The demand for accurate airborne LiDAR point cloud classification has increased with improved resolutions of land cover map products. Although existing deep learning-based methods are capable of classifying airborne LiDAR point clouds, these methods indeed have a limited capability to extract the local features and suffer from global and local information losses with the commonly used pooling approaches. Therefore, we present a deep learning-based optimal homogeneous neighbor selection (HNS) and hierarchical pooling by exploiting maximum entropy, called MEHPool. The module is designed to directly extract sufficient homogeneous neighbor points for each point, followed by a designed graph pooling (GP) layer that encapsulates the selected homogeneous neighbor points into small-size graphs to build hierarchical features. The plug-and-play module consisting of an HNS module, two GP layers, and three graph neural networks (GNNs) can be easily embedded into various networks for point cloud classification and produces the architecture MEHPool-Net in this letter. Our experimental results show that the proposed MEHPool-Net realizes effective performance for multispectral airborne LiDAR point cloud classification, consistently outperforms four other deep learning methods, and confirms the superiority of the GP module compared with five other pooling methods.

As a powerful tool for simulating bi-directional reflectance and radiative transfer (RT) in complex canopies, the radiosity graphics model (RGM) suffers from a reduced runtime speed or even crashes when facing considerable computation load of the view factor for fine-grained simulation of heterogeneous canopy. In this work, the RGM model at pixel scale (RGMPS model) is proposed with the open accelerator (OpenACC) acceleration techniques and two improved algorithms, which solve the overloaded view factor calculation and enhance scene availability without sacrificing accuracy. Two heterogeneous canopy scenario experiments were used for validation, including a realistic single-tree experiment and a large-scale synthetic heterogeneous canopy experiment. The RGMPS model has increased by nearly 70 times the speed of the original RGM model, demonstrating its capability to model large-scale scenes spanning ten thousand square meters. The${R} ^{2}$between RGM and RGMPS is over 0.94 and the root-mean-square error (RMSE) is below 0.0038. The cross-model validation between the RGMPS model and the discrete anisotropic RT (DART) model achieved a high agreement with${R} ^{2}$as high as 0.98 in the near-infrared (NIR) band. An assessment conducted using airborne multiangle measurements also demonstrated that the accuracy of the proposed solution was deemed satisfactory for bi-directional reflectance factor (BRF) simulation, with RMSEs of 0.0031 and 0.0340 for the red and NIR bands, respectively. Our research contributes to the development of more efficient and accurate BRF simulations for large heterogeneous canopy scenes using the RGM model.

Commercial stereo imagery has provided unprecedented planar detail (< 2 m) of the Earth’s surface. However, a number compounding factors, including view angle, convergence angle, imaging detector design, ground sampling distance (GSD), time of day/year etc., induce bias in surface reconstructions. BlackSky 1 m GSD data and its unique frame detector/telescope configuration provides a novel resource with sensor capabilities of staring, and motion imagery/video to benchmark existing commercial digital elevation model (DEM) products. The BlackSky constellation with its high repeat collection capability also allows for dense imagery collection and enables multi-view stereo reconstruction. We evaluated the ability of BlackSky data for vegetation surface reconstruction using a multi-view stereo methodology and compared products to airborne LiDAR data. We found an Absolute Median Error of 1.77 m and normalized mean absolute deviation (NMAD) of 1.54 m in areas with sufficient volume of available digital surface models (DSMs). Our results indicate the potential benefit of this workflow and resulting products for vegetation analyses.

We integrate the time series simulation capability of the maize model within an extended L-system (ELSYS) using the growth equations from a 4-D maize and a leaf breakpoint model. These models simulate maize growth from emergence to male anthesis, accounting for 3-D architecture during the vegetative season. We employ two methods to achieve time series simulation in ELSYS: directly use the growth equations in the 4-D maize and leaf breakpoint models, and name ELSYS coupling 4-D maize (ELSYS$_{\mathrm {4Dmaize}}$). Alternatively, replace the stem radius-leaf order function with a stem radius-height function and employ linear interpolation to transform the leaf width-length ratio from a constant value to a function that varies with leaf order, thereby simulating a 4-D maize structure, and name the dynamic L-system-based architectural maize (DLAmaize) model. The DLAmaize model is applied to maize canopy reflectance simulations using the discrete anisotropic radiative transfer (DART) model and radiosity-graphics combined method (RGM), along with a comparison with the 1-D scattering by arbitrarily inclined leaves (SAIL) model. The simulated reflectance of maize canopy from ELSYS4Dmaize and DLAmaize differs significantly in the hotspot direction (the absolute value of the relative difference can be up to 67.4%). In addition, comparisons among RT models show that the DART model and RGM simulate close reflectances. The SAIL model yields significant differences (e.g., the absolute value of the relative difference can be up to 41.16% in the nadir direction) owing to its assumption of the homogeneous canopy. DLAmaize enhances remote sensing of dynamic 4-D vegetation canopies modeling and holds promise for remote sensing applications.

