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1
Oh, Yisok, Jisung Geba Chang, and Maxim Shoshany. "An Improved Radiative Transfer Model for Polarimetric Backscattering from Agricultural Fields at C- and X-Bands." Journal of Electromagnetic Engineering and Science 21, no. 2 (April 30, 2021): 104–10. http://dx.doi.org/10.26866/jees.2021.21.2.104.
Повний текст джерелаАнотація:
The first-order vector radiative transfer model (FVRTM) is modified mainly by examining the effects of leaf curvature of vegetation canopies, the higher-order multiple scattering among vegetation scattering particles, and the underlying-surface roughness for forward reflection on radar backscattering from farming fields at C- and X-bands. At first, we collected the backscattering coefficients measured by scatterometers and space-borne synthetic aperture radar (SAR), field-measured ground-truth data sets, and theoretical scattering models for radar backscattering from vegetation fields at microwaves. Then, these effects on the RTM were examined using the database at the C- and X-bands. Finally, an improved RTM was obtained by adjusting its parameters, mainly related with the leaf curvature, the higher-order multiple scattering, and the underlying-surface small-roughness characteristics, and its accuracy was verified by comparisons between the improved RTM and measurement data sets.
2
del Águila, Ana, Dmitry S. Efremenko, Víctor Molina García, and Michael Yu Kataev. "Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O2 A- and CO2 Bands." Remote Sensing 12, no. 8 (April 15, 2020): 1250. http://dx.doi.org/10.3390/rs12081250.
Повний текст джерелаАнотація:
Current atmospheric composition sensors provide a large amount of high spectral resolution data. The accurate processing of this data employs time-consuming line-by-line (LBL) radiative transfer models (RTMs). In this paper, we describe a method to accelerate hyperspectral radiative transfer models based on the clustering of the spectral radiances computed with a low-stream RTM and the regression analysis performed for the low-stream and multi-stream RTMs within each cluster. This approach, which we refer to as the Cluster Low-Streams Regression (CLSR) method, is applied for computing the radiance spectra in the O2 A-band at 760 nm and the CO2 band at 1610 nm for five atmospheric scenarios. The CLSR method is also compared with the principal component analysis (PCA)-based RTM, showing an improvement in terms of accuracy and computational performance over PCA-based RTMs. As low-stream models, the two-stream and the single-scattering RTMs are considered. We show that the error of this approach is modulated by the optical thickness of the atmosphere. Nevertheless, the CLSR method provides a performance enhancement of almost two orders of magnitude compared to the LBL model, while the error of the technique is below 0.1% for both bands.
3
Mejia, Felipe A., Ben Kurtz, Keenan Murray, Laura M. Hinkelman, Manajit Sengupta, Yu Xie, and Jan Kleissl. "Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth." Atmospheric Measurement Techniques 9, no. 8 (August 30, 2016): 4151–65. http://dx.doi.org/10.5194/amt-9-4151-2016.
Повний текст джерелаАнотація:
Abstract. A method for retrieving cloud optical depth (τc) using a UCSD developed ground-based sky imager (USI) is presented. The radiance red–blue ratio (RRBR) method is motivated from the analysis of simulated images of various τc produced by a radiative transfer model (RTM). From these images the basic parameters affecting the radiance and red–blue ratio (RBR) of a pixel are identified as the solar zenith angle (θ0), τc, solar pixel angle/scattering angle (ϑs), and pixel zenith angle/view angle (ϑz). The effects of these parameters are described and the functions for radiance, Iλτc, θ0, ϑs, ϑz, and RBRτc, θ0, ϑs, ϑz are retrieved from the RTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for τc, where RBR increases with τc up to about τc = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured Iλmeasϑs, ϑz, in addition to RBRmeasϑs, ϑz, to obtain a unique solution for τc. The RRBR method is applied to images of liquid water clouds taken by a USI at the Oklahoma Atmospheric Radiation Measurement (ARM) program site over the course of 220 days and compared against measurements from a microwave radiometer (MWR) and output from the Min et al. (2003) method for overcast skies. τc values ranged from 0 to 80 with values over 80, being capped and registered as 80. A τc RMSE of 2.5 between the Min et al. (2003) method and the USI are observed. The MWR and USI have an RMSE of 2.2, which is well within the uncertainty of the MWR. The procedure developed here provides a foundation to test and develop other cloud detection algorithms.
4
Vicent, Jorge, Jochem Verrelst, Neus Sabater, Luis Alonso, Juan Pablo Rivera-Caicedo, Luca Martino, Jordi Muñoz-Marí, and José Moreno. "Comparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG) toolbox (version 2.0)." Geoscientific Model Development 13, no. 4 (April 20, 2020): 1945–57. http://dx.doi.org/10.5194/gmd-13-1945-2020.
Повний текст джерелаАнотація:
Abstract. Atmospheric radiative transfer models (RTMs) are software tools that help researchers in understanding the radiative processes occurring in the Earth's atmosphere. Given their importance in remote sensing applications, the intercomparison of atmospheric RTMs is therefore one of the main tasks used to evaluate model performance and identify the characteristics that differ between models. This can be a tedious tasks that requires good knowledge of the model inputs/outputs and the generation of large databases of consistent simulations. With the evolution of these software tools, their increase in complexity bears implications for their use in practical applications and model intercomparison. Existing RTM-specific graphical user interfaces are not optimized for performing intercomparison studies of a wide variety of atmospheric RTMs. In this paper, we present the Atmospheric Look-up table Generator (ALG) version 2.0, a new software tool that facilitates generating large databases for a variety of atmospheric RTMs. ALG facilitates consistent and intuitive user interaction to enable the running of model executions and storing of RTM data for any spectral configuration in the optical domain. We demonstrate the utility of ALG in performing intercomparison studies of radiance simulations from broadly used atmospheric RTMs (6SV, MODTRAN, and libRadtran) through global sensitivity analysis. We expect that providing ALG to the research community will facilitate the usage of atmospheric RTMs to a wide range of applications in Earth observation.
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Karthikeyan, Lanka, Ming Pan, Dasika Nagesh Kumar, and Eric F. Wood. "Effect of Structural Uncertainty in Passive Microwave Soil Moisture Retrieval Algorithm." Sensors 20, no. 4 (February 24, 2020): 1225. http://dx.doi.org/10.3390/s20041225.
Повний текст джерелаАнотація:
Passive microwave sensors use a radiative transfer model (RTM) to retrieve soil moisture (SM) using brightness temperatures (TB) at low microwave frequencies. Vegetation optical depth (VOD) is a key input to the RTM. Retrieval algorithms can analytically invert the RTM using dual-polarized TB measurements to retrieve the VOD and SM concurrently. Algorithms in this regard typically use the τ-ω types of models, which consist of two third-order polynomial equations and, thus, can have multiple solutions. Through this work, we find that uncertainty occurs due to the structural indeterminacy that is inherent in all τ-ω types of models in passive microwave SM retrieval algorithms. In the process, a new analytical solution for concurrent VOD and SM retrieval is presented, along with two widely used existing analytical solutions. All three solutions are applied to a fixed framework of RTM to retrieve VOD and SM on a global scale, using X-band Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB data. Results indicate that, with structural uncertainty, there ensues a noticeable impact on the VOD and SM retrievals. In an era where the sensitivity of retrieval algorithms is still being researched, we believe the structural indeterminacy of RTM identified here would contribute to uncertainty in the soil moisture retrievals.
6
Seidel, F. C., A. A. Kokhanovsky, and M. E. Schaepman. "Fast and simple model for atmospheric radiative transfer." Atmospheric Measurement Techniques Discussions 3, no. 3 (May 18, 2010): 2225–73. http://dx.doi.org/10.5194/amtd-3-2225-2010.
Повний текст джерелаАнотація:
Abstract. Radiative transfer models (RTMs) are of utmost importance for quantitative remote sensing, especially for compensating atmospheric perturbation. A persistent trade-off exists between approaches that prefer accuracy at the cost of computational complexity, versus those favouring simplicity at the cost of reduced accuracy. We propose an approach in the latter category, using analytical equations, parameterizations and a correction factor to efficiently estimate the effect of molecular multiple scattering. We discuss the approximations together with an analysis of the resulting performance and accuracy. The proposed Simple Model for Atmospheric Radiative Transfer (SMART) decreases the calculation time by a factor of more than 25 in comparison to the benchmark RTM~6S on the same infrastructure. The approximative computation of the atmospheric reflectance factor by SMART has an uncertainty ranging from about 5% to 10% for nadir spaceborne and airborne observational conditions. The combination of a large solar zenith angle (SZA) with high aerosol optical depth (AOD) at low wavelengths lead to uncertainties of up to 15%. SMART can be used to simulate the hemispherical conical reflectance factor (HCRF) for spaceborne and airborne sensors, as well as for the retrieval of columnar AOD.
7
Seidel, F. C., A. A. Kokhanovsky, and M. E. Schaepman. "Fast and simple model for atmospheric radiative transfer." Atmospheric Measurement Techniques 3, no. 4 (August 25, 2010): 1129–41. http://dx.doi.org/10.5194/amt-3-1129-2010.
