Heterogeneous Canopies Research Articles

Momentum Transport in Heterogeneous Forest Canopies

This study investigates the impact of spatial heterogeneity on momentum transport within forest canopies through wind tunnel experiments using 1:200 scale forest models. The models, crafted from 10 Pores Per Inch reticulated foam, emulate a leaf area index of 5.3 and include alternating patches and gaps of various sizes. Statistical results of the mean velocity profiles and velocity standard deviations show that the canopies develop a mixing layer. By employing lacunarity analysis to quantify spatial heterogeneity, we establish that the heterogeneity scale effectively represents variations in canopy height. The success of the lacunarity analysis as a metric is particularly noteworthy, providing a robust and practical measure of heterogeneity that can be easily applied in future research. Control volume analysis reveals that horizontal and vertical momentum advection terms rise as canopy heterogeneity increases, emphasizing its critical role in heterogeneous canopies and the possibility of describing this role using the lacunarity scale. The gaps also give rise to pressure terms through the local pressure gradient at each pattern. The study highlights the higher influence of gap size over heterogeneity scale on momentum flux. These insights contribute to improved parameterization of heterogeneous canopies in numerical weather prediction models, aiding in better representation of sub-grid scale processes and enhancing our understanding of canopy-atmosphere interactions.

Fine-scale retrieval of leaf chlorophyll content using a semi-empirically accelerated 3D radiative transfer model

Leaf chlorophyll content (LCC) retrieval from remote sensing imagery is essential for monitoring vegetation growth and stress in the agroforestry industry. Many remote sensing inversion methods for estimating LCC primarily rely on 1D radiative transfer models (RTMs) that abstract canopies into horizontal layers or simple geometric primitives. Yet, this methodology faces challenges when applied to heterogeneous canopies, particularly in fine-scale mapping where each pixel's reflectance is significantly influenced by its surroundings, e.g. crown shadows. While 3D RTMs hold promise for addressing these challenges by explicitly describing complex canopy structures, their computational demands and the complexity involved in parameterizing detailed 3D structures limit the generation of extensive training datasets, requiring simulations across numerous parameter combinations. In this study, we used a semi-empirically accelerated 3D RTM, termed Semi-LESS, with a 1D residual network to accurately retrieve leaf chlorophyll content (LCC) from UAV images and LiDAR data at a 3-m resolution. We first reconstructed structures of forest plots using UAV LiDAR point cloud, based on which, UAV images with varying leaf and soil optical properties are simulated using the Semi-LESS. Subsequently, a training dataset consisting LCC and its corresponding reflectance was generated from the simulated UAV images by focusing on sunlit pixels. A 1D residual network is trained using the training dataset for LCC estimation. For comparison, we also trained an estimation model using a dataset generated from PROSAIL. The results show that estimation model trained with Semi-LESS surpasses PROSAIL in retrieving LCC from both simulation datasets and field measurements of two forest plots. The RMSE of Semi-LESS was 5.40–6.92 µg/cm2 for simulation datasets and 8.21–9.76 µg/cm2 for field measurements, whereas PROSAIL exhibited lower accuracy with an RMSE of 7.76–9.83 µg/cm2 for simulation datasets and 12.76–13.06 µg/cm2 in field measurements. The results demonstrate that Semi-LESS coupled with deep learning is reliable and has great potential for LCC mapping using UAV images, which is particularly useful for fine-scale applications such as crop and orchard monitoring. This approach also highlights the impact of shadows on LCC retrieval.

