Bidirectional Reflectance Factor Research Articles

MART3D: A Multilayer Heterogeneous 3D Radiative Transfer Framework for Characterizing Forest Disturbances

The utilization of radiative transfer models for interpreting remotely sensed data to evaluate forest disturbances is a cost-effective approach. However, the current radiative transfer modeling approaches are either too abstract (e.g., 1D models) or too complex (detailed 3D models). This study introduces a novel multilayer heterogeneous 3D radiative transfer framework with medium complexity, termed MART3D, for characterizing forest disturbances. MART3D generates 3D canopy structures accounting for the within-crown clumping by clustering leaves, which is modeled as a turbid medium, around branches, applicable for forests of medium complexity, such as temperate forests. It then automatically generates a multilayer forest with grass, shrub and several layers of trees using statistical parameters, such as the leaf area index and fraction of canopy cover. By employing the ray-tracing module within the well-established LargE-Scale remote sensing data and image Simulation model (LESS) as the computation backend, MART3D achieves a high accuracy (RMSE = 0.0022 and 0.018 for red and Near-Infrared bands) in terms of the bidirectional reflectance factor (BRF) over two RAMI forest scenes, even though the individual structures of MART3D are generated solely from statistical parameters. Furthermore, we demonstrated the versatility and user-friendliness of MART3D by evaluating the band selection strategy for computing the normalized burn ratio (NBR) to assess the composite burn index over a forest fire scene. The proposed MART3D is a flexible and easy-to-use tool for studying the remote sensing response under varying vegetation conditions.

Diversity of 3D APAR and LAI dynamics in broadleaf and coniferous forests: Implications for the interpretation of remote sensing-based products

Forests substantially mediate the water and carbon dioxide exchanges between terrestrial ecosystems and the atmosphere. The rate of this exchange, including evapotranspiration (ET) and gross primary production (GPP), depends mainly on the underlying vegetation type, health state, and the composition of abiotic environmental drivers. However, the complex 3D structure of forest canopies and the inherent top-view perspective of optical and thermal remote sensing complicate remote sensing-based retrievals of biotic and abiotic factors that eventually determine ET and GPP. This study investigates the sensitivity of remote sensing approaches to 3D variation of abiotic and biotic environmental drivers. We use 3D virtual scenes of two structurally different Swiss forests and the radiative transfer model DART to simulate the 3D distribution of solar irradiance and reflected radiance in the forest canopy. These simulations, in combination with LiDAR data, are used to derive the absorbed photosynthetic active radiation (APAR) and the leaf area index (LAI) in 3D space. The 3D variation of both parameters was quantified and analyzed. We then simulated images of the top-of-canopy bi-directional reflectance factor (BRF) and compared them with the hemispheric-conical reflectance factor (HCRF) data derived from HyPlant airborne imaging spectrometer measurements. The simulated BRF data was used to derive APAR and LAI, and the results were compared to their respective 3D representations. We unravel considerable spatial differences between both representations. We discuss possible reasons for the disagreement, including a potential insensitivity of the inherent top-of-canopy view for the real 3D product dynamics and limitations of the processing of remote sensing data, especially the approximation of effective surface irradiance. Our results can help understanding sources of uncertainties in remote sensing based gas exchange products and defining mitigation strategies.

Improving retrieval of leaf chlorophyll content from Sentinel-2 and Landsat-7/8 imagery by correcting for canopy structural effects