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 ($R^{2}$= 0.24 and root mean square error (RMSE) = 4.32 K), and they become much smaller ($R^{2}$= 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.

Physical models simulating the radiative budget (RB) and remote sensing (RS) observation of three-dimensional (3D) landscapes are critical to better understand human and natural components of the Earth system and further develop RS technology. DART is one of the most comprehensive 3D models of Earth-atmosphere optical radiative transfer (RT), from ultraviolet (UV) to thermal infrared (TIR). It simulates the optical signal of proximal, aerial and satellite imaging spectrometers and laser scanners, the 3D RB and solar induced chlorophyll fluorescence (SIF) signal, for any urban or natural landscape and any experimental or instrument configuration. It is freely available for research and teaching activities (https://dart.omp.eu). Here, five recent advances are presented. 1) Atmosphere RT. 2) RT in non repetitive topography. 3) Monte Carlo modelling for fast RS image simulation of large landscapes. 4) SIF modelling for vegetation simulated as facets and turbid cells. 5) RS image inversion for mapping the optical properties of urban material and the urban radiative budget.

The potential of solar-induced chlorophyll fluorescence (SIF) to monitor photosynthesis and plant stress has attracted considerable interest in SIF remote sensing (RS). However, canopy SIF and RS observations are impacted by topography, vegetation three dimension (3D) structure, leaf orientation, non foliar elements (e.g., tree woody skeleton), ... Physically based downscaling of canopy SIF RS data to leaf-level (i.e., to leaf photosynthesis) requires 3D radiative transfer (RT) models simulating canopy SIF and its observation. These models are necessary to better exploit the potential of SIF, by linking leaf SIF and SIF in RS observations as a function of canopy 3D architecture and experimental configurations (sun and viewing directions, etc.). The Discrete Anisotropic Radiative Transfer (DART) model is a comprehensive 3D radiative transfer (RT) model for urban and natural landscapes. This paper presents its SIF modeling for vegetation simulated with facets, its validation with the SCOPE/mSCOPE 1D models, and its recent extension to SIF modelling for landscapes simulated with 3D turbid medium.

Topographic effects on canopy reflectance play a pivotal role in the retrieval of surface biophysical variables over rugged terrain. In this paper, we proposed a new canopy anisotropic reflectance model for discrete forests, Geometric Optical and Mutual Shadowing and Scattering-from-Arbitrarily-Inclined-Leaves model coupled with Topography (GOSAILT), which considers the effects of slope, aspect, geotropic nature of tree growth, multiple scattering, and diffuse skylight. GOSAILT-simulated areal proportions of four scene components (i.e., sunlit crown, shaded crown, sunlit background, and shaded background) were evaluated using the Geometric Optical model for Sloping Terrains (GOST) model. The canopy reflectances simulated by GOSAILT were validated against two reflectance data sets: Discrete anisotropic radiative transfer (DART) simulations and wide-angle infrared dual-model line/area array scanner (WIDAS) observations. Compared with a horizontal surface, the forest canopy reflectance over a steep slope (60°) is significantly distorted with absolute (relative) bias values of 0.048 (79.60%) and 0.056 (12.02%) for the red and near-infrared (NIR) bands, respectively. The GOSAILT-simulated component areal proportions show close agreements with GOST. Moreover, GOSAILT simulations have high overall accuracy (red band: coefficient of determination (R2) = 0.96; root-mean-square error (RMSE) = 0.003; and mean absolute percentage error (MAPE) = 3.91%; and NIR band: R2= 0.78, RMSE = 0.019; MAPE = 3.94%) when compared with the DART simulations. These extensive validations indicate good performances of GOSAILT in canopy reflectance simulations over sloped surfaces.

Models that simulate the radiative budget (RB) and remote sensing (RS) observation of landscapes with physical approaches and consideration of the three-dimensional (3-D) architecture of Earth surfaces are increasingly needed to better understand the life-essential cycles and processes of our planet and to further develop RS technology. DART (Discrete Anisotropic Radiative Transfer) is one of the most comprehensive physically based 3-D models of Earth-atmosphere optical radiative transfer (RT), from ultraviolet to thermal infrared. It simulates the optical 3-D RB and signal of proximal, aerial and satellite imaging spectrometers and laser scanners, for any urban and/ or natural landscapes and for any experimental and instrumental configurations. It is freely available for research and teaching activities. Here, an application is presented after a summary of its theory and recent advances: inversion of Sentinel 2 images for simulating time series of urban radiative budget `Q*sw' maps through the determination of maps of urban surface material. Results are very encouraging: satellite and in-situ Q*sware very close (RMSE ≈ 15W/m2; i.e., 2.7% mean relative difference).