Simulating High-Resolution Sun-Induced Chlorophyll Fluorescence Image of Three-Dimensional Canopy Based on Photon Mapping

The remote sensing of sun-induced chlorophyll fluorescence (SIF) is an emerging technique with immense potential for terrestrial vegetation sciences. However, the interpretation of fluorescence data is often hindered by the complexity of observed land surfaces. Therefore, advanced remote sensing models, particularly physically based simulations, are critical to accurately interpret SIF data. In this work, we propose a three-dimensional (3D) radiative transfer model that employs the Monte Carlo ray-tracing technique to simulate the excitation and transport of SIF within plant canopies. This physically based approach can quantify the various radiative processes contributing to the observed SIF signal with high fidelity. The model’s performance is rigorously evaluated by comparing the simulated SIF spectra and angular distributions to field measurements, as well as conducting systematic comparisons with an established radiative transfer model. The results demonstrate the proposed model’s ability to reliably reproduce the key spectral and angular characteristics of SIF, with the coefficient of determination (R2) exceeding 0.98 and root mean square error (RMSE) being less than 0.08 mW m−2 sr−1 nm−1 for both the red and far-red fluorescence peaks. Furthermore, the model’s versatile representation of canopy structures, enabled by the decoupling of radiation and geometry, is applied to study the impact of 3D structure on SIF patterns. This capability makes the proposed model a highly attractive tool for investigating SIF distributions in realistic, heterogeneous canopy environments.

A Wind Tunnel Test for the Effect of Seed Tree Arrangement on Wake Wind Speed

Changes in canopy structures caused by harvesting and regeneration practices can significantly alter the wind environment. Therefore, it is essential to understand the wind patterns influenced by seed tree arrangements for predicting seed dispersal by winds and ensuring the success of natural regeneration. This study aimed to identify how wind speed responds to seed tree arrangement designs with differing horizontal distances, vertical positions, and free-stream wind speeds. A wind tunnel test was conducted using pine saplings for a scale model of various seed tree arrangements, and the change in wake speed was tracked. The wake’s relative wind speed averaged 71%, ranging from 3.5% to 108.5%, depending on the seed tree arrangement, distance from saplings, and vertical position. It peaked within the patch of three seed trees compared to other arrangements and at the top canopy layer. The empirical function effectively described the wind speed reduction and recovery with distance from saplings. For instance, the minimum wind speed was reached at 0.6–2.2 times the canopy height, and a wind speed reduction of over 20% of the free-stream wind speed was maintained at a 1.6–7.6 canopy height. A negative relationship between the seed tree leaf area and the relative wind speed was observed only at the top canopy layer. This study presents empirical evidence on the patterns of wake winds induced by different types of heterogeneous canopy structures.

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.

Hyperspectral Leaf Area Index and Chlorophyll Retrieval over Forest and Row-Structured Vineyard Canopies

As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0.92–2.15, NRMSD = 40–67%, bias = −0.64–0.96) and CCC (RMSD = 0.27–1.27 g m−2, NRMSD = 64–84%, bias = −0.17–0.89 g m−2). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0.88–1.64, NRMSD = 27–64%, bias = −0.78–−0.13) and CCC (RMSD = 0.30–0.87 g m−2, NRMSD = 52–73%, bias = 0.03–0.42 g m−2) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation.

Flow Structure Of A Heterogeneous Seagrass Canopy

Seagrass canopies are widely recognized for their ecosystem services (Barbier et al., 2011) including flow and wave attenuation (Ondiviela et al., 2014), sediment stabilization (De Boer, 2007), carbon sequestration (Duarte & Krause-Jensen, 2017) and nutrient deposition (Gacia et al., 2002). Over the past decades, various studies have delved into the modifications imparted by a seagrass canopy under both unidirectional and oscillatory flow at various scales. However, until recently, the majority of studies have either reported on or modeled seagrass canopies without considering the heterogeneity of species within a single canopy. While simplified prototype models offer valuable insights into system dynamics, they can introduce biases, potentially leading to the reduction or amplification of relevant physical processes (Tinoco et al., 2020). For instance, the study by Weitzman et al. (2015) emphasized the significance of considering vertical heterogeneity in a canopy. They noted that the presence of an understory can modulate near-bed flow processes differently, with flow attenuation significantly increased due to the understory's presence. However, embedded physical mechanisms and quantitative limitations in ecosystem functioning and services are only little understood. Such a knowledge gap is pointed out in the recent review by Risandi et al. (2023) regarding field studies of seagrass meadows in Indonesia. Only few studies explore the interaction between such a heterogeneous canopy (Short et al., 2011) and hydrodynamics despite the abundance of seagrass species in the region (McKenzie et al., 2020). Through physical modeling of four different species of seagrass this study aims to improve the understanding of flow structure modulated by a heterogeneous canopy under unidirectional currents at full scale.