Accurately estimating leaf chlorophyll content (Chl) at large spatial scales is crucial for monitoring agricultural production and plant photosynthesis. Sentinel-2 and Landsat-7/8 offer the potential to assess Chl with high spatial resolutions using various physically-based, empirical, and hybrid methods, but they still present challenges due to the confounding effects of canopy structure and soil background. The near-infrared reflectance of vegetation (NIRV) has been proposed to characterize canopy structural and soil background effects in sun-induced chlorophyll fluorescence. Therefore, in this study, we developed a novel strategy that incorporates NIRV to minimize canopy structural and soil background effects on Chl retrieval, using bidirectional reflectance factor (BRF) from Sentinel-2 and Landsat-7/8 imagery. We compared Chl estimation using empirical random forest (RF), the physically-based PROSAIL-5B inversion, and a hybrid method using RF to train simulated datasets from PROSAIL-5B. We validated Chl retrieval performance with measured datasets from the National Ecological Observatory Network, covering seven vegetation types. We also compared spatiotemporal Chl variations from Sentinel-2, Landsat-7/8, and MODIS. The results showed that NIRV effectively characterized the canopy structural effect on Chl estimation, associated with leaf area index, average leaf angle, and canopy fraction of senescent leaves. Compared to BRF, correcting BRF by NIRV improved Chl retrieval with RMSE values reduced by 12.2%–27.0% for Sentinel-2, 7.4%–16.4% for Landsat-7, and 6.9%–13.7% for Landsat-8, respectively. This also resulted in a significant increase in R2 values for Sentinel-2 (0.13–0.46), Landsat-7 (0.08–0.19), and Landsat-8 (0.12–0.2), respectively. Sentinel-2 outperformed Landsat-7/8 in Chl estimation, due to the contribution of red edge bands and a higher spatial resolution. Furthermore, the hybrid method performed best for Chl estimation from Sentinel-2 and Landsat-7/8 with relative RMSE values of 17.33%, 20.22%, and 19.23%, respectively. Our hybrid method demonstrated lower model uncertainty with smaller variations of RMSE values across varying biochemical and structural traits and solar and viewing angles, and exhibited more consistent spatiotemporal variations of Chl from Sentinel-2 with phenology compared to Landsat-7/8 and the published MODIS Chl data. Our hybrid method demonstrates significant potential in mitigating canopy structural effects, enhancing Chl retrieval, and facilitating the development of Chl data from various satellite imagery sources.

Modeling the hotspot effect for vegetation canopies based on path length distribution

The hotspot effect, which refers to the increased reflectance in the solar direction, relies on the canopy structure. Numerous bi-directional reflectance distribution function (BRDF) models have been developed with careful attention to the hotspot effect. However, different assumptions may lead to very different shapes of BRDF, especially in the hotspot direction. With the development of high-resolution remote sensing satellites and three-dimensional (3D) mapping technologies, abundant 3D structural vegetation canopy information is available. However, traditional analytical models might not fully use these kinds of information. As a result, most of them are still very limited by their assumptions, such as the shape and spatial distribution of crowns. One BRDF model may do well only for one vegetation type and shows significant errors under other scenarios. We propose a new model named PATH_RT, which can model the hotspot effect at both the leaf and crown levels using the path length distribution (PLD). PLD can be easily obtained with the 3D information of the canopy at given remote sensing geometries. We validated the bi-directional reflectance factor (BRF) predicted by the PATH_RT model with the simulated scenes and field measurements which were produced by a 3D radiative transfer model LESS and UAV observations respectively. Compared to traditional analytical models SAILH, 4SAIL2, GORT, and 5SCALE, the PATH_RT model exhibits the highest accuracy, reducing the average RMSE by 86%, 66%, 52%, and 58% for the hotspot region respectively. PATH_RT accurately depicts the hotspot effects at the canopy and leaf levels and is highly efficient (99%, 75%, 86%, 99%, and 99% less time spent for one repeat run compared to LESS, SAILH, 4SAIL2, GORT, and 5SCALE models, respectively). Considering its high accuracy and efficiency, PATH_RT is expected to improve the accuracy and efficiency of remote sensing inversion.

3D Monte Carlo surface-atmosphere radiative transfer modelling with DART

Significant errors can arise if the adjacency effect (i.e., the contribution of neighbouring pixels to the radiance of a pixel) is neglected in the interpretation of remote sensing images. For example, adjacency radiances can account for >30% of the signal at the top of the atmosphere (TOA) of a white sand-lined coastline (Bulgarelli and Zibordi, 2018). This paper shows that 3D radiative transfer (RT) modelling can quantify this phenomenon. It presents a new 3D Monte Carlo surface-atmosphere RT modelling in the DART RT model, and a resulting virtual 3D Earth-Atmosphere laboratory for accurate simulation of atmospheric RT, including the adjacency effect. It was first validated with the atmosphere model SMART-G for 2D scenes: relative difference is 0.20% in TOA directional reflectance of infinite black surface for solar zenith equal to 60°, and 0.03% in TOA nadir reflectance of a black disc inside infinite white Lambertian surface, for solar zenith equal to 0° and 30°. Then, the adjacency effect on the TOA radiance and TOA albedo of a 3D scene was studied for a circular city (radius 2 km) surrounded by a forest (dimension 10 km), for four Sentinel-2A bands (blue, green, red, near infrared). Its contribution to TOA nadir radiance reaches ∼20% in the near infrared band, and increases with viewing zenith angle (VZA): ∼60% if VZA = 80°. The 3D scene TOA albedo was calculated after adapting the DART Bidirectional Reflectance Factor (BRF) camera to simulate TOA radiance for all upward directions at any angular resolution. For Sentinel-2A’s four bands, the adjacency effect influences the TOA albedo of the circular city by up to 10% for the green band and up to 27% for the near infrared band, well above the maximum uncertainty 5% usually required in land surface applications. The adjacency effect of the city neighbourhood 3D structure was studied by replacing it by a Lambertian surface with its albedo. The city nadir TOA radiance changed by up to 1.3% with very small change in TOA albedo (<0.5%). This new modelling greatly improves DART potential for accurate simulation of atmospheric effects. It is in the DART version freely available for research and education (https://dart.omp.eu).

