correlations with the Ns and LMA based on the leaf and canopy simulations. Unlike the existing spectral features applicable to only leaf or canopy level alone, OWI and OWF showed more consistent performance on estimating LWC and CWC. OWI- and OWF-based semi-empirical models were calibrated using model simulations and then evaluated using the independent measured datasets. The semi-empirical model based on OWI exhibited moderate estimation accuracy of LWC (R2 = 0.62, RMSE = 24.33 g/m2) and CWC (R2 = 0.69, RMSE = 0.15 kg/m2), outperforming previous ND-type VIs. In comparison, the best estimation accuracies for LWC (R2 = 0.80, RMSE = 17.47 g/m2) and CWC (R2 = 0.85, RMSE = 0.11 kg/m2) were obtained by the OWF-based semi-empirical model when the prior information of LMA was incorporated at the canopy level. These findings have great potential for mapping crop water status over large regions with universal semi-empirical models and hyperspectral satellite imagery.

Simulating snow-covered forest bidirectional reflectance by extending hybrid geometric optical–radiative transfer model

Accurate simulations of the bidirectional reflectance distribution function (BRDF) of snow-covered forests are crucial for retrieving snow and canopy properties, which affects the analysis of net radiation, radiative forcing, and climate change. The anisotropic reflectance of snow in conifer forests is greatly influenced by the high reflectance of snow and the complex travel pathways of radiation in 3D canopy structures, which complicates BRDF simulations. Although bidirectional reflection models have been widely established for snow-free forests, few studies have attempted to simulate the BRDF in snow-covered forests. Therefore, we propose a new snow-covered forest bidirectional reflectance (SFBR) model by coupling geometric optical and radiative transfer models, which includes a snow anisotropic reflectance model (ART), needle leaf optical properties model (LIBERTY), canopy radiative transfer model (4SAIL), and geometric optical model (GOST2). The SFBR model mainly focuses on conifer forests with snow backgrounds; it can simulate anisotropic reflectance spectra from 400 nm to 2500 nm and is the only model to fully consider discontinuous canopy distribution, topographic effects, and snow anisotropy. The SFBR model was validated by simulating the reflectance over snow-covered forests, the results of which showed good consistency with simulations of 3D scene model and remote sensing observations. The proposed SFBR model has the potential to quantify radiation interactions between the snow background and forest overstory, evaluate the sensitivity of canopy reflectance to snow and forest properties, and retrieve snow-covered forest parameters from satellite-observed reflectance data.

The development of a global LAI and FAPAR product using GCOM-C/SGLI data

The Japan Aerospace Exploration Agency (JAXA) launched the Global Change Observation Mission - Climate (GCOM-C) satellite on December 23rd, 2017. As a part of the standard products of JAXA’s GCOM-C satellite, we developed the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product. In comparison to other global LAI and FAPAR products by the National Aeronautics and Space Administration (NASA) and the Copernicus Global Land Service (CGLS), our products have the following advantageous features: high spatial and temporal resolution (250 m and daily); the retrieval algorithm (look-up-table) is based on “the Forest Light Environmental Simulator” (FLiES) which is a full 3-D canopy radiative transfer model as well as a vegetation landscape model and a global forest landscape map created for this study. The algorithm and ancillary data sets are completely independent of the existing products that are more or less influenced by the MODIS LAI/FAPAR, providing an alternate and independent source of LAI and FAPAR. The dataset has been created since April 2018 and is freely available on JAXA’s G-Portal site (https://gportal.jaxa.jp/gpr). As a result of accuracy assessment with in-situ data, the Root Mean Square Errors (RMSEs) for total LAI and FAPAR were 0.776 and 0.112, respectively. We also compared our LAI and FAPAR estimates with three existing global datasets (PROBA-V/OLCI, MODIS and VIIRS product). The spatial distribution of LAI and FAPAR showed different characteristics among the datasets although their temporal trends were similar. The differences in estimation results among datasets due to differences in the retrieval algorithms, base map (or land cover data) and input data need to be evaluated in the future.