Analysis of flux footprints in fragmented, heterogeneous croplands

An accurate quantification of fluxes from heterogeneous sites and further bifurcation into contributing homogeneous fluxes is an active field of research. Among such sites, fragmented croplands with varying surface roughness characteristics pose formidable challenges for footprint analysis. We conducted two flux monitoring experiments in fragmented croplands characterized by two dissimilar surfaces with objectives to: (i) evaluate the performance of two analytical footprint models in heterogeneous canopy considering aggregated roughness parameters and (ii) analyze the contribution of fluxes from individual surfaces under changing wind speed. A set of three eddy covariance (EC) towers (one each capturing the homogenous fluxes from individual surfaces and a third, high tower capturing the heterogeneous mixed fluxes) was used for method validation. High-quality EC fluxes that fulfill stationarity and internal turbulence tests were analyzed considering daytime, unstable conditions. In the first experiment, source area contribution from a surface is gradually reduced by progressive cut, and its effect on high-tower flux measurements is analyzed. Two footprint models (Kormann and Meixner ‘KM’; analytical solution to Lagrangian model ‘FFP’) with modified surface roughness parameters were applied under changing source area contributions. FFP model has consistently over predicted the footprints (RMSEFFP = 0.31 m−1, PBIASFFP = 19.00), whereas KM model prediction was gradually changed from over prediction to under prediction towards higher upwind distances (RMSEKM = 0.02 m−1, PBIASKM = 8.50). Sensitivity analysis revealed that the models are more sensitive to turbulent conditions than surface characteristics. This motivated to conduct the second experiment, where the fractional contribution of individual surfaces (α and β) to the heterogeneous fluxes measured by the high tower (T3) was estimated using the principle of superposition (FT3 = α FT1 + β FT2). Results showed that α and β are dynamic during daylight hours and strongly depend on mean wind speed (U) and friction velocity (u*). The contribution of fluxes from adjoining fields [1 − (α + β)] is significant beyond 80% isopleth. Our findings provide guidelines for future analysis of fluxes in heterogeneous, fragmented croplands.

Terrestrial laser scanning vs. manual methods for assessing complex forest stand structure: a comparative analysis on plenter forests

In continuous cover forestry, plenter silviculture is regarded as an elaborated system for optimizing the sustainable production of high-quality timber maintaining a constant but heterogeneous canopy. Its complexity necessitates high silvicultural expertise and a detailed assessment of forest stand structural variables. Terrestrial laser scanning (TLS) can offer reliable techniques for long-term tree mapping, volume calculation, and stand variables assessment in complex forest structures. We conducted surveys using both automated TLS and conventional manual methods (CMM) on two plots with contrasting silvicultural regimes within the Black Forest, Germany. Variations in automated tree detection and stand variables were greater between different TLS surveys than with CMM. TLS detected an average of 523 tree stems per hectare, while CMM counted 516. Approximately 9.6% of trees identified with TLS were commission errors, with 6.5% of CMM trees being omitted using TLS. Basal area per hectare was slightly higher in TLS (38.9 m3) than in CMM (38.2 m3). However, CMM recorded a greater standing volume (492.7 m3) than TLS (440.5 m3). The discrepancy in stand volume between methods was primarily due to TLS underestimating tree height, especially for taller trees. DBH bias was minor at 1 cm between methods. Repeated TLS inventories successfully matched an average of 424 tree positions per hectare. While TLS adequately characterizes complex plenter forest structures, we propose enhancing this methodology with personal laser scanning to optimize crown coverage and efficiency and direct volume measurements for increased accuracy of wood volume estimations. Additionally, utilizing 3D point cloud data-derived metrics, such as structural complexity indices, can further enhance plenter forest management.