Повний текст джерелаАнотація:
Abstract. Radiative transfer models (RTMs) are of utmost importance for quantitative remote sensing, especially for compensating atmospheric perturbation. A persistent trade-off exists between approaches that prefer accuracy at the cost of computational complexity, versus those favouring simplicity at the cost of reduced accuracy. We propose an approach in the latter category, using analytical equations, parameterizations and a correction factor to efficiently estimate the effect of molecular multiple scattering. We discuss the approximations together with an analysis of the resulting performance and accuracy. The proposed Simple Model for Atmospheric Radiative Transfer (SMART) decreases the calculation time by a factor of more than 25 in comparison to the benchmark RTM 6S on the same infrastructure. The relative difference between SMART and 6S is about 5% for spaceborne and about 10% for airborne computations of the atmospheric reflectance function. The combination of a large solar zenith angle (SZA) with high aerosol optical depth (AOD) at low wavelengths lead to relative differences of up to 15%. SMART can be used to simulate the hemispherical conical reflectance factor (HCRF) for spaceborne and airborne sensors, as well as for the retrieval of columnar AOD.
8
Albino, André, Daniele Bortoli, Mouhaydine Tlemçani, Abdeloawahed Hajjaji, and António Joyce. "Sensitivity analysis of atmospheric spectral irradiance model." European Physical Journal Applied Physics 88, no. 1 (October 2019): 11001. http://dx.doi.org/10.1051/epjap/2019190350.
Повний текст джерелаАнотація:
Many Radiative Transfer Models (RTM) have been developed to simulate and estimate solar irradiance. Theirs accuracy is well documents in literature nonetheless the effect of the parameters uncertainties on the established models has not been well studied yet. This work focuses on implementing a RTM based on the models found in the literature along with some updates, with the aim to study the sensitivity of the model towards the variations of the input parameters. The parameters studied in this paper are: the day of the year, the solar zenith angle, the local atmospheric pressure, the local temperature, the relative humidity, the height of ozone layer concentration, the ozone concentration, the single scattering albedo, the ground albedo, the Ångström’s exponent and the aerosol optical depth. The sensibility analysis is achieved using the Normalized Root Mean Square Error (NRMSE) as an independent function, calculated with a set of simulated measurements of spectral global solar irradiance and a reference spectrum generated with a group of standard input parameters.
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del Águila, Ana, and Dmitry S. Efremenko. "Fast Hyper-Spectral Radiative Transfer Model Based on the Double Cluster Low-Streams Regression Method." Remote Sensing 13, no. 3 (January 27, 2021): 434. http://dx.doi.org/10.3390/rs13030434.
Повний текст джерелаАнотація:
Fast radiative transfer models (RTMs) are required to process a great amount of satellite-based atmospheric composition data. Specifically designed acceleration techniques can be incorporated in RTMs to simulate the reflected radiances with a fine spectral resolution, avoiding time-consuming computations on a fine resolution grid. In particular, in the cluster low-streams regression (CLSR) method, the computations on a fine resolution grid are performed by using the fast two-stream RTM, and then the spectra are corrected by using regression models between the two-stream and multi-stream RTMs. The performance enhancement due to such a scheme can be of about two orders of magnitude. In this paper, we consider a modification of the CLSR method (which is referred to as the double CLSR method), in which the single-scattering approximation is used for the computations on a fine resolution grid, while the two-stream spectra are computed by using the regression model between the two-stream RTM and the single-scattering approximation. Once the two-stream spectra are known, the CLSR method is applied the second time to restore the multi-stream spectra. Through a numerical analysis, it is shown that the double CLSR method yields an acceleration factor of about three orders of magnitude as compared to the reference multi-stream fine-resolution computations. The error of such an approach is below 0.05%. In addition, it is analysed how the CLSR method can be adopted for efficient computations for atmospheric scenarios containing aerosols. In particular, it is discussed how the precomputed data for clear sky conditions can be reused for computing the aerosol spectra in the framework of the CLSR method. The simulations are performed for the Hartley–Huggins, O2 A-, water vapour and CO2 weak absorption bands and five aerosol models from the optical properties of aerosols and clouds (OPAC) database.
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Abdelbaki, Asmaa, and Thomas Udelhoven. "A Review of Hybrid Approaches for Quantitative Assessment of Crop Traits Using Optical Remote Sensing: Research Trends and Future Directions." Remote Sensing 14, no. 15 (July 22, 2022): 3515. http://dx.doi.org/10.3390/rs14153515.
Повний текст джерелаАнотація:
Remote sensing technology allows to provide information about biochemical and biophysical crop traits and monitor their spatiotemporal dynamics of agriculture ecosystems. Among multiple retrieval techniques, hybrid approaches have been found to provide outstanding accuracy, for instance, for the inference of leaf area index (LAI), fractional vegetation cover (fCover), and leaf and canopy chlorophyll content (LCC and CCC). The combination of radiative transfer models (RTMs) and data-driven models creates an advantage in the use of hybrid methods. Through this review paper, we aim to provide state-of-the-art hybrid retrieval schemes and theoretical frameworks. To achieve this, we reviewed and systematically analyzed publications over the past 22 years. We identified two hybrid-based parametric and hybrid-based nonparametric regression models and evaluated their performance for each variable of interest. From the results of our extensive literature survey, most research directions are now moving towards combining RTM and machine learning (ML) methods in a symbiotic manner. In particular, the development of ML will open up new ways to integrate innovative approaches such as integrating shallow or deep neural networks with RTM using remote sensing data to reduce errors in crop trait estimations and improve control of crop growth conditions in very large areas serving precision agriculture applications.
11
Zhang, Xuanze, Xiaogu Zheng, Zhian Sun, and San Luo. "Trends of MSU Brightness Temperature in the Middle Troposphere Simulated by CMIP5 Models and Their Sensitivity to Cloud Liquid Water." Journal of Atmospheric and Oceanic Technology 32, no. 5 (May 2015): 1029–41. http://dx.doi.org/10.1175/jtech-d-13-00250.1.
Повний текст джерелаАнотація:
AbstractOnly a climate model that is able to simulate well the historical atmospheric temperature trend can be used for estimating the future atmospheric temperature trends on different emission scenarios. Satellite-based Microwave Sounding Unit (MSU) brightness temperature in the middle troposphere (T2) is an important analog of midtropospheric atmospheric temperature. So, there is the need to compare the atmospheric temperature trend simulated by the fifth phase of the Coupled Model Intercomparison Project (CMIP5) historical realizations and the observed MSU T2. There are two approaches for estimating modeled MSU T2: apply a global-mean static weighting function to generate the weighted average of the modeled temperature at all atmospheric layers and simulate satellite-view MSU T2 using the model’s output as input into a radiative transfer model (RTM).In this paper, the two approaches for estimating modeled MSU T2 are evaluated. For each CMIP5, it is shown that there exists a model-simulated static weighting function, such that the MSU T2 trend using the weighting function is equivalent to that calculated by RTM. The effect of modeled cloud liquid water on MSU T2 trends in CMIP5 simulations is investigated by comparing the modeled cloud liquid water vertical profile and the weighting function. Moreover, it is found that warming trends of MSU T2 for CMIP5 simulations calculated by the RTM are about 15% less than those using the two traditional static weighting functions. By comparing the model-derived weighting function with the two traditional weighting functions, the reason for the systematical biases is revealed.
12
Melendo-Vega, José, M. Martín, Javier Pacheco-Labrador, Rosario González-Cascón, Gerardo Moreno, Fernando Pérez, Mirco Migliavacca, Mariano García, Peter North, and David Riaño. "Improving the Performance of 3-D Radiative Transfer Model FLIGHT to Simulate Optical Properties of a Tree-Grass Ecosystem." Remote Sensing 10, no. 12 (December 18, 2018): 2061. http://dx.doi.org/10.3390/rs10122061.
Повний текст джерелаАнотація:
The 3-D Radiative Transfer Model (RTM) FLIGHT can represent scattering in open forest or savannas featuring underlying bare soils. However, FLIGHT might not be suitable for multilayered tree-grass ecosystems (TGE), where a grass understory can dominate the reflectance factor (RF) dynamics due to strong seasonal variability and low tree fractional cover. To address this issue, we coupled FLIGHT with the 1-D RTM PROSAIL. The model is evaluated against spectral observations of proximal and remote sensing sensors: the ASD Fieldspec® 3 spectroradiometer, the Airborne Spectrographic Imager (CASI) and the MultiSpectral Instrument (MSI) onboard Sentinel-2. We tested the capability of both PROSAIL and PROSAIL+FLIGHT to reproduce the variability of different phenological stages determined by 16-year time series analysis of Moderate Resolution Imaging Spectroradiometer-Normalized Difference Vegetation Index (MODIS-NDVI). Then, we combined concomitant observations of biophysical variables and RF to test the capability of the models to reproduce observed RF. PROSAIL achieved a Relative Root Mean Square Error (RRMSE) between 6% to 32% at proximal sensing scale. PROSAIL+FLIGHT RRMSE ranged between 7% to 31% at remote sensing scales. RRMSE increased in periods when large fractions of standing dead material mixed with emergent green grasses —especially in autumn—; suggesting that the model cannot represent the spectral features of this material. PROSAIL+FLIGHT improves RF simulation especially in summer and at mid-high view angles.
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Lisok, Justyna, Anna Rozwadowska, Jesper G. Pedersen, Krzysztof M. Markowicz, Christoph Ritter, Jacek W. Kaminski, Joanna Struzewska, et al. "Radiative impact of an extreme Arctic biomass-burning event." Atmospheric Chemistry and Physics 18, no. 12 (June 22, 2018): 8829–48. http://dx.doi.org/10.5194/acp-18-8829-2018.
