Although remote sensing (RS) of solar-induced chlorophyll fluorescence (SIF) is increasingly used as a valuable source of information about vegetation photosynthetic activity, the RS SIF observations ...are significantly influenced by canopy-specific structural features (i.e., canopy architecture including leaf area index and presence of woody components), atmospheric conditions during their acquisition (e.g., proportion of direct and diffuse irradiance) and observational geometric configurations (e.g., sun and viewing directions). Radiative transfer (RT) models have the potential to provide a better understanding of the canopy structural effects on the SIF emission and RS signals. Here, we used the DART model to assess the daily influence, from morning to evening, of forest 3D architecture on SIF nadir radiance, emission, escape factor and nadir yield of eight 100 m × 100 m forest study plots established in a temperate deciduous forest of the Smithsonian Environmental Research Center (Edgewater, MD, USA). The 3D architecture of each plot was derived from airborne LiDAR. DART simulations of these 3D forest plots and their 1D (i.e., vertical profile of sun-adapted and shade-adapted leaves) and 0D (i.e., homogeneous layer of sun-adapted leaves above an homogeneous layer of shade-adapted leaves) abstractions were compared to assess the relative errors (ε1D−3D and ε0D−3D) associated with horizontal and vertical structural heterogeneity, respectively. Forest 3D structure, especially horizontal heterogeneity, had a great influence on forest nadir SIF radiance, resulting in ε1D−3D up to 55% at 8:00 and 18:00 (i.e., for oblique sun directions). The key indicators of this impact, in the descending order of importance, were the SIF escape factor (ε1D−3D up to 40%), the attenuation of incident photosynthetically active radiation (ε1D−3D less than 5%), and the SIF emission yield (ε1D−3D less than 2%). The influence of forest architecture on the nadir SIF escape factor and SIF yield (ε1D−3D up to 40%) varied over time, with differences in forest stand structure, and per spectral domain, being always larger between 640 and 700 nm than between 700 and 850 nm. In addition, woody elements demonstrated a large influence on forest SIF radiance due to their “shading” effect (ε up to 17%) and their “blocking” effect (ε ≈ 10%), both of them higher for far-red than for red SIF. These results underline the importance of 3D forest canopy architecture, especially 2D heterogeneity, and inclusion of woody elements in RT modeling used for interpretation of the RS SIF signal, and subsequently for the estimation of gross primary production and detection of vegetation stress.
•Solar-induced fluorescence (SIF) is simulated for LiDAR reconstructed forest sites•Modeled 3D forest heterogeneity and wood had a strong impact on nadir SIF radiance•Horizontal heterogeneity has a greater impact on SIF radiance than the vertical one•Horizontal heterogeneity affected more canopy SIF propagation than PAR absorption•Wood shadowing affects nadir SIF radiance more than its SIF blocking (obstruction)
Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor
is addressed. Two leaf ...area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacás area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs' saturation in the Apiacás area (i.e.,
= -0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g.,
= -0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI.
Recent studies have demonstrated the potential of using bidirectional reflectance distribution function (BRDF) signatures captured by multi-angle observation data to enhance land cover classification ...and retrieve vegetation architectures. Considering the diversity of crop architectures, we proposed that crop mapping precision may be enhanced by using BRDF signatures. We compared the accuracy of four supervised machine learning classifiers provided by the Google Earth Engine (GEE), namely random forest (RF), classification and regression trees (CART), support vector machine (SVM), and Naïve Bayes (NB), using the moderate resolution imaging spectroradiometer (MODIS) nadir BRDF-adjusted reflectance data (MCD43A4 V6) and BRDF and albedo model parameter data (MCD43A1 V6) as input. Our results indicated that using BRDF signatures leads to a moderate improvement in classification results in most cases, compared to using reflectance data from a single nadir observation direction. Specifically, the overall validation accuracy increased by up to 4.9%, and the validation kappa coefficients increased by up to 0.092. Furthermore, the classifiers were ranked in order of accuracy, from highest to lowest: RF, CART, SVM, and NB. Our study contributes to the development of crop mapping and the application of multi-angle observation satellites.
Urban geometry and materials combine to create complex spatial, temporal and directional patterns of longwave infrared (LWIR) radiation. Effective anisotropy (or directional variability) of thermal ...radiance causes remote sensing (RS) derived urban surface temperatures to vary with RS view angles. Here a new and novel method to resolve effective thermal anisotropy processes from LWIR camera observations is demonstrated at the Comprehensive Outdoor Scale MOdel (COSMO) test site. Pixel-level differences of brightness temperatures reach 18.4 K within one hour of a 24-h study period. To understand this variability, the orientation and shadowing of surfaces is explored using the Discrete Anisotropic Radiative Transfer (DART) model and Blender three-dimensional (3D) rendering software. Observed pixels and the entire canopy surface are classified in terms of surface orientation and illumination. To assess the variability of exitant longwave radiation (MLW) from the 3D COSMO surface (MLW3D), the observations are prescribed based on class. The parameterisation is tested by simulating thermal images using a camera view model to determine camera perspectives of MLW3D fluxes. The mean brightness temperature differences per image (simulated and observed) are within 0.65 K throughout a 24-h period. Pixel-level comparisons are possible with the high spatial resolution of MLW3D and DART camera view simulations. At this spatial scale (<0.10 m), shadow hysteresis, surface sky view factor and building edge effects are not completely resolved by MLW3D. By simulating apparent brightness temperatures from multiple view directions, effective thermal anisotropy of MLW3D is shown to be up to 6.18 K. The developed methods can be extended to resolve some of the identified sources of sub-facet variability in realistic urban settings. The extension of DART to the interpretation of ground-based RS is shown to be promising.
