Recent Improvements in the Dart Model for Atmosphere, Topography, Large Landscape, Chlorophyll Fluorescence, Satellite Image Inversion
Jean‐Philippe Gastellu‐EtchegorryYingjie WangOmar RegaiegTiangang YinZbyněk MalenovskýZhijun ZhenXianhai YangZhifu TaoLucas LandierAhmad Al BitarDeschampsNicolas LauretJordan GuilleuxE. ChavanonB. CaoJ. QiA. KallelZ. MitrakaNektarios ChrysoulakisB. CookDouglas C. Morton
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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.Keywords:
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Mediterranean-type ecosystems are among the most affected by global climate change due to an increase in droughts and fires. Sentinel-2 satellites are currently among the best alternative for operational vegetation properties monitoring because of their temporal revisit and global coverage. The increasing availability of spaceborne imaging spectrometer (e.g. DESIS, PRISMA, EnMAP) and the preparation of missions ensuring global accessibility (e.g. CHIME, BIODIVERSITY) will enable the estimation of vegetation traits with better accuracies. The SENTHYMED project aims to study the complementarity between multi- and hyperspectral images to evaluate Mediterranean forest functional traits. The objective is to estimate canopy pigment, leaf water and dry matter contents from physical model inversion using DART radiative transfer model. A preliminary step is to study the influence of DART optical properties parametrization on remote sensing image simulation in order to simulate scenes as accurately as possible. Two forests in the South of France, mainly composed of evergreen oaks and pubescent oaks, with heterogeneous canopy structure, were studied. UAV LiDAR data were first acquired and converted into voxel matrices of plant area density values with AMAPVox. Pytools4dart was then used to build the mock-ups, handle DART parameterization and generate images in spectral reflectance unit at canopy level. Several simulations were implemented, assigning different optical properties to the underground and to the canopy. These images were compared to airborne AVIRIS-Next generation acquisitions, acquired close to the field campaign that took place in June 2021 and where in-situ measurements were collected for calibration and validation of DART simulations.
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DART (Direct Analysis in Real Time) is an innovative technology to analyze complex solid samples at atmospheric pressure and ground potential by simply placing them between a DART ion source and a mass spectrometer. The analytes are ionized by a gun of neutral metastable species. The first examples of the application of DART to the analysis of flavor and fragrance raw materials in real, complex applications are reported here. A remarkably high potential of the technique is demonstrated. DART was applied to semi-quantitative analyses of perfumery raw materials deposited on smelling strips. In optimal cases, limits of detection around 100 pg were achieved. DART also allowed the assessment of the deposition and release of fragrance on surfaces such as fabric and hair. Finally, DART permitted the screening of twelve chewing gum samples for the possible presence of taste-refreshing compounds in less than 30 min.
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A simplified atmospheric radiative transfer model at L-band has been developed for the Soil Moisture Active/Passive (SMAP) forward brightness temperature (level 1) simulator. The upwelling and downwelling brightness temperatures and the total loss factor of the atmosphere are modeled as polynomial functions of pressure, temperature, and water vapor density near the Earth's surface, as well as incidence angle. The model has been developed and verified by using global radiosonde data, and the model error is within the 0.1 K error budget (atmosphere portion) of the SMAP brightness temperature (Level 1B) product.
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Radiative transfer models (RTMs) of vegetation canopies can be applied for the retrieval of numerical values of vegetation properties from satellite data. For such retrieval, it is necessary first to apply atmospheric correction to translate the top-of-atmosphere (TOA) satellite data into top-of-canopy (TOC) values. This atmospheric correction typically assumes a Lambertian surface reflection, which introduces errors if the real surface is non-Lambertian. Furthermore, atmospheric correction requires atmospheric characterization as input, which is not always available. In this study, we present an RTM for soil-plant-atmosphere systems to model TOC and TOA reflectance as observed by sensors, and to retrieve vegetation properties directly from TOA reflectance skipping the atmosphere correction processes with the inversion mode of the RTM. The model uses three computationally efficient RTMs for soil (BSM), vegetation canopies (PROSAIL) and atmosphere (SMAC), respectively. The sub-models are coupled by using the four-stream theory and the adding method. The resulting 'Soil-Plant-Atmosphere Radiative Transfer model' (SPART) simulates directional TOA spectral observations, with all major effects included, such as sun-observer geometries and non-Lambertian reflectance of the land surface. A sensitivity anaylsis of the model shows that neglecting anisotropic reflection of the surface in coupling the surface with atmosphere causes considerable errors in TOA reflectance. The model was validated by comparing TOC and TOA reflectance simulations with those simulated with the atmosphere-included version of the DART RTM model. We show that the differences between DART and SPART are less than 7% for simulating TOC reflectance, and are less than 20% (less than 10% at most bands) for simulating TOA reflectance. The model performance in retrieving key vegetation and atmospheric properties was evaluted by using a synthetic dataset and a satellite dataset. The inversion mode allows estimating vegetation properties along with atmospheric properties and TOC reflectance with reasonable accuracy directly from TOA observations, and remarkable accuracy can be achieved if prior information is used in the model inversion. The model can be used to investigate the sensitivity of surface and atmospheric properties on TOC and TOA reflectance and for the simulation of synthetic data of existing and forthcoming satellite missions. More importantly, it facilitates a quantitative use of remote sensing data from satellites directly without the need for atmospheric correction.