Повний текст джерелаАнотація:
Abstract. The aim of the presented study was to investigate the impact on the radiation budget of a biomass-burning plume, transported from Alaska to the High Arctic region of Ny-Ålesund, Svalbard, in early July 2015. Since the mean aerosol optical depth increased by the factor of 10 above the average summer background values, this large aerosol load event is considered particularly exceptional in the last 25 years. In situ data with hygroscopic growth equations, as well as remote sensing measurements as inputs to radiative transfer models, were used, in order to estimate biases associated with (i) hygroscopicity, (ii) variability of single-scattering albedo profiles, and (iii) plane-parallel closure of the modelled atmosphere. A chemical weather model with satellite-derived biomass-burning emissions was applied to interpret the transport and transformation pathways. The provided MODTRAN radiative transfer model (RTM) simulations for the smoke event (14:00 9 July–11:30 11 July) resulted in a mean aerosol direct radiative forcing at the levels of −78.9 and −47.0 W m−2 at the surface and at the top of the atmosphere, respectively, for the mean value of aerosol optical depth equal to 0.64 at 550 nm. This corresponded to the average clear-sky direct radiative forcing of −43.3 W m−2, estimated by radiometer and model simulations at the surface. Ultimately, uncertainty associated with the plane-parallel atmosphere approximation altered results by about 2 W m−2. Furthermore, model-derived aerosol direct radiative forcing efficiency reached on average −126 W m-2/τ550 and −71 W m-2/τ550 at the surface and at the top of the atmosphere, respectively. The heating rate, estimated at up to 1.8 K day−1 inside the biomass-burning plume, implied vertical mixing with turbulent kinetic energy of 0.3 m2 s−2.
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Bates, T. S., T. L. Anderson, T. Baynard, T. Bond, O. Boucher, G. Carmichael, A. Clarke, et al. "Aerosol direct radiative effects over the northwest Atlantic, northwest Pacific, and North Indian Oceans: estimates based on in-situ chemical and optical measurements and chemical transport modeling." Atmospheric Chemistry and Physics Discussions 6, no. 1 (January 3, 2006): 175–362. http://dx.doi.org/10.5194/acpd-6-175-2006.
Повний текст джерелаАнотація:
Abstract. The largest uncertainty in the radiative forcing of climate change over the industrial era is that due to aerosols, a substantial fraction of which is the uncertainty associated with scattering and absorption of shortwave (solar) radiation by anthropogenic aerosols in cloud-free conditions (IPCC, 2001). Quantifying and reducing the uncertainty in aerosol influences on climate is critical to understanding climate change over the industrial period and to improving predictions of future climate change for assumed emission scenarios. Measurements of aerosol properties during major field campaigns in several regions of the globe during the past decade are contributing to an enhanced understanding of atmospheric aerosols and their effects on light scattering and climate. The present study, which focuses on three regions downwind of major urban/population centers (North Indian Ocean (NIO) during INDOEX, the Northwest Pacific Ocean (NWP) during ACE-Asia, and the Northwest Atlantic Ocean (NWA) during ICARTT), incorporates understanding gained from field observations of aerosol distributions and properties into calculations of perturbations in radiative fluxes due to these aerosols. This study evaluates the current state of observations and of two chemical transport models (STEM and MOZART). Measurements of burdens, extinction optical depth (AOD), and direct radiative effect of aerosols (DRE – change in radiative flux due to total aerosols) are used as measurement-model check points to assess uncertainties. In-situ measured and remotely sensed aerosol properties for each region (mixing state, mass scattering efficiency, single scattering albedo, and angular scattering properties and their dependences on relative humidity) are used as input parameters to two radiative transfer models (GFDL and University of Michigan) to constrain estimates of aerosol radiative effects, with uncertainties in each step propagated through the analysis. Constraining the radiative transfer calculations by observational inputs increases the clear-sky, 24-h averaged AOD (34±8%), top of atmosphere (TOA) DRE (32±12%), and TOA direct climate forcing of aerosols (DCF – change in radiative flux due to anthropogenic aerosols) (37±7%) relative to values obtained with "a priori" parameterizations of aerosol loadings and properties (GFDL RTM). The resulting constrained TOA DCF is −3.3±0.47, −14±2.6, −6.4±2.1 Wm−2 for the NIO, NWP, and NWA, respectively. Constraining the radiative transfer calculations by observational inputs reduces the uncertainty range in the DCF in these regions relative to global IPCC (2001) estimates by a factor of approximately 2. Such comparisons with observations and resultant reductions in uncertainties are essential for improving and developing confidence in climate model calculations incorporating aerosol forcing.
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Zhang, Hu, Jing Li, Qinhuo Liu, Yadong Dong, Songze Li, Zhaoxing Zhang, Xinran Zhu, Liangyun Liu, and Jing Zhao. "Estimating Leaf Area Index with Dynamic Leaf Optical Properties." Remote Sensing 13, no. 23 (December 2, 2021): 4898. http://dx.doi.org/10.3390/rs13234898.
Повний текст джерелаАнотація:
Leaf area index (LAI) plays an important role in models of climate, hydrology, and ecosystem productivity. The physical model-based inversion method is a practical approach for large-scale LAI inversion. However, the ill-posed inversion problem, due to the limited constraint of inaccurate input parameters, is the dominant source of inversion errors. For instance, variables related to leaf optical properties are always set as constants or have large ranges, instead of the actual leaf reflectance of pixel vegetation in the current model-based inversions. This paper proposes to estimate LAI with the actual leaf optical property of pixels, calculated from the leaf chlorophyll content (Chlleaf) product, using a three-dimensional stochastic radiative transfer model (3D-RTM)-based, look-up table method. The parameter characterizing leaf optical properties in the 3D-RTM-based LAI inversion algorithm, single scattering albedo (SSA), is calculated with the Chlleaf product, instead of setting fixed values across a growing season. An algorithm to invert LAI with the dynamic SSA of the red band (SSAred) is proposed. The retrieval index (RI) increases from less than 42% to 100%, and the RMSE decreases to less than 0.28 in the simulations. The validation results show that the RMSE of the dynamic SSA decreases from 1.338 to 0.511, compared with the existing 3D-RTM-based LUT algorithm. The overestimation problem under high LAI conditions is reduced.
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Marino, Eva, Marta Yebra, Mariluz Guillén-Climent, Nur Algeet, José Luis Tomé, Javier Madrigal, Mercedes Guijarro, and Carmen Hernando. "Investigating Live Fuel Moisture Content Estimation in Fire-Prone Shrubland from Remote Sensing Using Empirical Modelling and RTM Simulations." Remote Sensing 12, no. 14 (July 14, 2020): 2251. http://dx.doi.org/10.3390/rs12142251.
Повний текст джерелаАнотація:
Previous research has demonstrated that remote sensing can provide spectral information related to vegetation moisture variations essential for estimating live fuel moisture content (LFMC), but accuracy and timeliness still present challenges to using this information operationally. Consequently, many regional administrations are investing important resources in field campaigns for LFMC monitoring, often focusing on indicator species to reduce sampling time and costs. This paper compares different remote sensing approaches to provide LFMC prediction of Cistus ladanifer, a fire-prone shrub species commonly found in Mediterranean areas and used by fire management services as an indicator species for wildfire risk assessment. Spectral indices (SI) were derived from satellite imagery of different spectral, spatial, and temporal resolution, including Sentinel-2 and two different reflectance products of the Moderate Resolution Imaging Spectrometer (MODIS); MCD43A4 and MOD09GA. The SI were used to calibrate empirical models for LFMC estimation using on ground field LFMC measurements from a monospecific shrubland area located in Madrid (Spain). The empirical models were fitted with different statistical methods: simple (LR) and multiple linear regression (MLR), non-linear regression (NLR), and general additive models with splines (GAMs). MCD43A4 images were also used to estimate LFMC from the inversion of radiative transfer models (RTM). Empirical model predictions and RTM simulations of LFMC were validated and compared using an independent sample of LFMC values observed in the field. Empirical models derived from MODIS products and Sentinel-2 data showed R2 between estimated and observed LFMC from 0.72 to 0.75 and mean absolute errors ranging from 11% to 13%. GAMs outperformed regression methods in model calibration, but NLR had better results in model validation. LFMC derived from RTM simulations had a weaker correlation with field data (R2 = 0.49) than the best empirical model fitted with MCD43A4 images (R2 = 0.75). R2 between observations and LFMC derived from RTM ranged from 0.56 to 0.85 when the validation was performed for each year independently. However, these values were still lower than the equivalent statistics using the empirical models (R2 from 0.65 to 0.94) and the mean absolute errors per year for RTM were still high (ranging from 25% to 38%) compared to the empirical model (ranging 7% to 15%). Our results showed that spectral information derived from Sentinel-2 and different MODIS products provide valuable information for LFMC estimation in C. ladanifer shrubland. However, both empirical and RTM approaches tended to overestimate the lowest LFMC values, and therefore further work is needed to improve predictions, especially below the critical LFMC threshold used by fire management services to indicate higher flammability (<80%). Although lower extreme LFMC values are still difficult to estimate, the proposed empirical models may be useful to identify when the critical threshold for high fire risk has been reached with reasonable accuracy. This study demonstrates that remote sensing data is a promising source of information to derive reliable and cost-effective LFMC estimation models that can be used in operational wildfire risk systems.
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Youn, D., K. O. Patten, J. T. Lin, and D. J. Wuebbles. "Explicit calculation of indirect global warming potentials for halons using atmospheric models." Atmospheric Chemistry and Physics Discussions 9, no. 4 (July 21, 2009): 15511–40. http://dx.doi.org/10.5194/acpd-9-15511-2009.