Retrieving BRDFs from UAV-based radiometers for fiducial reference measurements: caveats and recommendations

Surface Bidirectional reflectance distribution function (BRDF) is a key intrinsic geophysical variable depending only on the characteristics of the observed medium. It is therefore the most suitable measurand to support the definition of fiducial reference measurements (FRM). Field acquisition of surface reflectance data relies on substantial assumptions and simplifications, often without accounting for their impact. For example, the BRDF is a theoretical concept and can never be measured in the field. In contrast, the hemispherical conical reflectance factor (HCRF), which is the measurand obtained during field campaigns, is impacted by all scene elements and is not intrinsic to the surface. This study analyses the impact of four parameters (atmospheric scattering, measurement device field of view cropping, acquisition duration, non-Lambertian reference panels) on HCRF estimation. Simulations are performed on a 3D vegetation scene, using the new radiative transfer model Eradiate. It is found that among the aforementioned parameters, atmospheric scattering alone leads to a relative root-mean-square error (RRMSE) of more than 10% between HCRF and reference Bidirectional reflectance factor (BRF).

Reconstructing the digital twin of forests from a 3D library: Quantifying trade-offs for radiative transfer modeling

Radiative transfer models that use spatially explicit 3D models to represent forest structure can simulate highly realistic Earth Observation (EO) data. Such simulations at the forest stand scale (≥ 1-ha) allow for more direct calibration and validation of EO products. Explicitly reconstructing 3D forest structures at scales that can be compared directly with satellite EO data (i.e., dozens to hundreds of meters) is challenging. Reconstructing large forest areas (≥ 1-ha) using a representative subset (i.e., forest subsampling) is a potentially more practical and feasible method. However, the impacts of forest subsampling on radiative transfer (RT) modeling were never formally tested in the spatially explicit forest scene.This study quantified the trade-offs involved in two main subsampling approaches when reconstructing the spatially explicit scene of a real forest for RT modeling. The two subsampling approaches were: (1) subplot subsampling - area-based, using the subplot (i.e. a fixed area) as the basic sampling and reconstruction unit; and (2) tree library subsampling - tree-based, using the individual tree as the basic sampling and reconstruction unit. We used the Discrete Anisotropic Radiative Transfer Model (DART) to simulate the Bidirectional Reflectance Factor (BRF) of the completely reconstructed 1-ha 3D-explicit forest scene, as well as the simplified forest scenes built from various subsets of the same forest. The simulated reflectance deviation of the simplified forest scenes was evaluated by comparing it with the fully reconstructed forest scene.The results showed that for subplot subsampling, as the sampling fraction increased from 10% to 90%, the normalized mean BRF deviation of radiative transfer simulations decreased from −2.7% to −0.0034% and its standard deviation decreased from 7.7% to 0.54%. Additionally, as the sampling fraction increased from 10% to 90%, the normalized mean BRF deviation of tree library subsampling decreased from −7.4% to −1.3% and its standard deviation decreased from 2.8% to 0.51%.Overall, sampling 20% of the forest area using the subplot subsampling method was an effective reconstruction strategy for the temperate deciduous forest. This strategy ensured a normalized mean BRF deviation of −2.9% and a standard deviation of 3.7% in the forest stand studied, with a relatively low reconstruction effort.This study shows that the forest subsampling approach allows for reconstructing 3D-explicit forest scenes for RT modeling at the forest stand scale (≥ 1-ha). Accordingly, highly realistic EO data can be modeled at the forest stand scale, which allows for more direct calibration and validation of EO products.