Row orientation influences the diurnal cycle of solar-induced chlorophyll fluorescence emission from wheat canopy, as demonstrated by radiative transfer modeling

Although multiple studies have used solar-induced chlorophyll fluorescence (SIF) of the crop canopy to estimate photosynthetic activity, the effect of canopy geometry (row orientation) on diurnal SIF remains uncertain. Using a combination of 3D canopy radiative transfer modeling and spectroscopic observations collected in a winter wheat field, we investigated (i) the relationships between observed SIF and factors including temperature, humidity, absorbed photosynthetically active radiation (APAR), and row orientation; (ii) the temporal pattern and multi-angle distribution of diurnal canopy SIF; and (iii) the mechanisms influencing components of diurnal SIF under the effect of row orientation. The relationship between observed SIF and APAR was relatively strong at the early growth period, but SIF was influenced by various factors (temperature, humidity, and row–sun angle) at the middle of the growth period. To test an asymmetric crop row effect as a potential cause of this skewness, we evaluated the modeled SIF with a 3D northwestward (315°) crop row alignment and other orientations. Each row orientation resulted in a trough-like depression parallel to the row in simulated canopy SIF, which could cause a diurnal asymmetry of canopy SIF, a fraction of fluorescence escape, and a fraction of APAR. View angles, the dimension of canopy objects (1D/3D), inter-row width, and leaf structure parameters impacted the representativeness of these simulations. An increased shade SIF, synchronized with the time when row–sun angles become zero at the incident sunlight being parallel to the crop row, was linked to decreasing canopy SIF emissions and would cause a non-linear SIF–APAR relationship at certain row orientations especially facing diagonally to the south. The concept of row orientation provides a novel viewpoint of diurnal canopy SIF from which to test the effect of row geometry in cropland, and will promote a mechanistic understanding of photosynthesis and canopy light use.

Evaluating the saturation effect of vegetation indices in forests using 3D radiative transfer simulations and satellite observations

Vegetation indices (VIs) have been used extensively for qualitative and quantitative remote sensing monitoring of vegetation vigor and growth dynamics. However, the saturation phenomenon of VIs (i.e., insignificant change at moderate to high vegetation densities) poses a known limitation to their ability to characterize surface vegetation over the dense canopy. Although the mechanisms underlying saturation are relatively straightforward and several VIs have been proposed to mitigate the saturation effect, the assessment of the saturation effect of VIs remains insufficient. Notably, no unified metric has been proposed to quantify the VI saturation phenomenon, limiting VI selection in practical applications. In this study, we proposed two indicators to describe the saturation phenomenon and utilized a well-validated three-dimensional (3D) canopy radiative transfer (RT) model large-scale remote sensing data and image simulation framework (LESS) to simulate the bidirectional reflectance factor (BRF) of six forests scenes and assessed the variations in VIs in relation to leaf area index (LAI) values over different backgrounds, sun-sensor geometries, and spatial distribution types. The saturation characteristics of 36 VIs were evaluated in combination with simulation results and satellite observations from multiple sensors. The ranking of VI saturation from simulated and satellite results revealed a good agreement. Our results indicated that the simple ratio vegetation index (SR) performed best with the highest saturation point and can well characterize the surface vegetation condition until LAI reaches 4. Besides, we found that the saturation effect of VIs was influenced by soil brightness, sun-sensor geometry, and canopy structure. SR, modified simple ratio (MSR) and normalized green red difference index (NGRDI) were the most susceptible to these disturbing factors, although they had higher resistance to saturation. Modified triangular vegetation index 1 (MTVI1), modified non-linear vegetation index (MNLI), triangular greenness index (TGI), and triangular vegetation index (TriVI) performed well overall, combining the ability to resist saturation and disturbance factors. Appropriate application of VIs can help better understand vegetation responses to climate change and accurately assess ecosystem status. Our results contribute to the understanding of the VI saturation effect and provide a combined model and satellite data experimental workflow in appropriate VI selection to accurately characterize vegetation.

Mapping foliar photosynthetic capacity in sub-tropical and tropical forests with UAS-based imaging spectroscopy: Scaling from leaf to canopy