Повний текст джерелаАнотація:
Abstract. The concept of Global Warming Potentials (GWPs) has been extensively used in policy consideration as a relative index for comparing the climate impact of an emitted greenhouse gas (GHG), relative to carbon dioxide with equal mass emissions. Ozone depletion due to emission of chlorinated or brominated halocarbons leads to cooling of the climate system in the opposite direction to the direct warming contribution by halocarbons as GHGs. This cooling is a key indirect effect of the halocarbons on climatic radiative forcing, which is accounted for by indirect GWPs. With respect to climate, it is critical to understand net influences considering direct warming and indirect cooling effects for Halons. Until now, the indirect GWPs have been calculated using a para\\-meterized approach based on the concept of Equivalent Effective Stratospheric Chlorine (EESC) and the observed ozone depletion over the last few decades. As a step towards obtaining indirect GWPs through a more robust approach, we use atmospheric models to explicitly calculate the indirect GWPs of Halon-1211 and Halon-1301 for a 100-year time horizon. State-of-the-art global chemistry-transport models (CTMs) were used as the computational tools to derive more realistic ozone depletion changes caused by an added pulse emission of the two major Halons at the surface. The radiative forcings on climate from the ozone changes have been calculated for indirect GWPs using an atmospheric radiative transfer model (RTM). The simulated temporal variations of global average total column Halons after a pulse perturbation follow an exponential decay with an e-folding time which is consistent with the expected chemical lifetimes of the Halons. Our calculated indirect GWPs for the two Halons are smaller than past studies although direct GWPs agree with the published values. Nonetheless, our model-based assessment of the Halon indirect GWPs confirms the significant importance of indirect effects on climate.
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Wagner, T., J. P. Burrows, T. Deutschmann, B. Dix, C. von Friedeburg, U. Frieß, F. Hendrick, et al. "Comparison of box-air-mass-factors and radiances for Multiple-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) geometries calculated from different UV/visible radiative transfer models." Atmospheric Chemistry and Physics 7, no. 7 (April 13, 2007): 1809–33. http://dx.doi.org/10.5194/acp-7-1809-2007.
Повний текст джерелаАнотація:
Abstract. The results of a comparison exercise of radiative transfer models (RTM) of various international research groups for Multiple AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) viewing geometry are presented. Besides the assessment of the agreement between the different models, a second focus of the comparison was the systematic investigation of the sensitivity of the MAX-DOAS technique under various viewing geometries and aerosol conditions. In contrast to previous comparison exercises, box-air-mass-factors (box-AMFs) for different atmospheric height layers were modelled, which describe the sensitivity of the measurements as a function of altitude. In addition, radiances were calculated allowing the identification of potential errors, which might be overlooked if only AMFs are compared. Accurate modelling of radiances is also a prerequisite for the correct interpretation of satellite observations, for which the received radiance can strongly vary across the large ground pixels, and might be also important for the retrieval of aerosol properties as a future application of MAX-DOAS. The comparison exercises included different wavelengths and atmospheric scenarios (with and without aerosols). The strong and systematic influence of aerosol scattering indicates that from MAX-DOAS observations also information on atmospheric aerosols can be retrieved. During the various iterations of the exercises, the results from all models showed a substantial convergence, and the final data sets agreed for most cases within about 5%. Larger deviations were found for cases with low atmospheric optical depth, for which the photon path lengths along the line of sight of the instrument can become very large. The differences occurred between models including full spherical geometry and those using only plane parallel approximation indicating that the correct treatment of the Earth's sphericity becomes indispensable. The modelled box-AMFs constitute an universal data base for the calculation of arbitrary (total) AMFs by simple convolution with a given trace gas concentration profile. Together with the modelled radiances and the specified settings for the various exercises, they can serve as test cases for future RTM developments.
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Lu, Bing, and Yuhong He. "Evaluating Empirical Regression, Machine Learning, and Radiative Transfer Modelling for Estimating Vegetation Chlorophyll Content Using Bi-Seasonal Hyperspectral Images." Remote Sensing 11, no. 17 (August 22, 2019): 1979. http://dx.doi.org/10.3390/rs11171979.
Повний текст джерелаАнотація:
Different types of methods have been developed to retrieve vegetation attributes from remote sensing data, including conventional empirical regressions (i.e., linear regression (LR)), advanced empirical regressions (e.g., multivariable linear regression (MLR), partial least square regression (PLSR)), machine learning (e.g., random forest regression (RFR), decision tree regression (DTR)), and radiative transfer modelling (RTM, e.g., PROSAIL). Given that each algorithm has its own strengths and weaknesses, it is essential to compare them and evaluate their effectiveness. Previous studies have mainly used single-date multispectral imagery or ground-based hyperspectral reflectance data for evaluating the models, while multi-seasonal hyperspectral images have been rarely used. Extensive spectral and spatial information in hyperspectral images, as well as temporal variations of landscapes, potentially influence the model performance. In this research, LR, PLSR, RFR, and PROSAIL, representing different types of methods, were evaluated for estimating vegetation chlorophyll content from bi-seasonal hyperspectral images (i.e., a middle- and a late-growing season image, respectively). Results show that the PLSR and RFR generally performed better than LR and PROSAIL. RFR achieved the highest accuracy for both images. This research provides insights on the effectiveness of different models for estimating vegetation chlorophyll content using hyperspectral images, aiming to support future vegetation monitoring research.
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Bates, T. S., T. L. Anderson, T. Baynard, T. Bond, O. Boucher, G. Carmichael, A. Clarke, et al. "Aerosol direct radiative effects over the northwest Atlantic, northwest Pacific, and North Indian Oceans: estimates based on in-situ chemical and optical measurements and chemical transport modeling." Atmospheric Chemistry and Physics 6, no. 6 (May 22, 2006): 1657–732. http://dx.doi.org/10.5194/acp-6-1657-2006.
Повний текст джерелаАнотація:
Abstract. The largest uncertainty in the radiative forcing of climate change over the industrial era is that due to aerosols, a substantial fraction of which is the uncertainty associated with scattering and absorption of shortwave (solar) radiation by anthropogenic aerosols in cloud-free conditions (IPCC, 2001). Quantifying and reducing the uncertainty in aerosol influences on climate is critical to understanding climate change over the industrial period and to improving predictions of future climate change for assumed emission scenarios. Measurements of aerosol properties during major field campaigns in several regions of the globe during the past decade are contributing to an enhanced understanding of atmospheric aerosols and their effects on light scattering and climate. The present study, which focuses on three regions downwind of major urban/population centers (North Indian Ocean (NIO) during INDOEX, the Northwest Pacific Ocean (NWP) during ACE-Asia, and the Northwest Atlantic Ocean (NWA) during ICARTT), incorporates understanding gained from field observations of aerosol distributions and properties into calculations of perturbations in radiative fluxes due to these aerosols. This study evaluates the current state of observations and of two chemical transport models (STEM and MOZART). Measurements of burdens, extinction optical depth (AOD), and direct radiative effect of aerosols (DRE – change in radiative flux due to total aerosols) are used as measurement-model check points to assess uncertainties. In-situ measured and remotely sensed aerosol properties for each region (mixing state, mass scattering efficiency, single scattering albedo, and angular scattering properties and their dependences on relative humidity) are used as input parameters to two radiative transfer models (GFDL and University of Michigan) to constrain estimates of aerosol radiative effects, with uncertainties in each step propagated through the analysis. Constraining the radiative transfer calculations by observational inputs increases the clear-sky, 24-h averaged AOD (34±8%), top of atmosphere (TOA) DRE (32±12%), and TOA direct climate forcing of aerosols (DCF – change in radiative flux due to anthropogenic aerosols) (37±7%) relative to values obtained with "a priori" parameterizations of aerosol loadings and properties (GFDL RTM). The resulting constrained clear-sky TOA DCF is −3.3±0.47, −14±2.6, −6.4±2.1 Wm−2 for the NIO, NWP, and NWA, respectively. With the use of constrained quantities (extensive and intensive parameters) the calculated uncertainty in DCF was 25% less than the "structural uncertainties" used in the IPCC-2001 global estimates of direct aerosol climate forcing. Such comparisons with observations and resultant reductions in uncertainties are essential for improving and developing confidence in climate model calculations incorporating aerosol forcing.
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de Sá, Nuno César, Mitra Baratchi, Leon T. Hauser, and Peter van Bodegom. "Exploring the Impact of Noise on Hybrid Inversion of PROSAIL RTM on Sentinel-2 Data." Remote Sensing 13, no. 4 (February 11, 2021): 648. http://dx.doi.org/10.3390/rs13040648.
Повний текст джерелаАнотація:
Remote sensing (RS) of biophysical variables plays a vital role in providing the information necessary for understanding spatio-temporal dynamics in ecosystems. The hybrid approach to retrieve biophysical variables from RS by combining Machine Learning (ML) algorithms with surrogate data generated by Radiative Transfer Models (RTM). The susceptibility of the ill-posed solutions to noise currently constrains further application of hybrid approaches. Here, we explored how noise affects the performance of ML algorithms for biophysical trait retrieval. We focused on synthetic Sentinel-2 (S2) data generated using the PROSAIL RTM and four commonly applied ML algorithms: Gaussian Processes (GPR), Random Forests (RFR), and Artificial Neural Networks (ANN) and Multi-task Neural Networks (MTN). After identifying which biophysical variables can be retrieved from S2 using a Global Sensitivity Analysis, we evaluated the performance loss of each algorithm using the Mean Absolute Percentage Error (MAPE) with increasing noise levels. We found that, for S2 data, Carotenoid concentrations are uniquely dependent on band 2, Chlorophyll is almost exclusively dependent on the visible ranges, and Leaf Area Index, water, and dry matter contents are mostly dependent on infrared bands. Without added noise, GPR was the best algorithm (<0.05%), followed by the MTN (<3%) and ANN (<5%), with the RFR performing very poorly (<50%). The addition of noise critically affected the performance of all algorithms (>20%) even at low levels of added noise (≈5%). Overall, both neural networks performed significantly better than GPR and RFR when noise was added with the MTN being slightly better when compared to the ANN. Our results imply that the performance of the commonly used algorithms in hybrid-RTM inversion are pervasively sensitive to noise. The implication is that more advanced models or approaches are necessary to minimize the impact of noise to improve near real-time and accurate RS monitoring of biophysical trait retrieval.