Evaluation of a forest radiative transfer model using an extensive boreal forest inventory database

The forest reflectance and transmittance model (FRT) is applicable over a wide swath of boreal forest landscapes mainly because its stand-specific inputs can be generated from standard forest inventory variables. We quantified the accuracy of this model over an extensive region for the first time. This was done by carrying out a simulation study over a large number (12,369) of georeferenced forest plots from operational forest management inventories conducted in Southern Finland. We compared the FRT simulated bidirectional reflectance factors (BRF) with those measured by Landsat 8 satellite Operational Land Imager (OLI). We also quantified the relative importance of several explanatory factors that affected the magnitude of the discrepancy between the measured and simulated BRFs using a linear mixed effects modelling framework. A general trend of FRT overestimating BRFs is seen across all tree species and spectral bands examined: up to ∼0.05 for the red band, and ∼0.10 for the near infrared band. The important explanatory factors associated with the overestimations included the dominant tree species, understory type of the forest plot, timber volume (acts as a proxy for stand maturity), vegetation heterogeneity and time of the year. Our analysis suggests that approximately 20% of the error is caused by the non-representative spectra of canopy foliage and understory. Our results demonstrate the importance of collecting representative spectra from a diverse set of forest stands, and over the full range of seasons.

Impacts of clumping effect on canopy reflectance using 3D radiative transfer modeling

The product of leaf area index (LAI) and clumping index (CI) quantifies the effective leaf abundance and distribution across the landscape, and therefore, governs the radiation absorption, evapotranspiration, and carbon assimilation processes in the terrestrial ecosystems. Previous studies were mainly focused on developing inversion methods applicable to large scale for retrieving LAI and CI from multi-angular satellite observations. However, a few studies focused on quantifying the sensitivity of canopy bidirectional reflectance distribution function (BRDF) to changes in CI in a forward manner, hampering an accurate understanding of the relationship between CI and BRDF. In this study, we simulated how BRDF responds to changes in CI in Qinghai spruce (Picea crassifolia) forests based on a 3D radiative transfer model LESS and ground-measured data. We found that the LESS model effectively simulated the hot-spot, roof, and bowl-edge characteristics of the canopy BRDF by changing the sun-sensor geometry. We constructed forest scenes with variable CI (ranging from 0.4 to 0.8) to investigate the clumping effect on BRDF with different solar and observation angles. The red band bidirectional reflectance factor (BRF) showed higher sensitivity to changes in CI than that in the near-infrared (NIR) band. Canopy BRFs in the red band along the principal plane and cross principal planes measured in different seasons showed consistent sensitivity to changes in CI, suggesting that the red band BRF is helpful for CI inversion for forests with different levels of foliage clumping. In the NIR band, canopy BRFs along the principal plane measured in growing seasons [with solar zenith angle (SZA) &amp;lt;40°] and the cross principal plane measured in non-growing seasons (with SZA &amp;gt;40°) were sensitive to changes in CI in highly clumped forests (with CI ranging from 0.4 to 0.6). However, canopy BRF in the NIR band showed low sensitivity to changes in CI in highly clumped forests (CI &amp;lt;0.6), especially along the cross principal plane when SZA was approximately 10°. The simulated BRFs in the red and NIR bands showed relatively low sensitivity to changes in SZAs at a VZA of 40° and 0°, respectively. We highly recommend including the red band BRF for CI retrievals, and using a VZA of 40° in the red band and 0° in the NIR band may help reduce the CI estimation uncertainty caused by changes in SZA. This research provides a new perspective for understanding the sensitivity of multi-angular satellite data to changes in canopy structural characteristics of vegetation in global ecosystem studies and may help improve CI estimations using the multiangular optical remote sensing data.

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.

Generality of leaf spectroscopic models for predicting key foliar functional traits across continents: A comparison between physically- and empirically-based approaches