Potential of solar-induced chlorophyll fluorescence (SIF) to track time variable environmental stress of vegetation explains high interest in SIF remote sensing. There is an increasing need for physical models that consider the 3D structure of Earth surfaces, in order to better understand the relationships between SIF, vegetation three-dimensional (3D) architecture, irradiance and remote sensing configuration at canopy level. The Discrete Anisotropic Radiative Transfer (DART) model is one of the most comprehensive physically based 3D models of Earth-atmosphere radiative transfer (RT), covering the spectral domain from ultraviolet to thermal infrared wavelengths. This paper presents the determination of the sun and shade adapted leaf elements of a 3D vegetation canopy in DART, which is required for accurate RT simulations of SIF in geometrically explicit 3D canopy representations.

Three dimensional (3D) radiative transfer simulation is becoming an important and essential tool to understand the interaction between solar radiation and forest canopies. However, conducting a 3D simulation usually needs a lot of input parameters, especially the 3D information of forest scene, which is difficult to obtain and reconstruct. This paper presents a voxel approach that derives forest mockups from LiDAR data. These 3D mock-ups are adapted to DART model, using its recently introduced data access objects (DAO) tool. Here, they are used to simulate multispectral images with DART.

Light Detection And Ranging (LiDAR) techniques have been extensively applied in spaceborne, airborne and ground-based platforms. Understanding LiDAR data requires modeling approaches that can precisely account for the physical interactions between the emitted laser pulse and reflecting targets. Diverse LiDAR data types arise from different systems, platforms, and applications. However, most existing physical models consider only single pulse configurations to simulate large footprint LiDAR waveforms, which do not correspond to standard data formats. Hence, in many cases, model outputs are not well adapted to research conducted with actual LiDAR systems, especially for Aerial and Terrestrial Laser Scanning (ALS and TLS) systems. The Discrete Anisotropic Radiation Transfer (DART) model provides accurate and efficient simulations of multiple LiDAR pulses from all platform types. This paper presents the latest development of the DART LiDAR module: Gaussian decomposition of the simulated ALS and TLS waveforms followed by the provision of LiDAR point cloud and waveforms in text and standard ASPRS LAS formats.

The fast development of the light detection and ranging (LiDAR) technique, especially with scanning and multi-beam systems that launch pulses along different directions, requires efficient and accurate simulation tools to analyze existing data and to design future systems. This work presents the recent advantages of the discrete anisotropic radiative transfer (DART) model in LiDAR data simulation. A more comprehensive comparison between DART-simulated waveforms and Laser Vegetation Imaging Sensor (LVIS) over Howland forest, Maine is presented. Results show that in addition to waveform shape simulation, radiometric calibration coefficients could be inferred from the comparison. This new discovery could provide an approach for possible radiometric modeling of LiDAR data, that convert the digital number of the waveform into actual energy.

We introduced an approach to simulate and separate atmospheric contribution in ground-based thermal-infrared (TIR) camera measurements. Different from the traditional approach which uses the look-up table built from 1-D radiative transfer model (RTM), this approach directly simulates 3-D ray propagations and interactions in the heterogeneous urban environment by using the Discrete Anisotropic Radiative Transfer (DART) model. The atmospheric turbid cells that occupy every part of the urban scene are created using the vertical constituent distribution and the optical property profiles in the existing databases or from the actual meteorological measurements. The two components of atmospheric effects on the TIR at-sensor radiance are attenuated transmission and path thermal emission. Taking both into account, the at-surface radiance corresponding to the signal emitted only from the urban surface can be derived.

The need of better accuracy for analyzing remote sensing (RS) data of complex Earth surfaces explains the increasing need of models that simulate RS data with physical approaches. Similarly, the study of Earth surfaces functioning requires physical models that simulate the 3D radiative budget (RB) of these surfaces. DART (Discrete Anisotropic Radiative Transfer is one of the most comprehensive physically based 3D models that model the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It simulates optical signals at the entrance of terrain/airborne/satellite imaging radiometers and laser scanners, as well as the 3D RB, of urban/natural landscapes for any experimental and instrumental configurations. Its licenses are free for research and teaching activities. Here, we present its major recent advances.