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Wagner, T., J. P. Burrows, T. Deutschmann, B. Dix, C. von Friedeburg, U. Frieß, F. Hendrick, et al. "Comparison of Box-Air-Mass-Factors and Radiances for Multiple-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) Geometries calculated from different UV/visible Radiative Transfer Models." Atmospheric Chemistry and Physics Discussions 6, no. 5 (October 6, 2006): 9823–76. http://dx.doi.org/10.5194/acpd-6-9823-2006.
Повний текст джерелаАнотація:
Abstract. The results of a comparison exercise of radiative transfer models (RTM) of various international research groups for Multiple AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) viewing geometry are presented. In contrast to previous comparison exercises, box-air-mass-factors (box-AMFs) for various atmospheric height layers were modelled, which describe the sensitivity of the measurements as a function of altitude. In addition, radiances were calculated allowing the identification of potential errors, which might be overlooked if only AMFs are compared. Accurate modelling of radiances is also a prerequisite for the correct interpretation of satellite observations, for which the received radiance can strongly vary across the large ground pixels, and might be also important for the retrieval of aerosol properties as a future application of MAX-DOAS. The comparison exercises included different wavelengths and atmospheric scenarios (with and without aerosols). The results were systematically investigated with respect to their dependence on the telescope's elevation angle and the azimuth angle. For both dependencies, a strong and systematic influence of aerosol scattering was found indicating that from MAX-DOAS observations also information on atmospheric aerosols can be retrieved. During the various iterations of the exercises, the results from all models showed a substantial convergence, and the final data sets agreed for most cases within about 5%. Larger deviations were found for cases with low atmospheric optical depth, for which the photon path lengths along the line of sight of the instrument can become very large. The differences occurred between models including full spherical geometry and those using only plane parallel approximation indicating that the correct treatment of the Earth's sphericity becomes indispensable. The modelled box-AMFs constitute an universal data base for the calculation of arbitrary (total) AMFs by simple convolution with a given trace gas concentration profile. Together with the modelled radiances and the specified settings for the various exercises, they can serve as test cases for future RTM developments.
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García, Rosa Delia, Africa Barreto, Emilio Cuevas, Julian Gröbner, Omaira Elena García, Angel Gómez-Peláez, Pedro Miguel Romero-Campos, Alberto Redondas, Victoria Eugenia Cachorro, and Ramon Ramos. "Comparison of observed and modeled cloud-free longwave downward radiation (2010–2016) at the high mountain BSRN Izaña station." Geoscientific Model Development 11, no. 6 (June 12, 2018): 2139–52. http://dx.doi.org/10.5194/gmd-11-2139-2018.
Повний текст джерелаАнотація:
Abstract. A 7-year (2010–2016) comparison study between measured and simulated longwave downward radiation (LDR) under cloud-free conditions was performed at the Izaña Atmospheric Observatory (IZO, Spain). This analysis encompasses a total of 2062 cases distributed approximately evenly between day and night. Results show an excellent agreement between Baseline Surface Radiation Network (BSRN) measurements and simulations with libRadtran V2.0.1 and MODerate resolution atmospheric TRANsmission model (MODTRAN) V6 radiative transfer models (RTMs). Mean bias (simulated − measured) of < 1.1 % and root mean square of the bias (RMS) of < 1 % are within the instrumental error (2 %). These results highlight the good agreement between the two RTMs, proving to be useful tools for the quality control of LDR observations and for detecting temporal drifts in field instruments. The standard deviations of the residuals, associated with the RTM input parameters uncertainties are rather small, 0.47 and 0.49 % for libRadtran and MODTRAN, respectively, at daytime, and 0.49 to 0.51 % at night-time. For precipitable water vapor (PWV) > 10 mm, the observed night-time difference between models and measurements is +5 W m−2 indicating a scale change of the World Infrared Standard Group of Pyrgeometers (WISG), which serves as reference for atmospheric longwave radiation measurements. Preliminary results suggest a possible impact of dust aerosol on infrared radiation during daytime that might not be correctly parametrized by the models, resulting in a slight underestimation of the modeled LDR, of about −3 W m−2, for relatively high aerosol optical depth (AOD > 0.20).
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Tang, Guanglin, Ping Yang, George W. Kattawar, Xianglei Huang, Eli J. Mlawer, Bryan A. Baum, and Michael D. King. "Improvement of the Simulation of Cloud Longwave Scattering in Broadband Radiative Transfer Models." Journal of the Atmospheric Sciences 75, no. 7 (June 18, 2018): 2217–33. http://dx.doi.org/10.1175/jas-d-18-0014.1.
Повний текст джерелаАнотація:
Abstract Cloud longwave scattering is generally neglected in general circulation models (GCMs), but it plays a significant and highly uncertain role in the atmospheric energy budget as demonstrated in recent studies. To reduce the errors caused by neglecting cloud longwave scattering, two new radiance adjustment methods are developed that retain the computational efficiency of broadband radiative transfer simulations. In particular, two existing scaling methods and the two new adjustment methods are implemented in the Rapid Radiative Transfer Model (RRTM). The results are then compared with those based on the Discrete Ordinate Radiative Transfer model (DISORT) that explicitly accounts for multiple scattering by clouds. The two scaling methods are shown to improve the accuracy of radiative transfer simulations for optically thin clouds but not effectively for optically thick clouds. However, the adjustment methods reduce computational errors over a wide range, from optically thin to thick clouds. With the adjustment methods, the errors resulting from neglecting cloud longwave scattering are reduced to less than 2 W m−2 for the upward irradiance at the top of the atmosphere and less than 0.5 W m−2 for the surface downward irradiance. The adjustment schemes prove to be more accurate and efficient than a four-stream approximation that explicitly accounts for multiple scattering. The neglect of cloud longwave scattering results in an underestimate of the surface downward irradiance (cooling effect), but the errors are almost eliminated by the adjustment methods (warming effect).
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Youn, D., K. O. Patten, J. T. Lin, and D. J. Wuebbles. "Explicit calculation of indirect global warming potentials for halons using atmospheric models." Atmospheric Chemistry and Physics 9, no. 22 (November 16, 2009): 8719–33. http://dx.doi.org/10.5194/acp-9-8719-2009.
Повний текст джерелаАнотація:
Abstract. The concept of Global Warming Potentials (GWPs) has been extensively used in policy consideration as a relative index for comparing the climate impact of an emitted greenhouse gas (GHG), relative to carbon dioxide with equal mass emissions. Ozone depletion due to emission of chlorinated or brominated halocarbons leads to cooling of the climate system in the opposite direction to the direct warming contribution by halocarbons as GHGs. This cooling is a key indirect effect of the halocarbons on climatic radiative forcing, which is accounted for by indirect GWPs. With respect to climate, it is critical to understand net influences considering direct warming and indirect cooling effects especially for Halons due to the greater ozone-depleting efficiency of bromine over chlorine. Until now, the indirect GWPs have been calculated using a parameterized approach based on the concept of Equivalent Effective Stratospheric Chlorine (EESC) and the observed ozone depletion over the last few decades. As a step towards obtaining indirect GWPs through a more robust approach, we use atmospheric models to explicitly calculate the indirect GWPs of Halon-1211 and Halon-1301 for a 100-year time horizon. State-of-the-art global chemistry-transport models (CTMs) were used as the computational tools to derive more realistic ozone depletion changes caused by an added pulse emission of the two major Halons at the surface. The radiative forcings on climate from the ozone changes have been calculated for indirect GWPs using an atmospheric radiative transfer model (RTM). The simulated temporal variations of global average total column Halons after a pulse perturbation follow an exponential decay with an e-folding time which is consistent with the expected chemical lifetimes of the Halons. Our calculated indirect GWPs for the two Halons are much smaller than those from past studies but are within a single standard deviation of WMO (2007) values and the direct GWP values derived agree with the published values. Our model-based assessment of the Halon indirect GWPs thus confirms the significant importance of indirect effects on climate.
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Aires, Filipe, Frédéric Bernardo, Héléne Brogniez, and Catherine Prigent. "An Innovative Calibration Method for the Inversion of Satellite Observations." Journal of Applied Meteorology and Climatology 49, no. 12 (December 1, 2010): 2458–73. http://dx.doi.org/10.1175/2010jamc2435.1.
Повний текст джерелаАнотація:
Abstract Retrieval schemes often use two important components: 1) a radiative transfer model (RTM) inside the retrieval procedure or to construct the learning dataset for the training of the statistical retrieval algorithms and 2) a numerical weather prediction (NWP) model to provide a first guess or, again, to construct a learning dataset. This is particularly true in operational centers. As a consequence, any physical retrieval or similar method is limited by inaccuracies in the RTM and NWP models on which it is based. In this paper, a method for partially compensating for these errors as part of the sensor calibration is presented and evaluated. In general, RTM/NWP errors are minimized as best as possible prior to the training of the retrieval method, and then tolerated. The proposed method reduces these unknown and generally nonlinear residual errors by training a separate preprocessing neural network (NN) to produce calibrated radiances from real satellite data that approximate those radiances produced by the “flawed” NWP and RTM models. The final “compensated/flawed” retrieval assures better internal consistency of the retrieval procedure and then produces more accurate results. To the authors’ knowledge, this type of NN model has not been used yet for this purpose. The calibration approach is illustrated here on one particular application: the retrieval of atmospheric water vapor from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and the Humidity Sounder for Brazil (HSB) measurements for nonprecipitating scenes, over land and ocean. Before being inverted, the real observations are “projected” into the space of the RTM simulation space from which the retrieval is designed. Validation of results is performed with radiosonde measurements and NWP analysis departures. This study shows that the NN calibration of the AMSR-E/HSB observations improves water vapor inversion, over ocean and land, for both clear and cloudy situations. The NN calibration is efficient and very general, being applicable to a large variety of problems. The nonlinearity of the NN allows for the calibration procedure to be state dependent and adaptable to specific cases (e.g., the same correction will not be applied to medium-range measurement and to extreme conditions). Its multivariate nature allows for a full exploitation of the complex correlation structure among the instrument channels, making the calibration of each single channel more robust. The procedure would make it possible to project the satellite observations in a reference observational space defined by radiosonde measurements, RTM simulations, or other instrument observational space.