Leaf spectroscopy provides an efficient way of predicting foliar functional traits, commonly using physically- and empirically-based models. However, the generality of both models has not been fully investigated, and it is not clear if inversion strategies of physically-based models can be transferred across datasets. In this study, we evaluated the generality of leaf spectroscopic models for predicting key foliar functional traits and compared the performance of physically- and empirically-based approaches. Two extensive datasets compiling a total of 3861 foliar samples were collected from 24 field sites in eastern United States and south China. The leaf radiative transfer model PROSPECT was coupled with COSINE (PROCOSINE) to retrieve foliar traits from leaf bidirectional reflectance factor (BRF). A commonly used empirically-based model, partial least squares regression (PLSR) was performed as a comparison. Results showed that both PROSPECT and PROCOSINE can accurately estimate leaf mass per area (LMA) and equivalent water thickness (EWT). Inversion strategies including the selection of optimal spectral domains and the use of prior information (IS3) greatly improved the estimation accuracy of leaf nitrogen, leaf chlorophyll a + b and carotenoids. The estimation accuracies were similar when transferring inversion strategies across datasets, indicating a high level of transferability of physically-based models. PLSR and interval PLSR (iPLSR, via feature selection) could predict foliar traits with high accuracies when cross-validation was performed, and iPLSR achieved higher accuracies. But both the empirical approaches demonstrated low transferability when applied to an independent dataset. Our findings highlight the importance of generalized traits models with respect to development and calibration of leaf radiative transfer model, as well as incorporating representative samples in training empirical models. This study can help us to better understand the variation of foliar traits among and within species, their response to environmental change, as well as plant biodiversity.

A GeoNEX-based high-spatiotemporal-resolution product of land surface downward shortwave radiation and photosynthetically active radiation

Abstract. Surface downward shortwave radiation (DSR) and photosynthetically active radiation (PAR) play critical roles in the Earth's surface processes. As the main inputs of various ecological, hydrological, carbon, and solar photovoltaic models, increasing requirements for high-spatiotemporal-resolution DSR and PAR estimation with high accuracy have been observed in recent years. However, few existing products satisfy all of these requirements. This study employed a well-established physical-based lookup table (LUT) approach to the GeoNEX gridded top-of-atmosphere bidirectional reflectance factor data acquired by the Advanced Himawari Imager (AHI) and Advanced Baseline Imager (ABI) sensors. It produced a data product of DSR and PAR over both AHI and ABI coverage at an hourly temporal step with a 1 km spatial resolution. GeoNEX DSR data were validated over 63 stations, and GeoNEX PAR data were validated over 27 stations. The validation showed that the new GeoNEX DSR and PAR products have accuracy higher than other existing products, with root mean square error (RMSE) of hourly GeoNEX DSR achieving 74.3 W m−2 (18.0 %), daily DSR estimation achieving 18.0 W m−2 (9.2 %), hourly GeoNEX PAR achieving 34.9 W m−2 (19.6 %), and daily PAR achieving 9.5 W m−2 (10.5 %). The study also demonstrated the application of the high-spatiotemporal-resolution GeoNEX DSR product in investigating the spatial heterogeneity and temporal variability of surface solar radiation. The data product can be freely accessed through the NASA Advanced Supercomputing Division GeoNEX data portal: https://data.nas.nasa.gov/geonex/geonexdata/GOES16/GEONEX-L2/DSR-PAR/ (last access: 12 March 2023) and https://data.nas.nasa.gov/geonex/geonexdata/HIMAWARI8/GEONEX-L2/DSR-PAR/ (last access: 12 March 2023) (https://doi.org/10.5281/zenodo.7023863; Wang and Li, 2022).

Estimating leaf nitrogen and chlorophyll content in wheat by correcting canopy structure effect through multi-angular remote sensing

Canopy scattering coefficient (CSC) is the ratio of bidirectional reflectance factor (BRF) to directional area scattering coefficient (DASF), and has been successfully applied to correct the effect of canopy structure. The key to calculate CSC is to calculate DASF, which is determined by the intercept b and slope k of the linear relationship between BRF and BRFλΩωλ (the ratio of hyperspectral BRF in certain view direction to leaf albedo (ωλ). However, due to the limitation of multispectral bands, how to accurately calculate crop DASF during the whole growth period using multispectral UAV data is still an urgent problem to be solved. In this study, wheat canopy multi-angular (0°, −30°, −45°) datasets including near-ground hyperspectral data, UAV multispectral data, and PROSAIL simulation data were obtained for three consecutive years. The DASFk-b model was proposed to estimate b and k based on multispectral sensors by relative accumulated growing degree days (RAGDD) and BRF. The previously developed DASFg-NIR model and vegetation index (VI) model were compared with DASFk-b model under different view angles (VZAs), to evaluate their performances in correcting canopy structural effect, estimating leaf nitrogen content (LNC) and leaf chlorophyll content (LCC) based on generalized additive model (GAM). The results showed that the hyperspectral band range suitable for estimating wheat DASF was 710–760 nm. Parameter b decreased with increased N application rates, and activated first and then inhibited with increased RAGDD; the tendency of k, DASFHy were opposite. Compared with CSCg-NIR (calculated from DASFg-NIR model) and VIs, CSCk-b (calculated from DASFk-b model) had the best correction effect on canopy structure under different VZAs. The estimation accuracy of LNC and LCC using CSCk-b was improved compared with CSCg-NIR and VIs, with RRMSE values of 9.2% and 7.0%, respectively, and the recommended VZA was −45° for both models.