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DART (Discrete Anisotropic Radiative Transfer) model was designed in 1996 for simulating optical directional images and 3-D radiation budget of heterogeneous 3-D scenes with various landscape elements (i.e., trees, water, grass, soil, etc.). It was already used for many scientific works; e.g., impact of canopy structure on satellite images texture and 3-D canopy photosynthesis rate and primary production rate. It was successfully tested against reflectance measurements and also against radiative transfer (RT) models in the frame of the RAMI exercise. Recently, DART was greatly improved to make it more comprehensive and operational. The new DART 2003 model simulates directional images in the sensor plane, for any altitude, simultaneously in several spectral bands in the whole optical domain, for natural, agricultural and urban landscapes with topography, with/without the simulation of the atmospheric radiative transfer, with/without the use of spectral databases (0.3 /spl mu/m-15 /spl mu/m), etc. In order to validate these improvements, DART 2003 simulations were tested against red and NIR reflectance values that were simulated by some RT models used in the frame of RAMI exercise: (1) two 1-D RT models (ProSAIL, 1/2 Discrete) and (2) five 3-D RT models (Flight, DART, Sprint, RAYTRAN, RGM). Results stress that DART accuracy is compatible with that of other models. Work is being conducted for generalizing this first result. Unfortunately, a few major features of DART (i.e., simulation of directional images, atmospheric RT, thermal inferred) could not be tested because other RT models do not simulate them.
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In this chapter we will introduce you the Dart programming language, including how Dart functions and what Dart is. We'll see what structured programming is and how we can take advantage of it using Dart.
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We introduce applications of Direct Analysis in Real Time (DART) ion source, used in open-air at ground potential under ambient conditions, coupled with time-of-flight or quadrupole mass spectrometer.DART was developed in 2005 as a new type of atmospheric pressure ion source. One of the most interesting features of DART is that researchers can access the place of ionizing point directly. It means we can observe real-time spectra of a sample at ground potential under ambient condition. In this paper, we will introduce the principles and features of DART ion source and some applications of DART mass spectrometry.
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Remote sensing (RS) of solar-induced chlorophyll fluorescence (SIF) has a great potential for monitoring plant photosynthetic activity. Radiative transfer models (RTM) are essential to better interpret and extract information from SIF signals. DART is one of the most comprehensive and accurate 3D RTMs. Its standard mode DART-FT simulates SIF using a discrete ordinates method but is not adapted to large landscapes due to computational constraints. DART-Lux, the new mode based on a bi-directional path tracing algorithm, greatly improves DART computational efficiency for simulating images. This paper presents the theory of a novel SIF modelling algorithm in DART-Lux. We verified its accuracy with DART-FT and the SCOPE model for three types of canopies: turbid medium, maize field and forest. DART-Lux closely matches DART-FT (relative difference < 2%) with much better computational efficiency depending on the scene complexity, number of spectral bands and needed accuracy. For example, simulation time is reduced by a factor of ≈48, and memory usage by ≈50 for a maize field at 1 cm resolution. It allowed to simulate SIF images of large scenes as the 3×3km2 Ripperdan agricultural site that DART-FT could not simulate. The new SIF modelling algorithm opens new horizons for RS studies of large and complex landscapes. It is available as part of released DART versions (v1152 onwards) (https://dart.omp.eu/).
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This paper presents a 1-D atmospheric radiative transfer model that can rigorously take into account molecular absorption along with particulate/molecular scattering. It is unrestricted in spectral resolution and has been developed for remote sensing both as a simulator of high-resolution measurements of polarized reflectivity and a validation tool.
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The surface fluxes of several important radiatively active gases, including H2O, CO2, CH4, N2O, O3, and the chlorofluorocarbons CFC11 and CFC12, were simulated with the radiation band models from the National Center for Atmospheric Research (NCAR) community climate model 3 (CCM3), the single-column community atmospheric model (SCAM), and the Canadian global climate model 3 (GCM3). These results were compared with the measured fluxes for a very cold winter day and with the simulated results for other standard atmospheres using the line-by-line radiative transfer model (LBLRTM). The comparison shows that the total surface radiative flux contributed by all the greenhouse gases combined is well simulated by the SCAM and GCM3 radiation band models. The two models generally agree within about 1% of the line-by-line result for all the atmospheric conditions studied. The error in the total flux simulated by the older CCM3 code, however, can be as large as 7% depending on the atmospheric conditions. The SCAM code consistently models H2O better than the CCM3 and GCM3 codes, typically displaying errors of less than 1 W/m2 for all atmospheric conditions. All of the models have difficulty in modelling accurately the radiative flux of CH4 and N2O. In general, the inaccuracy increases, by as much as 200% in some cases, as the amount of H2O in the atmosphere increases. The source of the problem appears to be related to the overlapping bands of other gases. The error in the ozone flux varies from 5% to 15% for the CCM3 and SCAM models, and it can be as large as 30% for the GCM3 code. The CCM3 and SCAM models simulated the chlorofluorocarbon fluxes to within 0.06 W/m2, but this leads to relative errors of 20%–40% for the various atmospheric scenarios. The errors for the CFCs are even larger in the case of the GCM3 model.
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