•Diurnal longwave infrared radiation observations of the COSMO urban canopy•Method for per-pixel dynamic classification of observations•Brightness temperature for all surfaces at high spatial and temporal resolution•Explanation of observed variability based on surface orientation and shading•Brightness temperatures used to model urban thermal anisotropy
Current thermal infrared satellite images are full of mixed pixels. This work is a quantitative analysis, based on radiative transfer modelling, of the distribution of mixed pixels and their impact ...on the use of TES. TES was applied to radiance images of the cities of Basel and Heraklion simulated at different spatial resolutions by the DART radiative transfer model with three-dimensional (3D) representations of these cities. The accuracy of the TES was assessed by comparing the retrieved land surface temperature (LST) and surface emissivity (LSE) to the input temperature and emissivity of DART. The spatial resolution of 30 m appeared to be a crucial threshold for the presence of pure pixels in these cities. When the spatial resolution reaches 30 m, the percentage of mixed pixels shows significant growth. We evaluated the performance of the TES algorithm on pure and mixed pixels. For homogeneous, isothermal, flat and shadow-less pure pixels, the variation of TES accuracy with the resolution is not obvious. For mixed pixels or pure pixels with a high non-planar structure, the accuracy of TES even decreases with the increase of resolution. The reason may be that higher spatial resolution enhances spatial heterogeneity (due to shadow and pixel non-planarity). A physically acceptable average temperature and average emissivity can be obtained even if TES is applied to mixed pixels. Our study stresses the need to consider the spatial resolution variation effect when applying the TES method to urban areas.
Satellite and airborne optical sensors are increasingly used by scientists, and policy makers, and managers for studying and managing forests, agriculture crops, and urban areas. Their data acquired ...with given instrumental specifications (spectral resolution, viewing direction, sensor field-of-view, etc.) and for a specific experimental configuration (surface and atmosphere conditions, sun direction, etc.) are commonly translated into qualitative and quantitative Earth surface parameters. However, atmosphere properties and Earth surface 3D architecture often confound their interpretation. Radiative transfer models capable of simulating the Earth and atmosphere complexity are, therefore, ideal tools for linking remotely sensed data to the surface parameters. Still, many existing models are oversimplifying the Earth-atmosphere system interactions and their parameterization of sensor specifications is often neglected or poorly considered. The Discrete Anisotropic Radiative Transfer (DART) model is one of the most comprehensive physically based 3D models simulating the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It has been developed since 1992. It models optical signals at the entrance of imaging radiometers and laser scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental configuration and instrumental specification. It is freely distributed for research and teaching activities. This paper presents DART physical bases and its latest functionality for simulating imaging spectroscopy of natural and urban landscapes with atmosphere, including the perspective projection of airborne acquisitions and LIght Detection And Ranging (LIDAR) waveform and photon counting signals.
In radiative transfer modeling, the angular variable Ω discretization can strongly influence the radiative transfer simulation, especially with small numbers of discrete directions. Most radiative ...transfer models use discrete ordinate method or finite volume method for solving the transport equation. Both of the methods have their own algorithms to discretize the 4π space, under the constraint of satisfying geometric symmetry and specific moments. This paper introduces a new direction discretization and oversampling scheme, IUSD, and compares it with the other methods in simulating satellite signals. This method considers the constraint of geometric shape of angular sector, and iteratively discretizes the 4π space under this constraint. The result shows that IUSD is quite competitive in the accuracy of simulating remote sensing images. Furthermore, the new method provides a flexibility for adding any oversampling angular region, with any number of additional directions, using an optimal approach in terms of the total number of directions. Several case studies are presented. It turns out that the regional oversampling has significant influence for strong anisotropic scattering. This method has been implemented in the latest code of DART 3D radiative transfer model. DART is available for scientific purpose upon request.
•We develop a new direction discretization method for radiative transfer.•Results show that the new method is more accurate than the traditional DOM and FVM.•2 oversampling methods are presented for strongly anisotropic radiation region.•Advantage of this approach is improving accuracy without adding many directions.