Multiscale Analysis of Reflected Radiation on Lunar Surface Region Based on MRRT Model

The moon has stable luminosity. Radiometric calibration on the lunar region is a good step in the right direction with the expansion of instrument observation capabilities. The uneven composition and terrain types of the lunar surface make it possible for inaccuracies in albedo calculation from coarse-scale data if the within-pixel topology is overlooked. The expression between the region’s bidirectional reflectance factor (BRF) and the actual microtopography reflectance was established by the multiple reflections of radiation between terrains (MRRT) model. This research studied the radiation properties on the lunar surface region at various spatial resolutions (scales) based on the MRRT model. To determine the ideal scale of microtopography to be built, the scale-effect evaluation factor of albedo is established, and the scale-effect function is fitted. Experiments demonstrate that a microtopography with a spatial resolution of 60 m to 120 m, with 80 m being the most suitable scale, can be constructed for an area having (6000 × 6000) m2. This research adds to the MRRT model’s applicability analysis in multiscale DEM modeling, helps choose and build a radiation calibration field on the lunar surface, and lays the groundwork for employing the area of the lunar surface for radiation calibration.

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.

Enhanced Branch Simulation to Improve RAPID in Optical Region Using RAMI Scenes

To improve the simulation accuracy of vegetation canopy reflectance in optical bands, the Radiosity Applicable to Porous IndiviDual objects (RAPID) model has been upgraded to better deal with branches in the latest RAPID4. Previous versions of RAPID (RAPID1 and RAPID3) neglected branches in porous objects in optical bands, while RAPID2 emphasized them in microwave bands. This inconsistency needed to be addressed to establish a unified radiosity-based simulation framework. By incorporating branches in RAPID4, we have improved several aspects of the model, including the random dynamic projection process, the equivalent reflectance or transmittance, the single scattering estimation, the multiple scattering solution, and the bidirectional reflectance factor (BRF) calculation. Three-dimensional trees from the fifth RAdiation transfer Model Intercomparison (RAMI-V) have been used to test the contribution of branches on BRF. Comparisons with a ray-tracing-based LESS model (the LargE-Scale remote sensing data and image Simulation framework) on RAMI-V scenes show a general agreement on BRF ( R 2 ≥ 0.96 and root mean square error ranging from 0.014 to 0.054). The major biases occur in a realistic scene (i.e., HET51_WWO_TLS) created from terrestrial laser scanning data. Sensitivity analysis has been conducted to show the branch contribution on BRF in optical domain. Without considering dense branches, the BRF error can exceed 0.1.

A New Method to Estimate the Leaf Chlorophyll Content From Multiangular Measurements: Anisotropy Index

Anisotropy index (ANIX), which is defined as the ratio between the maximum and minimum reflectance factors in the principal plane, has been applied in characterizing the optical properties of vegetation. But it is seldom used to estimate the chlorophyll content in leaf level. In this study, we found the leaf spectral anisotropy index (ANIX) depended on the variation of leaf chlorophyll content (LCC). Newly proposed indices, (ASRI: A750/A720 and ANDI: (A750-A720)/(A750+A720), A is the modified ANIX (mANIX=ANIX-1)), were derived from the format of the existing reflectance-based vegetation indices: simple ratio (SR) and normalized difference (ND) indices. The diffuse reflection, closely related to LCC, can be extracted from the two new indices (or directly obtained as the minimum bidirectional reflectance factor). They have strong linear relationships with LCC (R2=0.88 and 0.89), and have good estimation accuracy using an independent dataset (RMSE=6.39 μg/cm2 and 6.10 μg/cm2). We also found the ASRI (ANDI) had similar LCC estimation accuracy as the range-ASRIs or range-ANDIs (RANDIs or RANDIs), which were calculated by the random combination of reflectance factors in the forward scattering directions (ranged from -30° to -50°) and near the backward scattering directions (ranged from 0° to 20°). The wide and effective ranges of viewing angles strengthen the usability of ASRI and ANDI to estimate LCC in the practical measurements.