Accurate understanding of the variability in foliar physiological traits across landscapes is critical to improve parameterization and evaluation of terrestrial biosphere models (TBMs) that seek to represent the response of terrestrial ecosystems to a changing climate. Numerous studies suggest imaging spectroscopy can characterize foliar biochemical and morphological traits at the canopy scale, but there is only limited evidence for retrieving canopy photosynthetic capacity (e.g., maximum carboxylation rate, Vc,max and maximum electron transport rate, Jmax). Moreover, the effect of canopy structure within forest communities on scaling up spectra-trait relationships from leaf to canopy level is not well known. To advance the spectra-trait approach and enable the estimation of key traits using remote sensing, we collected imaging spectroscopy data from an Unoccupied Aerial System (UAS) platform over two forest sites in China (a subtropical forest in Mt. Dinghu and a tropical rainforest in Xishuangbanna). At these sites, we also collected ground measurements of leaf spectra and traits, including biochemical (leaf nitrogen, phosphorus, chlorophyll, and water content), morphological (leaf mass per area, LMA) and physiological (Vc,max25 and Jmax25) traits (n = 135 tree-crowns from 42 species across two sites). Using a partial least-squares regression (PLSR) approach, we built and tested spectra-trait models with repeated cross-validation. The spectral models developed with leaf spectra were directly transferred to canopy spectra to evaluate the effect of canopy structure. We further applied canopy spectral models to map these traits at individual tree-crown scale. The results demonstrate that (1) UAS-based canopy spectra can be used to estimate Vc,max (R2 = 0.55, nRMSE = 11.79%), Jmax (R2 = 0.54, nRMSE = 12.34%), and five additional foliar traits (R2 = 0.38–0.60, nRMSE = 10.11–13.56%) at the tree-crown scale with demonstrated generalizability across two sites; (2) canopy structure strongly affects the spectra-trait relationships from leaf to canopy level, but the effects vary considerably across foliar traits and cannot be well captured by the 4SAIL canopy radiative transfer model. UAS-based imaging spectroscopy maps large variability in all foliar traits (including physiological traits) with spatially explicit information, reproducing the field-observed inter- and intra-specific variations. These results demonstrate the capability of using UAS-based imaging spectroscopy for characterizing the variability of foliar physiological traits at individual tree-crown scale over forest landscapes and highlight the similar generalizability but different biophysical mechanisms underlying spectra-trait relationships at leaf and canopy levels.

Towards uninterrupted canopy-trait time-series: A Bayesian radiative transfer model inversion using multi-sourced satellite observations

Canopy traits play an important role in plant growth, carbon sequestration, and the land surface water and energy balance, and thus it is important to understand and monitor how these traits change over time. Canopy radiative transfer models, which use canopy traits to predict spectra, provide a powerful means to understand canopy trait variation and these models can be inverted, allowing us to use remotely sensed data to monitor canopy traits. However, a single inversion represents a snapshot in time for a single sensor. In this study, we used a Bayesian approach to assimilate snow-free observations from three different sensors (Landsat 8, Sentinel-2A and MODIS) over the course of 2019 by inverting the ProSAIL radiative transfer model, producing a robust estimate of six canopy traits and their joint uncertainties at each time point. Next, we used a state-space time series model to integrate observations from multiple sensors to capture variation in vegetation optical traits throughout the year. The results demonstrate that our model can capture seasonal variation of surface reflectance with less than 5% RMSE compared with observed reflectance. Moreover, linking observations in time narrows the uncertainty intervals compared to the posterior distributions for individual time points. We are also able to make statistical inferences on dates when no data is available, and provide reasonable and reliable time-series inversion results compared with LAI ground measurements.

Bidirectional reflectance factor measurement of conifer needles with microscopic spectroscopy imaging

Leaf reflectance is widely used to retrieve leaf chlorophyll content (Cab) and parameterize canopy radiative transfer models. Measurements of broadleaf reflectance are typically made by using integrating sphere devices, but the approach is generally limited in conifer needle measurements due to the narrow needle coverage relative to the sample port of the integrating sphere. In this study, we proposed a method to measure the bidirectional reflectance factor (BRF) of needles by integrating a hyperspectral imaging spectrometer and an optical microscope. Pure needle pixels can be easily extracted from hyperspectral images after microscope magnification. The method was first validated by the narrow strips of broad leaves as proxies of needles, resulting in the difference between the measured BRF of strips and the BRF of whole broad leaves in the visible bands (400–700 nm) and in the near-infrared bands (700–1000 nm) being 0.0043 and 0.019, respectively. The method was also indirectly verified by tracking the variation in the natural needle BRF with its Cab, resulting in the change in BRF being consistent with the change in Cab. In addition, linear relationships between the needle Cab and the classic vegetation indices (i.e., the red-edge simple ratio and normalized difference vegetation index) calculated from the BRF were found. Apart from the above, we found that (1) the BRF of the abaxial side has a discrepancy with that of the adaxial side of needles, (2) the BRF of a needle varies with its azimuth, hence affecting the correlation between vegetation indices and Cab, and (3) filtering the specular reflection on the microsurfaces of the needle could enhance the correlation between the vegetation indices and needle Cab. The method is expected to accurately measure the BRF of needles and hence to improve the applications of needle optical properties in plant sciences, ecology, and remote sensing fields.