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Aebi, Christine, Julian Gröbner, Stelios Kazadzis, Laurent Vuilleumier, Antonis Gkikas, and Niklaus Kämpfer. "Estimation of cloud optical thickness, single scattering albedo and effective droplet radius using a shortwave radiative closure study in Payerne." Atmospheric Measurement Techniques 13, no. 2 (February 26, 2020): 907–23. http://dx.doi.org/10.5194/amt-13-907-2020.
Повний текст джерелаАнотація:
Abstract. We have used a method based on ground-based solar radiation measurements and radiative transfer models (RTMs) in order to estimate the following cloud optical properties: cloud optical thickness (COT), cloud single scattering albedo (SSAc) and effective droplet radius (reff). The method is based on the minimisation of the difference between modelled and measured downward shortwave radiation (DSR). The optical properties are estimated for more than 3000 stratus–altostratus (St–As) and 206 cirrus–cirrostratus (Ci–Cs) measurements during 2013–2017, at the Baseline Surface Radiation Network (BSRN) station in Payerne, Switzerland. The RTM libRadtran is used to simulate the total DSR as well as its direct and diffuse components. The model inputs of additional atmospheric parameters are either ground- or satellite-based measurements. The cloud cases are identified by the use of an all-sky cloud camera. For the low- to mid-level cloud class St–As, 95 % of the estimated cloud optical thickness values using total DSR measurements in combination with a RTM, herein abbreviated as COTDSR, are between 12 and 92 with a geometric mean and standard deviation of 33.8 and 1.7, respectively. The comparison of these COTDSR values with COTBarnard values retrieved from an independent empirical equation results in a mean difference of -1.2±2.7 and is thus within the method uncertainty. However, there is a larger mean difference of around 18 between COTDSR and COT values derived from MODIS level-2 (L2), Collection 6.1 (C6.1) data (COTMODIS). The estimated reff (from liquid water path and COTDSR) for St–As are between 2 and 20 µm. For the high-level cloud class Ci–Cs, COTDSR is derived considering the direct radiation, and 95 % of the COTDSR values are between 0.32 and 1.40. For Ci–Cs, 95 % of the SSAc values are estimated to be between 0.84 and 0.99 using the diffuse radiation. The COT for Ci–Cs is also estimated from data from precision filter radiometers (PFRs) at various wavelengths (COTPFR). The herein presented method could be applied and validated at other stations with direct and diffuse radiation measurements.
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Verrelst, Jochem, Jorge Vicent, Juan Pablo Rivera-Caicedo, Maria Lumbierres, Pablo Morcillo-Pallarés, and José Moreno. "Global Sensitivity Analysis of Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval from Top-of-Atmosphere Radiance Data." Remote Sensing 11, no. 16 (August 17, 2019): 1923. http://dx.doi.org/10.3390/rs11161923.
Повний текст джерелаАнотація:
Knowledge of key variables driving the top of the atmosphere (TOA) radiance over a vegetated surface is an important step to derive biophysical variables from TOA radiance data, e.g., as observed by an optical satellite. Coupled leaf-canopy-atmosphere Radiative Transfer Models (RTMs) allow linking vegetation variables directly to the at-sensor TOA radiance measured. Global Sensitivity Analysis (GSA) of RTMs enables the computation of the total contribution of each input variable to the output variance. We determined the impacts of the leaf-canopy-atmosphere variables into TOA radiance using the GSA to gain insights into retrievable variables. The leaf and canopy RTM PROSAIL was coupled with the atmospheric RTM MODTRAN5. Because of MODTRAN’s computational burden and GSA’s demand for many simulations, we first developed a surrogate statistical learning model, i.e., an emulator, that allows approximating RTM outputs through a machine learning algorithm with low computation time. A Gaussian process regression (GPR) emulator was used to reproduce lookup tables of TOA radiance as a function of 12 input variables with relative errors of 2.4%. GSA total sensitivity results quantified the driving variables of emulated TOA radiance along the 400–2500 nm spectral range at 15 cm − 1 (between 0.3–9 nm); overall, the vegetation variables play a more dominant role than atmospheric variables. This suggests the possibility to retrieve biophysical variables directly from at-sensor TOA radiance data. Particularly promising are leaf chlorophyll content, leaf water thickness and leaf area index, as these variables are the most important drivers in governing TOA radiance outside the water absorption regions. A software framework was developed to facilitate the development of retrieval models from at-sensor TOA radiance data. As a proof of concept, maps of these biophysical variables have been generated for both TOA (L1C) and bottom-of-atmosphere (L2A) Sentinel-2 data by means of a hybrid retrieval scheme, i.e., training GPR retrieval algorithms using the RTM simulations. Obtained maps from L1C vs L2A data are consistent, suggesting that vegetation properties can be directly retrieved from TOA radiance data given a cloud-free sky, thus without the need of an atmospheric correction.
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Morcillo-Pallarés, Pablo, Juan Pablo Rivera-Caicedo, Santiago Belda, Charlotte De Grave, Helena Burriel, Jose Moreno, and Jochem Verrelst. "Quantifying the Robustness of Vegetation Indices through Global Sensitivity Analysis of Homogeneous and Forest Leaf-Canopy Radiative Transfer Models." Remote Sensing 11, no. 20 (October 18, 2019): 2418. http://dx.doi.org/10.3390/rs11202418.
Повний текст джерелаАнотація:
Vegetation indices (VIs) are widely used in optical remote sensing to estimate biophysical variables of vegetated surfaces. With the advent of spectroscopy technology, spectral bands can be combined in numerous ways to extract the desired information. This resulted in a plethora of proposed indices, designed for a diversity of applications and research purposes. However, it is not always clear whether they are sensitive to the variable of interest while at the same time, responding insensitive to confounding factors. Hence, to be able to quantify the robustness of VIs, a systematic evaluation is needed, thereby introducing a widest possible variety of biochemical and structural heterogeneity. Such exercise can be achieved with coupled leaf and canopy radiative transfer models (RTMs), whereby input variables can virtually simulate any vegetation scenario. With the intention of evaluating multiple VIs in an efficient way, this led us to the development of a global sensitivity analysis (GSA) toolbox dedicated to the analysis of VIs on their sensitivity towards RTM input variables. We identified VIs that are designed to be sensitive towards leaf chlorophyll content (LCC), leaf water content (LWC) and leaf area index (LAI) for common sensors of terrestrial Earth observation satellites: Landsat 8, MODIS, Sentinel-2, Sentinel-3 and the upcoming imaging spectrometer mission EnMAP. The coupled RTMs PROSAIL and PROINFORM were used for simulations of homogeneous and forest canopies respectively. GSA total sensitivity results suggest that LCC-sensitive indices respond most robust: for the great majority of scenarios, chlorophyll a + b content (Cab) drives between 75% and 82% of the indices’ variability. LWC-sensitive indices were most affected by confounding variables such as Cab and LAI, although the equivalent water thickness (Cw) can drive between 25% and 50% of the indices’ variability. Conversely, the majority of LAI-sensitive indices are not only sensitive to LAI but rather to a mixture of structural and biochemical variables.
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Iacovazzi, Robbie, Lin Lin, Ninghai Sun, and Quanhua Liu. "NOAA Operational Microwave Sounding Radiometer Data Quality Monitoring and Anomaly Assessment Using COSMIC GNSS Radio-Occultation Soundings." Remote Sensing 12, no. 5 (March 4, 2020): 828. http://dx.doi.org/10.3390/rs12050828.
Повний текст джерелаАнотація:
National Oceanic and Atmospheric Administration (NOAA) operational Advanced Technology Microwave Sounder (ATMS) and Advanced Microwave Sounding Unit-A (AMSU-A) data used in numerical weather prediction and climate analysis are essential to protect life and property and maintain safe and efficient commerce. Routine data quality monitoring and anomaly assessment is important to sustain data effectiveness. One valuable parameter used to monitor microwave sounder data quality is the antenna temperature (Ta) difference (O-B) computed between direct instrument Ta measurements and forward radiative transfer model (RTM) brightness temperature (Tb) simulations. This requires microwave radiometer data to be collocated with atmospheric temperature and moisture sounding profiles, so that representative boundary conditions are used to produce the RTM-simulated Tb values. In this study, Constellation Observing System for Meteorology, Ionosphere, and Climate/Formosa Satellite Mission 3 (COSMIC) Global Navigation Satellite System (GNSS) Radio Occultation (RO) soundings over the ocean and equatorward of 60° latitude are used as input to the Community RTM (CRTM) to generate simulated NOAA-18, NOAA-19, Metop-A, and Metop-B AMSU-A and S-NPP and NOAA-20 ATMS Tb values. These simulated Tb values, together with observed Ta values that are nearly simultaneous in space and time, are used to compute Ta O-B statistics on monthly time scales for each instrument. In addition, the CRTM-simulated Tb values based on the COSMIC GNSS RO soundings can be used as a transfer standard to inter-compare Ta values from different microwave radiometer makes and models that have the same bands. For example, monthly Ta O-B statistics for NOAA-18 AMSU-A Channels 4–12 and NOAA-20 ATMS Channels 5–13 can be differenced to estimate the “double-difference” Ta biases between these two instruments for the corresponding frequency bands. This study reveals that the GNSS RO soundings are critical to monitoring and trending individual instrument O-B Ta biases and inter-instrument “double-difference” Ta biases and also to estimate impacts of some sensor anomalies on instrument Ta values.