Increased urbanization and climate change have resulted in the intensification of the urban heat island (UHI) effect, particularly in tropical cities. One of the main causes of UHI is the man-made ...urban surfaces influencing the radiation budget by absorbing, reflecting, and emitting radiation at various wavelengths. The radiative budget of a city is directly influenced by the urban geometry, surface materials, direct solar radiation and incident angle, and atmospheric diffuse radiation. Vegetation cover, in contrast, can decrease UHI by intercepting radiation and through the process of photosynthesis. Better understanding the effect of urban vegetation on the radiative budget can thus contribute towards the mitigation of the UHI effect and ultimately the development of climate resilient urban spaces. To analyze the contribution of vegetation to the radiative budget of a city, a detailed simulation of the complex interaction between the built environment and the vegetation is required. This study proposes an approach for analyzing the 3-D structure of both vegetation and built environment to quantify the contribution of vegetation to the radiative budget of an urban landscape. In a first step, a detailed 3-D model of Singapore including buildings and vegetation was reconstructed using a combination of free and commercial Earth Observation data. Then, the 3-D Discrete Anisotropic Radiative Transfer (DART) model was repurposed to estimate the radiation absorbed by the urban surfaces accounting for the presence of vegetation cover with changing Leaf Area Density (LAD) conditions. The presence of trees in the scene accounted for a significant reduction of the absorbed radiation by buildings and ground. For example, in the case of a residential low-building neighborhood, although having low tree cover, the reduction of the absorbed radiation by buildings and ground was up to 15.5% for a LAD =1. The field validation shows good agreement (R2 = 0.9633, RMSE = 10.8830 and Bias = −1.3826) between the DART-simulated shortwave exitance and upwelling shortwave measurements obtained from a net radiometer mounted on a local flux tower in the urban area of Singapore, over the studied period. Our approach can be used for neighborhood-scale analysis, at any desired location of a city, to allow test scenarios with varying surface materials and vegetation properties.
•New method for canopy segmentation of urban trees at high spatial resolution.•Investigated tree Cab retrieval for complex structure, background and illumination.•Proposed new narrow band index, ...UTCI, for estimating Cab of urban trees.•Proposed UTCI index outperforms existing NBIs for simulated and field Cab data.
Urban trees provide important ecosystem services to improve cities’ liveability and sustainability. Leaf chlorophyll content (Cab) estimation by remote sensing can help monitor tree health efficiently. However, the Cab retrieval of urban trees is challenging because of the complex canopy structures, backgrounds, and illuminations conditions. This paper proposed an automatic method for partitioning sunlit/shaded pixels and removal of bright-specular/dark-hole and background pixels. In addition, we proposed a new index, the Urban Tree Chlorophyll Index (UTCI), defined as UTCI=(ρ709-ρ697)/(ρ709-ρ686), based on the simulated hyperspectral images of urban tree using radiative transfer model. This proposed UTCI index outperforms existing narrow-band indices (NBIs) for estimating Cab for complex canopy structures, backgrounds, and illumination conditions evaluated using simulated hyperspectral images. The advantage of the UTCI was also demonstrated when applied to UAV hyperspectral images validated with direct field foliar measurements of Cab. It surpasses existing NBIs, demonstrating a moderate correlation (R2 = 0.34) with Cab under varying irradiance and a strong correlation (R2 = 0.62) with Cab under stable diffuse illumination. This study, for the first time, extensively investigated NBIs for Cab estimation of urban trees from UAV hyperspectral images, providing a theoretical and operational basis for future monitoring of Cab in the management of urban trees. This new method can be potentially applied to other vegetation types with complex canopy structures, backgrounds, and illuminations conditions.
The microclimatic conditions of the urban environment influence significantly the thermal comfort of human beings. One of the main human biometeorology parameters of thermal comfort is the Mean ...Radiant Temperature (Tmrt), which quantifies effective radiative flux reaching a human body. Simulation tools have proven useful to analyze the radiative behavior of an urban space and its impact on the inhabitants. We present a new method to produce detailed modeling of Tmrt spatial distribution using the 3-D Discrete Anisotropic Radiation Transfer model (DART). Our approach is capable to simulate Tmrt at different scales and under a range of parameters including the urban pattern, surface material of ground, walls, roofs, and properties of the vegetation (coverage, shape, spectral signature, Leaf Area Index and Leaf Area Density). The main advantages of our method are found in (1) the fine treatment of radiation in both short-wave and long-wave domains, (2) detailed specification of optical properties of urban surface materials and of vegetation, (3) precise representation of the vegetation component, and (4) capability to assimilate 3-D inputs derived from multisource remote sensing data. We illustrate and provide a first evaluation of the method in Singapore, a tropical city experiencing strong Urban Heat Island effect (UHI) and seeking to enhance the outdoor thermal comfort. The comparison between DART modelled and field estimated Tmrt shows good agreement in our study site under clear-sky condition over a time period from 10:00 to 19:00 (R2 = 0.9697, RMSE = 3.3249). The use of a 3-D radiative transfer model shows promising capability to study urban microclimate and outdoor thermal comfort with increasing landscape details, and to build linkage to remote sensing data. Our methodology has the potential to contribute towards optimizing climate-sensitive urban design when combined with the appropriate tools.
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