Estimating leaf mass per area with leaf radiative transfer model

Leaf mass per area (LMA) is an important leaf trait but challenging to be accurately estimated. This article proposes a simple leaf radiative transfer model called ISPECT. It explains the difference in optical properties observed on the adaxial (upper) and abaxial (lower) sides of leaves, i.e., their dorsiventrality, with a limited number of structural parameters. The performance of ISPECT in estimating LMA is compared to that of five other leaf radiative transfer models (FASPECT, DLM, PROSPECT-D, PROSPECT-5B, and Leaf-SIP). We tested six experimental datasets with 962 leaf samples and two spectral ranges: the solar domain (0.4–2.5 µm) and the shortwave infrared (1.7–2.4 µm). Results show that PROSPECT-D and PROSPECT-5B accurately estimate LMA using the shortwave infrared spectra, while ISPECT and FASPECT perform well in both spectral ranges. Further analysis demonstrates that leaf dorsiventrality is likely to be an influential factor for LMA estimation: thus ISPECT can accurately estimate LMA in the solar and shortwave infrared domains, with NRMSE of 26.0% and 28.8%, respectively. This motivates further studies on LMA mapping from spaceborne imaging spectrometers (e.g., PRISMA, GaoFen-5, EnMAP) by coupling ISPECT with canopy radiative transfer models.

Simulating Global Dynamic Surface Reflectances for Imaging Spectroscopy Spaceborne Missions: LPJ‐PROSAIL

AbstractSpectroscopic reflectance data provide novel information on the properties of the Earth's terrestrial and aquatic surfaces. Until recently, imaging spectroscopy missions were dependent mainly on airborne instruments, such as the Next Generation Airborne Visible InfraRed Imaging Spectrometer (AVIRIS‐NG), providing limited spatial and temporal observations. Currently, there is an emergence of spaceborne imaging spectroscopy missions, which require advances in end‐to‐end model support for traceability studies. To provide this support, the LPJ‐wsl dynamic global vegetation model is coupled with the canopy radiative transfer model, PROSAIL, to generate global, gridded, daily visible to shortwave infrared (VSWIR) spectra (400–2,500 nm). LPJ‐wsl variables are cross‐walked to meet required PROSAIL parameters, which include leaf structure, chlorophyll a + b, brown pigment, equivalent water thickness, and dry matter content. Simulated spectra are compared to a boreal forest site, a temperate forest, managed grassland, a dryland and a tropical forest site using reflectance data from tower‐mounted, aircraft, and spaceborne imagers. We find that canopy nitrogen and leaf‐area index are the most uncertain variables in translating LPJ‐wsl to PROSAIL parameters but at first order, LPJ‐PROSAIL successfully simulates surface reflectance dynamics. Future work will optimize functional relationships required for improving PROSAIL parameters and include the development of the LPJ‐model to represent improvements in leaf water content and canopy nitrogen. The LPJ‐PROSAIL model is intended to support missions such as NASA's Surface Biology and Geology and subsequent modeled products related to the carbon cycle and hydrology.

STMRT: A simple tree canopy radiative transfer model for outdoor mean radiant temperature

Tree canopy shading can significantly decrease the outdoor mean radiant temperature (MRT). However, canopy radiative transfer is quite complex, making it challenging to evaluate the MRT under a tree canopy. In this study, a simple tree canopy radiative transfer model (STMRT) was developed to determine the MRT under a tree canopy. In STMRT, the direct and diffuse solar radiation transfers are calculated based on the leaf inclination angle distribution, and the longwave radiation emitted from a canopy is evaluated based on the simplification that the canopy surface temperature is the same as the surrounding air temperature. The STMRT's accuracy was evaluated by measurement and compared to that of 4SAIL. The results demonstrate that the MRT differences (ΔMRT) in STMRT are typically less than 2.3 °C; in Shanghai, China, ΔMRT caused by the differences in direct solar radiation transmissivity between STMRT and 4SAIL are less than 2.0 °C, unless the leaf area index is less than 2 and the solar zenith angle is greater than 70°. According to STMRT, it is found that ΔMRT generated by the leaf inclination angle variances across various tree species can reach 9.5 °C; ΔMRT caused by a uniform distribution simplification for leaf inclination angle can reach 5.7 °C. STMRT will help plan the outdoor thermal environment by evaluating the MRT under tree canopies.