Multi-sensor systems are increasingly demanding in recent remote sensing (RS) applications. Combination of LiDAR and imaging spectrometers is an emerging technique used by several recent airborne systems. The combined data provide both functional and structural information, which makes this technique a unique tool for understanding and management of the Earth's ecosystems. The rapid development of this technique demands the simulation and validation of the combined data. In this paper, we introduce a new method to simulate data fusion of multi-sensor system which combined LiDAR and imaging spectrometer, with any experimental, instrumental, and geometrical configurations of systems. This method is implemented in the latest release of discrete anisotropic radiative transfer (DART) model.

DART model was extended for simulating satellite lidar signal of Earth-Atmosphere systems. The adopted approach combines Monte Carlo and flux tracking methods. It improves a lot signal to noise ratios of simulated waveforms. For accurate simulation of atmosphere photon tracing, the atmosphere modelling was modified for obtaining continuous vertical distribution of extinction coefficients. It leads to a much better accuracy than the use of atmosphere layers with constant extinction coefficients. This improvement is valid with any type of atmosphere, exponential or not.

DART model was extended for simulating satellite Lidar data of 3D Earth scenes with Monte Carlo based methods. 2 major modeling methods were developed. (1) Monte Carlo method for efficiently handling complex phase functions: once scattering directions with close occurrence probabilities are grouped within classes, a 1strandom pulling gives the class of scattering directions, and a 2ndrandom pulling gives the scattering direction within the class. (2) A so-called RayCarlo method combines the classical Monte Carlo forward photon tracing method and the flux tracking method, which allows one to decrease computer time of classical Monte Carlo method by factors that can reach 108. Simulation results are very encouraging. Validation tests are being conducted.

A DART based data processing chain was developed for assessing the potential of a high spatial resolution geostationary satellite. It simulates time series of TOA and BOA radiance values and their spatial variability at any satellite spatial resolution, for any experimental, instrumental and view configuration (e.g., geosynchronous orbit). Account of sources of noise (e.g., atmosphere, sensor, anisotropy of surface optical properties,...) gives the radiance SNR (i.e., domain of validity). First results with the desert and tree savannah sites are very encouraging.

Nowadays, several optical space-borne systems with high resolution, high temporal revisit frequency and constant viewing angles are preparing to be be launched: Venμs, Sentinel-2, etc. The usefulness of these data will be limited due to for instance cloud coverage over the scene. Image fusion techniques with other satellite products of even higher revisit frequency will dramatically promote the usefulness of the data. Therefore, our objective is to find the proper image fusion technique to adapt these new missions. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is one the techniques we implemented. During our research, this technique is modified to fit the parameters of our data, and the result shows an obvious improvement.

Many radiative transfer (RT) models combine exact kernel and discrete ordinate techniques for solving the transport equation. They discretize the 4π space into a finite number of angular sectors, with directions along which radiation propagates. They can be more or fewer and equally spaced or not. RT model improvement is usually focused on 3D landscapes simulation and RT mathematical modeling. The angular variable Ω discretization is a much less addressed problem, although it can strongly influence the simulation of satellite signals, especially with small numbers of discrete directions. Here, we present a new Ω discretization that improves the accuracy of simulated result.

Lusi is a mud volcano in East Java, Indonesia, which started its eruption on 29thMay 2006 and never stopped. To study this volcano, we used SAR interferometry with ALOS/Palsar satellite images from 2006 to 2010. By creation of a set of interferograms, and suppression of unwanted components, such as Earth curvature and elevation, we were able to compute the motion that occurred between two dates. The obtained set of motions shows that the region of Lusi is undergoing a general subsidence of several metres. However, as this region is very wet, more precise results, on the volcano itself, can be obtained by GPS campaigns.

On September 30th, 2009, a major earthquake of magnitude 7.6 occurred near the west coast of Sumatra close to the city of Padang. On October 1st a significant aftershock (magnitude 6.6) occurred 270 km away. The casualties are estimated at 1200. This earthquake comes at a time when seismic activity in the region is particularly high. The purpose of this paper is to show for an earthquake occurring in such regions how L-band interferometry can contribute to both damage assessment and a better understanding of the underlying geological phenomena.

Baseline calibration is needed in most of SAR interferometry processing. An iterative optimization of baseline with constrain of relative platform position is presented in this paper. The SAR passes which gives inaccurate platform position is successfully detected and calibrated using this algorithm. After processing, new estimated baseline improves the quality of interferogram. Existence of reference Digital Elevation Model (DEM) error and atmospheric phase screen (APS) can also be detected from the convergence value. This method is based on a reversed concept of platform position estimation from interferometric result. Validation of method performed on multiple SAR images over Singapore.