Improving the estimation of canopy structure using spectral invariants: Theoretical basis and validation

Directional area scattering factor (DASF) is a critical canopy structural parameter for vegetation monitoring as it provides an efficient tool for detection of vegetation functional traits and their dynamics. Current standard approach to estimate DASF from canopy bidirectional reflectance factor (BRF) is based on the assumption that in the weakly absorbing 710 to 790 nm spectral interval, leaf scattering does not change much with the concentration of dry matter and thus its variation can be neglected. This results in biased estimates of DASF and consequently leads to uncertainty in DASF-related applications. This study proposes a new approach to account for variations in concentrations of this biochemical constituent, which additionally uses the canopy BRF at 2260 nm. Analysis of the proposed approach based on radiative transfer models suggests significant decrease in relative root mean square error (rRMSE) by nearly a half for one- and three dimensional dense vegetated scenes, and the improvement is insensitive to the reflectivity of background. Robust refinement is also achieved in a larger actual forest scene. When the new approach is adopted to indoor multi-angular hyperspectral measurements on individual tree crowns, the relative error has also reduced, with more improvement seen in needle-leaf than in broadleaf species. Thus, the proposed DASF estimation approach outperforms the current one and can be used more reliably in DASF-related applications, such as retrieval of vegetation biochemical contents, grasping periodic changes of equatorial forests, and other ecological and environmental vegetation monitoring tasks based on spectral invariants theory.

Remote estimation of leaf nitrogen concentration in winter oilseed rape across growth stages and seasons by correcting for the canopy structural effect

Estimating crop leaf nitrogen concentration (LNC, %) with the canopy bidirectional reflectance factor (BRF) is an effective method for detecting the nitrogen (N) deficiency in crops. It is challenging to remotely estimate LNC across growth stages and seasons with a general empirical model, since the complex change in canopy structure under N deficiencies and across growth stages affects the accuracy of the estimations. The canopy scattering coefficient (CSC), the ratio of the BRF to the directional area scattering factor (DASF), has been suggested to reduce the canopy structural effect on BRF. However, the DASF can only be calculated for closed canopies and is not applicable to the early growth stages of crops when the fields are sparsely vegetated. This study proposed a new method for decoupling the canopy structural effect and canopy BRF using the near-infrared reflectance of vegetation (NIRV). NIRV is driven by the change in canopy structure while mitigates the soil contribution. The method was tested through six field experiments on ten farmers' fields of winter oilseed rape (Brassica napus L.) using both in-situ hyperspectral data and unmanned aerial vehicle (UAV) multispectral images. The results demonstrated that NIRV was closely related to the leaf area index (LAI) (R2 = 0.79) across growth stages and seasons. The CSC was derived with NIRV based on the linear relationship between NIRV and DASF for the closed canopies. The LNC predicted by the NIRV-derived CSC (R2 = 0.69, RMSE = 0.51 for in-situ hyperspectral data and R2 = 0.65, RMSE = 0.49 for UAV multispectral images) was more accurate than the results derived from the BRF (R2 = 0.55, RMSE = 0.62 for in-situ hyperspectral data and R2 = 0.59, RMSE = 0.60 for UAV multispectral images) with the independent dataset, suggesting that correcting for the canopy structural effect with NIRV provided a new alternative for suppressing the impact of canopy structure on the canopy BRF. As NIRV is easily calculated with diverse remote sensing data sources, this study proved the potential of applying NIRV to improving the accuracy and transferability of the LNC prediction model at different scales.

Multi-Irradiance: A Method for Simultaneous Measurement of the Temperature and Spectral Emissivity of High-Temperature Targets in SWIR.

The currently used energy methods in spectral emissivity measurement are susceptible to the difference in temperature between the target and the reference blackbody. It is also limited by the state of the observation target and observation. This paper introduces the irradiance condition, while using the correlation between the information of emission energy and reflected energy of the high-temperature target. Based on the principle of radiative transmission and energy conservation, the relationship between the emissivity and bidirectional reflectance factor (BRF) was used to perform the retrieval of emissivity and temperature. An experimental device was designed, and graphite and rock were considered to verify the feasibility of the experimental scheme. The error of emissivity and temperature of both targets were, respectively, less than 5% and 0.5%, due to the Lambertian assumption, and the systematic errors had negligible impact on the retrieval. This verifies that the experimental observation method and scheme is reasonable.