A rapid method for computing 3-D high-resolution vegetative canopy winds in weakly complex terrain

To determine near-surface winds within and above vegetation canopies for operational environmental applications, a wind model must run at high-resolution (O(1–10 m)), in a few minutes, using limited input information, and requiring minimal computing resources (e.g., personal computers). Current research models simulate large domains at coarse resolution or small domains at fine scale, but canopy simulations can take days. Fast-modeling approaches are used to solve large complex wind fields, but they oversimplify the roughness elements’ distribution impact on momentum exchanges. To overcome these deficits, the fast-running wind model QUIC-URB (Quick Urban and Industrial Complex) was augmented with a high-resolution canopy wind solver. The wind model includes a non-local factor that describes how momentum propagates through the canopy and how sub-canopy jets appear under certain conditions. QUIC-URB was also coupled with the mesoscale WRF (Weather Research and Forecasting) model to downscale wind fields from a few kilometers to a meter. The new QUIC Canopy Model resolves 3-D wind fields over hundreds of millions of cells in less than 30 s per time step on a personal computer. It was compared to two canopy models for real quasi-homogeneous and heterogeneous canopies. An error analysis shows that the model was relatively accurate with a normalized root-mean-square error of about 0.2 m s−1 in the quasi-homogeneous canopy, and a mean absolute error of 0.3 m s−1. The new model is suitable for coupling with pollution dispersion, wildfire spread, and numerical weather prediction models over weakly complex terrain, defined here as a mildly undulating environment with gradual changes in elevation and a heterogeneous distribution of plants.

Flow Structures in Open Channels with Emergent Rigid Vegetation: A Review

On the edges of rivers where the flow velocity is low, aquatic plants flourish, with emergent rigid herbs being the most common. Since the flow structures of vegetated flow are strongly influenced by vegetation distribution patterns, homogeneous and heterogeneous canopies are defined based on the characteristics of vegetation distribution. A review summarizing recent advances in flow structures under the influence of different types of canopy arrangements, including ribbon-like homogeneous canopies, ribbon-like heterogeneous canopies, and patched heterogeneous canopies, is needed. Their flow development process, shear layer properties, coherent structure features, and momentum exchange characteristics are summarized, and a future research agenda for an in-depth understanding of the interactions between vegetation and flow is also highlighted.

A Modified Beer–Lambert–Bouguer Law for Nonrandom Distributions and Its Application in Gap Probability Calculations for Heterogeneous Canopies

AbstractThe three‐dimensional (3D) structure of vegetation canopy strongly influence the absorption and reflection of radiation at the land surface. The gap probability is one primary indicator of the canopy structure. For homogeneous canopies, where the plant elements are distributed completely randomly and statistically independent, the gap probability can be modeled with the Beer–Lambert–Bouguer law. However, this method is not suitable for nonrandom distributions. A physically based and computationally efficient algorithm is developed to address this limitation, by introducing the pair correlation function to describe the nonrandom distribution pattern. Pair correlation function is based on the distribution of distances of pairs of points, and can therefore describe random, clumped, or regular distribution patterns, and detect mixed patterns at different scales. In this paper, we take tree crown as the basic structure element, and apply the new method to calculate the gap probability. The simulations agree well with results obtained by the ray casting method for various distribution patterns. The results show that horizontal distribution of trees is one of the key factors impacts the gap probability. For the case when different patterns coexist on various scales, we can ignore the distribution pattern at larger scale, and focus on the pattern at smaller scale. When zenith angel is large, the azimuthal dependency of the gap probability should be noted.