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Reyes-Muñoz, Pablo, Luca Pipia, Matías Salinero-Delgado, Santiago Belda, Katja Berger, José Estévez, Miguel Morata, Juan Pablo Rivera-Caicedo, and Jochem Verrelst. "Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine." Remote Sensing 14, no. 6 (March 10, 2022): 1347. http://dx.doi.org/10.3390/rs14061347.
Повний текст джерелаАнотація:
Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SCOPE and the atmospheric RTM 6SV. The retrieval models, named to S3-TOA-GPR-1.0, were directly implemented in Google Earth Engine (GEE) to enable the quantification of the traits from TOA data as acquired from the S3 Ocean and Land Colour Instrument (OLCI) sensor. Following good to high theoretical validation results with normalized root mean square error (NRMSE) ranging from 5% (FAPAR) to 19% (LAI), a three fold evaluation approach over diverse sites and land cover types was pursued: (1) temporal comparison against LAI and FAPAR products obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) for the time window 2016–2020, (2) spatial difference mapping with Copernicus Global Land Service (CGLS) estimates, and (3) direct validation using interpolated in situ data from the VALERI network. For all three approaches, promising results were achieved. Selected sites demonstrated coherent seasonal patterns compared to LAI and FAPAR MODIS products, with differences between spatially averaged temporal patterns of only 6.59%. In respect of the spatial mapping comparison, estimates provided by the S3-TOA-GPR-1.0 models indicated highest consistency with FVC and FAPAR CGLS products. Moreover, the direct validation of our S3-TOA-GPR-1.0 models against VALERI estimates indicated good retrieval performance for LAI, FAPAR and FVC. We conclude that our retrieval workflow of spatiotemporal S3 TOA data processing into GEE opens the path towards global monitoring of fundamental vegetation traits, accessible to the whole research community.
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Markowicz, Krzysztof M., Iwona S. Stachlewska, Olga Zawadzka-Manko, Dongxiang Wang, Wojciech Kumala, Michal T. Chilinski, Przemyslaw Makuch, et al. "A Decade of Poland-AOD Aerosol Research Network Observations." Atmosphere 12, no. 12 (November 27, 2021): 1583. http://dx.doi.org/10.3390/atmos12121583.
Повний текст джерелаАнотація:
The Poland-AOD aerosol research network was established in 2011 to improve aerosol–climate interaction knowledge and provide a real-time and historical, comprehensive, and quantitative database for the aerosol optical properties distribution over Poland. The network consists of research institutions and private owners operating 10 measurement stations and an organization responsible for aerosol model transport simulations. Poland-AOD collaboration provides observations of spectral aerosol optical depth (AOD), Ångstrom Exponent (AE), incoming shortwave (SW) and longwave (LW) radiation fluxes, vertical profiles of aerosol optical properties and surface aerosol scattering and absorption coefficient, as well as microphysical particle properties. Based on the radiative transfer model (RTM), the aerosol radiative forcing (ARF) and the heating rate are simulated. In addition, results from GEM-AQ and WRF-Chem models (e.g., aerosol mass mixing ratio and optical properties for several particle chemical components), and HYSPLIT back-trajectories are used to interpret the results of observation and to describe the 3D aerosol optical properties distribution. Results of Poland-AOD research indicate progressive improvement of air quality and at mospheric turbidity during the last decade. The AOD was reduced by about 0.02/10 yr (at 550 nm), which corresponds to positive trends in ARF. The estimated clear-sky ARF trend is 0.34 W/m2/10 yr and 0.68 W/m2/10 yr, respectively, at TOA and at Earth’s surface. Therefore, reduction in aerosol load observed in Poland can significantly contribute to climate warming.
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Efremenko, Dmitry S. "Discrete Ordinate Radiative Transfer Model With the Neural Network Based Eigenvalue Solver: proof Of Concept." Light & Engineering, no. 01-2021 (February 2021): 56–62. http://dx.doi.org/10.33383/2020-075.
Повний текст джерелаАнотація:
Artificial neural networks are attracting increasing attention in various applications. They can be used as ‘universal approximations’, which substitute computationally expensive algorithms by relatively simple sequences of functions, which simulate a reaction of a set of neurons to the incoming signal. In particular, neural networks have proved to be efficient for parameterization of the computationally expensive radiative transfer models (RTMs) in atmospheric remote sensing. Although a direct substitution of RTMs by neural networks can lead to the multiple performance enhancements, such an approach has certain drawbacks, such as loss of generality, robustness issues, etc. In this regard, the neural network is usually trained for a specific application, predefined atmospheric scenarios and a given spectrometer. In this paper a new concept of neural-network based RTMs is examined, in which the neural network substitutes not the whole RTM but rather a part of it (the eigenvalue solver), thereby reducing the computational time while maintaining its generality. The explicit dependencies on geometry of observation and optical thickness of the medium are excluded from training. It is shown that although the speedup factor due to this approach is modest (around 3 times against 103 speed up factor of other approaches reported in recent papers), the resulting neural network is flexible and easy to train. It can be used for arbitrary number of atmospheric layers. Moreover, this approach can be used in conjunction with any RTMs based on the discrete ordinate method. The neural network is applied for simulations of the radiances at the top of the atmosphere in the Huggins band.
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Croci, Michele, Giorgio Impollonia, Andrea Marcone, Giulia Antonucci, Tommaso Letterio, Michele Colauzzi, Marco Vignudelli, Francesca Ventura, Stefano Anconelli, and Stefano Amaducci. "RTM Inversion through Predictive Equations for Multi-Crop LAI Retrieval Using Sentinel-2 Images." Agronomy 12, no. 11 (November 13, 2022): 2835. http://dx.doi.org/10.3390/agronomy12112835.
Повний текст джерелаАнотація:
Near-real-time, high-spatial-resolution leaf area index (LAI) maps would enable producers to monitor crop health and growth status, improving agricultural practices such as fertiliser and water management. LAI retrieval methods are numerous and can be divided into statistical and physically based methods. While statistical methods are generally subject to high site-specificity but possess high ease of implementation and use, physically based methods are more transferable, albeit more complex to use in operational settings. In addition, statistical methods need a large amount of data for calibration and subsequent validation, and this is only seldom feasible. Techniques based on predictive equations (PEphysical) represent a viable alternative, allowing the partial combination of statistical and physical methods merits while minimising their shortcomings. In this paper, predictive equation-based techniques were compared with four other methods: two radiative transfer model (RTM) inversion methods, one based on neural network (NNET) and one based on a look-up table (LUT), and two empirical methods (one using empirical models based on vegetation indices and in situ data and one based on empirical models found in the scientific literature). The methods were chosen based on common use. To evaluate the performance of the studied methods, the coefficient of determination (R2), root mean square error (RMSE), and normalised root mean square error (nRMSE, %) between the estimates and in situ LAI measurements were reported. The best PEphysical results, achieved by the OSAVI index (RMSE = 0.84 m2 m−2), provided better performance for LAI recovery than the NNET-based RTM inversions (0.86 m2 m−2) or the estimates made by LUT (0.94 m2 m−2). Furthermore, the best PEphysical produced accuracies comparable to the best empirical model (RMSE = 0.71 m2 m−2), calibrated through in situ data, and similar to the best literature model (RMSE = 0.76 m2 m−2). These results indicated that PEphysical can be used to recover LAI with transferability comparable to literature models.
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del Águila, Ana, Dmitry Efremenko, Víctor Molina García, and Jian Xu. "Analysis of Two Dimensionality Reduction Techniques for Fast Simulation of the Spectral Radiances in the Hartley-Huggins Band." Atmosphere 10, no. 3 (March 16, 2019): 142. http://dx.doi.org/10.3390/atmos10030142.
Повний текст джерелаАнотація:
The new generation of atmospheric composition sensors such as TROPOMI is capable of providing spectra of high spatial and spectral resolution. To process this vast amount of spectral information, fast radiative transfer models (RTMs) are required. In this regard, we analyzed the efficiency of two acceleration techniques based on the principal component analysis (PCA) for simulating the Hartley-Huggins band spectra. In the first one, the PCA is used to map the data set of optical properties of the atmosphere to a lower-dimensional subspace, in which the correction function for an approximate but fast RTM is derived. The second technique is based on the dimensionality reduction of the data set of spectral radiances. Once the empirical orthogonal functions are found, the whole spectrum can be reconstructed by performing radiative transfer computations only for a specific subset of spectral points. We considered a clear-sky atmosphere where the optical properties are defined by Rayleigh scattering and trace gas absorption. Clouds can be integrated into the model as Lambertian reflectors. High computational performance is achieved by combining both techniques without losing accuracy. We found that for the Hartley-Huggins band, the combined use of these techniques yields an accuracy better than 0.05% while the speedup factor is about 20. This innovative combination of both PCA-based techniques can be applied in future works as an efficient approach for simulating the spectral radiances in other spectral regions.