Tree species identity, canopy structure and prey availability differentially affect canopy spider diversity and trophic composition

Forest canopies maintain a high proportion of arthropod diversity. The drivers that structure these communities, however, are poorly understood. Therefore, integrative research connecting tree species identity and environmental stand properties with taxonomic and functional community composition of canopy arthropods is required. In this study, we investigated how the taxonomic, functional and trophic composition of arboreal spider communities is affected by tree species composition and associated differences in canopy structure and prey availability in temperate forests. We sampled canopy spiders as well as their potential prey using insecticidal fogging in monospecific and mixed stands of native European beech, native Norway spruce and non-native Douglas fir. Trophic metrics were obtained from stable isotope analysis and structural canopy properties were assessed with mobile laser scanning. Monospecific native spruce stands promoted local canopy spider abundance and diversity, but native beech and beech–conifer mixtures had the highest diversity at landscape scale. Spider community composition differed between monospecific stands, with broadleaf–conifer mixtures mitigating these differences. Irrespective of tree species identity, spider abundance, taxonomic diversity, functional richness and isotopic richness increased in structurally heterogeneous canopies with high prey abundances, but functional evenness and trophic divergence decreased. Our study shows that canopy spiders are differentially affected by tree species identity, canopy structure and prey availability. Broadleaf–conifer mixtures mitigated negative effects of (non-native) conifers, but positive mixture effects were only evident at the landscape scale. Structurally heterogeneous canopies promoted the dominance of only specific trait clusters. This indicates that intermediate heterogeneity might result in high stability of ecological communities.

A modified Beer–Lambert–Bouguer law for nonrandom distributions and its application in gap probability calculations for heterogeneous canopies

A modified Beer–Lambert–Bouguer law for nonrandom distributions and its application in gap probability calculations for heterogeneous canopies

Conditional statistics of Reynolds stress in open channel flows with modeled canopies of homogeneous and heterogeneous density

The flow structures under the effects of heterogeneous canopies have been shown to be significantly different from those under the effects of homogeneous canopies. The purpose of this study is to investigate how the changes in density and density uniformity of the canopy affect the turbulent characteristics of the flow in a partially vegetated channel. A comparative experiment is conducted, including two cases of homogeneous canopy with different densities and one case of heterogeneous canopy consisting of alternating sparse and dense vegetation patches. While the lateral profiles of Reynolds stress, magnitudes of quadrant motions, and high-order moments of velocity fluctuations present a high similarity within the shear layer, variations in both the density and density uniformity of the canopy markedly affect the turbulence at the interface between the canopy and the main channel. The results show that canopy density heterogeneity enhances the momentum exchange at the interface and promotes the penetration of stress-driven flow into the sparse vegetation patch while inhibiting its penetration into the dense vegetation patch. An analogy can be drawn between the canopy flow with sufficient density and the turbulent rough-wall boundary layers based on the turbulent statistics within the shear layer. Furthermore, the effect of increased canopy density on the flow corresponds well to the effect of decreased wall roughness. By using the cumulant expansion method, the assumption of structural similarity present in wall-bounded flows is found to be applicable to the canopy flows considered in this study.

The role of height increment and marginal height cost in the production ecology of Eucalyptus plantations

In forest production ecology, growth has been separated into a tree’s capacity to absorb radiant energy, and its efficiency in converting absorbed radiant energy into biomass or stem volume. Stand production is also be expressed a function of height growth and the stem size required to physically and physiologically support height growth. Each additional increment in height requires a corresponding increment in stem size that can be considered a marginal cost. Since tree height is linked to light absorption, variation in height increment and marginal height cost should be related to the distribution of stem growth as well. This hypothesis was evaluated with data collected in a long-term study with fast-growing eucalyptus in a plantation setting. The experiment was designed to compare the effects of homogeneous and heterogeneous canopies created by varying planting date. Indigenous sources of variation due to genotypic, microsite variation, and spacing were controlled to the extent possible. Within-plot variation of stem growth (Sim), height increment (Sih), and marginal height cost (Sim/ih) were quantified with a symmetry index calculated as the cumulative proportional value of the growth variables for the tree with median height minus 0.5. Negative values indicate positively skewed distribution. In most cases the three symmetry indexes for the heterogeneous-planting treatment were more negative than those for the uniform treatment corresponding to the induced variation in height. Among the set of symmetry indexes for a canopy treatment, the symmetry index for height increment were the least negative and the indexes for stem growth were the most negative. A separate effect of canopy treatment was detected indicating the not all the variation in Sim could be explained by Sih and Sim/ih. Based on past studies, the independent effect of canopy heterogeneity on Sim could be due to a mix of mechanical perturbation and shade avoidance effects. For both canopy treatments, a constant difference between the symmetry indexes for height increment and marginal height cost was evident in the data, suggesting an interaction between height growth capacity and marginal height cost in these plantations. Previous studies also show interactions between resource-based components of capacity and efficiency variables. Identifying the physiological mechanisms responsible for these interactions may be a new direction for forest production ecology studies.

Canopy structure from space using GEDI lidar

Canopy structure from space using <scp>GEDI</scp> lidar

Performance of analytical footprint models in heterogeneous landscapes under varying atmospheric stability conditions

Analytical footprint models that simulate the source area of scalar fluxes generally include a fundamental assumption that the fluxes originate from a horizontal, homogeneous surface. It is widely understood that this assumption is often violated in flux studies, especially for sites where there are significant variations in topography, leaf area, photosynthetic pathway and underlying soil properties. An accurate interpretation of the measured flux footprint under heterogeneous canopy condition can help alleviate the problem. We evaluated the performance of analytical models (Hsieh, K&amp;M, and Schuepp) under stable and unstable atmosheric conditions for the homoeneous canopy (Cotton- C3, zm = 3m and Sugarcane- C4, zm = 4m) and heterogeneous canopy (mixed fetch) compared to FFP model in a complex sugarcane-cotton (C3-C4) cropping system. Performance of models were evaluated using a set of three eddy covariance (EC) towers (one each capturing homogenous C3 and C4 fluxes, and a third capturing heterogeneous, mixed (C3-C4) fluxes at zm = 8m). High-quality EC fluxes that fulfil stationarity and internal turbulence tests were analyzed on the basis of daytime, unstable condition datasets. K&amp;M model (Corr &gt;0.75 , RMSE &lt;0.06 , SD &lt;0.006) performed the best in comparison to FFP model flux footprint prediction under unstable atmospheric condition in heterogeneous canopy condition with respect to Hsieh (Corr &lt;0.6, RMSE &gt;0.01 , SD &gt;0.005), and Schuepp analytical model (Corr =0.2, RMSE &lt;0.01, SD&gt;0.2 ). Unstable atmospheric condition is further classified into four categories (neutral, near neutral unstable, unstable, and very unstable). Relative performance of the analytical models was further analyzed with experimental flux tower generated flux footprint under neutral, near neutral unstable, unstable, and very unstable atmospheric condition. FFP model performs the best in heterogeneous canopy condition under varying neutral to very unstable atmospheric condition. We make clear recommendations for future analysis of fluxes in heterogeneous crop lands under varying atmospheric stability condition.

Optimizing the Protocol of Near-Surface Remote Sensing Experiments Over Heterogeneous Canopy Using DART Simulated Images

Optical canopy models that connect land surface properties and satellite-observed radiance must be validated before being used. These models include the bidirectional reflectance distribution function (BRDF) models in the visible and near-infrared domains, and directional brightness temperature (DBT) models in the thermal infrared domain. Near-surface experiments have been extensively conducted to evaluate the modeling accuracy, including ground-, tower-, and aircraft-based measurements. Indeed, it should be noted that in situ measured BRDF/DBT results are sensitive to the experiment protocol, such as sensor moving orientation, flight height, and sampling frequency. A practical tool for optimizing the in situ measurement protocols is needed in the community of remote sensing modeling. For that, we devised a virtual experiment framework based on the discrete anisotropic radiative transfer (DART) 3-D radiative transfer model that is capable of simultaneously simulating both the BRDF/DBT pattern and the images acquired by in situ cameras. Here, as an optimization case, we use it to determine the optimal sensor flight orientation over heterogeneous vegetated canopies (a row-planted scene with three solar angles and a discrete scene with three solar angles) for measuring their DBT distribution. Results showed considerable errors (i.e., image-extracted DBT minus DART-simulated DBT) exist for sensor flight orientation along the canopy rows ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> = 0.24 and root mean square error (RMSE) = 4.32 K), and they become much smaller ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> = 0.94 ~ 0.98 and RMSE = 0.82 ~ 1.03 K) in other typical orientations (e.g., cross row plane, solar principal plane, and cross solar principal plane). The critical azimuth offset relative to the row direction that can ensure an acceptable RMSE < 1 K is quantified as atan(3*Unitwidth/Scenesize) based on a series of intensive simulations by this new tool. However, the RMSE of the discrete scene is not sensitive to the flight orientation. Such accuracy differences in various protocols were experimentally verified over row-planted maize using a 4-D tower in Huailai, Hebei, China. The result highlights the great potential of this newly designed DART-based virtual experiment to optimize near-surface experiment protocols.

Simulation of solar-induced chlorophyll fluorescence in a heterogeneous forest using 3-D radiative transfer modelling and airborne LiDAR

Solar-induced chlorophyll fluorescence (SIF) provides a means to estimate plant photosynthetic activities and to detect early plant stress. The accurate quantification of SIF emitted by various scene components (tree crowns and background) may significantly improve the interpretation of top-of-canopy SIF (SIFtoc) measurements made over heterogeneous canopies. To do so, a three-dimensional (3-D) canopy SIF model (FluorFLiES) was introduced by coupling the excitation-fluorescence matrices (EF-matrices) with a 3-D Monte Carlo canopy radiative transfer model (Forest Light Environmental Simulator, FLiES). A tool was developed to construct forest canopy scene components from LiDAR data and enable their simulated contributions in structurally complex forest scenes. FluorFLiES is able to quantify SIF measurements with good accuracy at both half-hourly (R2 = 0.72, RMSE = 0.26 mW m−2 sr−1 nm−1) and daily (R2 = 0.83, RMSE = 0.19 mW m−2 sr−1 nm−1) scales. This study showed that non-photosynthetic elements in tree crowns, the fractional vegetation cover (FVC), and the background (including understory vegetation and soils) had a strong influence on SIFtoc intensity. Non-photosynthetic woody material suppressed the propagation of photons within crowns, thereby decreasing SIFtoc by around 10%. The canopy background made a significant contribution to SIFtoc in the NIR region by scattering downward SIF photons upward, and the background contribution increased rapidly with decreasing FVC: SIFtoc increased two-fold from 0.15 to 0.3 mW m−2 sr−1 nm−1 when ground leaf area index increased from 0.5 to 1.5 m2/m2. The results showed that the fluorescence escape ratio (fesc), a key variable relating observed SIFtoc to photosynthesis CO2 rate, contained a contribution from the background with a magnitude of 42%, even for relatively dense forest canopies. Assuming fesc simulated by the FluorFLiES model as a reference value, this study demonstrated that the current reflectance-based approach may cause large uncertainties (29%) when understory vegetation and/or FVC changes, largely due to neglecting the contribution of background elements. This study highlights the need to separate scene components and to consider multiple scattering within/among these components in interpreting the SIFtoc signal when working with heterogeneous ecosystems. The source code of FluorFLiES is available for further benchmarking.