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Labdon, Aaron, Stefan Kraus, Claire L. Davies, Alexander Kreplin, Jacques Kluska, Tim J. Harries, John D. Monnier, et al. "Dusty disk winds at the sublimation rim of the highly inclined, low mass young stellar object SU Aurigae." Astronomy & Astrophysics 627 (June 27, 2019): A36. http://dx.doi.org/10.1051/0004-6361/201935331.
Повний текст джерелаАнотація:
Context. T Tauri stars are low-mass young stars whose disks provide the setting for planet formation. Despite this, their structure is poorly understood. We present new infrared interferometric observations of the SU Aurigae circumstellar environment that offer resolution that is three times higher and a better baseline position angle coverage than previous observations. Aims. We aim to investigate the characteristics of the circumstellar material around SU Aur, constrain the disk geometry, composition and inner dust rim structure. Methods. The CHARA array offers unique opportunities for long baseline observations, with baselines up to 331 m. Using the CLIMB three-telescope combiner in the K-band allows us to measure visibilities as well as closure phase. We undertook image reconstruction for model-independent analysis, and fitted geometric models such as Gaussian and ring distributions. Additionally, the fitting of radiative transfer models constrain the physical parameters of the disk. For the first time, a dusty disk wind is introduced to the radiative transfer code TORUS to model protoplanetary disks. Our implementation is motivated by theoretical models of dusty disk winds, where magnetic field lines drive dust above the disk plane close to the sublimation zone. Results. Image reconstruction reveals an inclined disk with slight asymmetry along its minor-axis, likely due to inclination effects obscuring the inner disk rim through absorption of incident star light on the near-side and thermal re-emission and scattering of the far-side. Geometric modelling of a skewed ring finds the inner rim at 0.17 ± 0.02 au with an inclination of 50.9 ± 1.0° and minor axis position angle 60.8 ± 1.2°. Radiative transfer modelling shows a flared disk with an inner radius at 0.18 au which implies a grain size of 0.4 μm assuming astronomical silicates and a scale height of 15.0 at 100 au. Among the tested radiative transfer models, only the dusty disk wind successfully accounts for the K-band excess by introducing dust above the mid-plane.
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Alahmadi, Reham A., Jawad Raza, Tahir Mushtaq, Shaimaa A. M. Abdelmohsen, Mohammad R. Gorji, and Ahmed M. Hassan. "Optimization of MHD Flow of Radiative Micropolar Nanofluid in a Channel by RSM: Sensitivity Analysis." Mathematics 11, no. 4 (February 13, 2023): 939. http://dx.doi.org/10.3390/math11040939.
Повний текст джерелаАнотація:
These days, heat transfer plays a significant role in the fields of engineering and energy, particularly in the biological sciences. Ordinary fluid is inadequate to transfer heat in an efficient manner, therefore, several models were considered for the betterment of heat transfer. One of the most prominent models is a single-phase nanofluid model. The present study is devoted to solving the problem of micropolar fluid with a single-phase model in a channel numerically. The governing partial differential equations (PDEs) are converted into nonlinear ordinary differential equations (ODEs) by introducing similarity transformation and then solved numerically by the finite difference method. Response surface methodology (RSM) together with sensitivity analysis are implemented for the optimization analysis. The study reveals that sensitivity of the skin friction coefficient (Cfx) to the Reynolds number (R) and magnetic parameter (M) is positive (directly proportional) and negative (inversely proportional) for the micropolar parameter.
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Schwaerzel, Marc, Claudia Emde, Dominik Brunner, Randulph Morales, Thomas Wagner, Alexis Berne, Brigitte Buchmann, and Gerrit Kuhlmann. "Three-dimensional radiative transfer effects on airborne and ground-based trace gas remote sensing." Atmospheric Measurement Techniques 13, no. 8 (August 14, 2020): 4277–93. http://dx.doi.org/10.5194/amt-13-4277-2020.
Повний текст джерелаАнотація:
Abstract. Air mass factors (AMFs) are used in passive trace gas remote sensing for converting slant column densities (SCDs) to vertical column densities (VCDs). AMFs are traditionally computed with 1D radiative transfer models assuming horizontally homogeneous conditions. However, when observations are made with high spatial resolution in a heterogeneous atmosphere or above a heterogeneous surface, 3D effects may not be negligible. To study the importance of 3D effects on AMFs for different types of trace gas remote sensing, we implemented 1D-layer and 3D-box AMFs into the Monte carlo code for the phYSically correct Tracing of photons In Cloudy atmospheres (MYSTIC), a solver of the libRadtran radiative transfer model (RTM). The 3D-box AMF implementation is fully consistent with 1D-layer AMFs under horizontally homogeneous conditions and agrees very well (<5 % relative error) with 1D-layer AMFs computed by other RTMs for a wide range of scenarios. The 3D-box AMFs make it possible to visualize the 3D spatial distribution of the sensitivity of a trace gas observation, which we demonstrate with two examples. First, we computed 3D-box AMFs for ground-based multi-axis spectrometer (MAX-DOAS) observations for different viewing geometry and aerosol scenarios. The results illustrate how the sensitivity reduces with distance from the instrument and that a non-negligible part of the signal originates from outside the line of sight. Such information is invaluable for interpreting MAX-DOAS observations in heterogeneous environments such as urban areas. Second, 3D-box AMFs were used to generate synthetic nitrogen dioxide (NO2) SCDs for an airborne imaging spectrometer observing the NO2 plume emitted from a tall stack. The plume was imaged under different solar zenith angles and solar azimuth angles. To demonstrate the limitations of classical 1D-layer AMFs, VCDs were then computed assuming horizontal homogeneity. As a result, the imaged NO2 plume was shifted in space, which led to a strong underestimation of the total VCDs in the plume maximum and an underestimation of the integrated line densities that can be used for estimating emissions from NO2 images. The two examples demonstrate the importance of 3D effects for several types of ground-based and airborne remote sensing when the atmosphere cannot be assumed to be horizontally homogeneous, which is typically the case in the vicinity of emission sources or in cities.
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Revilla, Sergio, María Lamelas, Darío Domingo, Juan de la Riva, Raquel Montorio, Antonio Montealegre, and Alberto García-Martín. "Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation—Application to Fuel Type Mapping." Remote Sensing 13, no. 3 (January 20, 2021): 342. http://dx.doi.org/10.3390/rs13030342.
Повний текст джерелаАнотація:
Fuel type is one of the key factors for analyzing the potential of fire ignition and propagation in agricultural and forest environments. The increase of three-dimensional datasets provided by active sensors, such as LiDAR (Light Detection and Ranging), has improved the classification of fuel types through empirical modelling. Empirical methods are site and sensor specific while Radiative Transfer Models (RTM) approaches provide broader universality. The aim of this work is to analyze the suitability of Discrete Anisotropic Radiative Transfer (DART) model to replicate low density small-footprint Airborne Laser Scanning (ALS) measurements and subsequent fuel type classification. Field data measured in 104 plots are used as ground truth to simulate LiDAR response based on the sensor and flight characteristics of low-density ALS data captured by the Spanish National Plan for Aerial Orthophotography (PNOA) in two different dates (2011 and 2016). The accuracy assessment of the DART simulations is performed using Spearman rank correlation coefficients between the simulated metrics and the ALS-PNOA ones. The results show that 32% of the computed metrics overpassed a correlation value of 0.80 between simulated and ALS-PNOA metrics in 2011 and 28% in 2016. The highest correlations were related to high height percentiles, canopy variability metrics as for example standard deviation and Rumple diversity index, reaching correlation values over 0.94. Two metric selection approaches and Support Vector Machine classification method with variants were compared to classify fuel types. The best-fitted classification model, trained with the DART simulated sample and validated with ALS-PNOA data, was obtained using Support Vector Machine method with radial kernel. The overall accuracy of the classification after validation was 88% and 91% for the 2011 and 2016 years, respectively. The use of DART demonstrates its value for simulating generalizable 3D data for fuel type classification providing relevant information for forest managers in fire prevention and extinction.
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Kosmopoulos, Panagiotis G., Stelios Kazadzis, Michael Taylor, Panagiotis I. Raptis, Iphigenia Keramitsoglou, Chris Kiranoudis, and Alkiviadis F. Bais. "Assessment of surface solar irradiance derived from real-time modelling techniques and verification with ground-based measurements." Atmospheric Measurement Techniques 11, no. 2 (February 15, 2018): 907–24. http://dx.doi.org/10.5194/amt-11-907-2018.
Повний текст джерелаАнотація:
Abstract. This study focuses on the assessment of surface solar radiation (SSR) based on operational neural network (NN) and multi-regression function (MRF) modelling techniques that produce instantaneous (in less than 1 min) outputs. Using real-time cloud and aerosol optical properties inputs from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (MSG) satellite and the Copernicus Atmosphere Monitoring Service (CAMS), respectively, these models are capable of calculating SSR in high resolution (1 nm, 0.05∘, 15 min) that can be used for spectrally integrated irradiance maps, databases and various applications related to energy exploitation. The real-time models are validated against ground-based measurements of the Baseline Surface Radiation Network (BSRN) in a temporal range varying from 15 min to monthly means, while a sensitivity analysis of the cloud and aerosol effects on SSR is performed to ensure reliability under different sky and climatological conditions. The simulated outputs, compared to their common training dataset created by the radiative transfer model (RTM) libRadtran, showed median error values in the range −15 to 15 % for the NN that produces spectral irradiances (NNS), 5–6 % underestimation for the integrated NN and close to zero errors for the MRF technique. The verification against BSRN revealed that the real-time calculation uncertainty ranges from −100 to 40 and −20 to 20 W m−2, for the 15 min and monthly mean global horizontal irradiance (GHI) averages, respectively, while the accuracy of the input parameters, in terms of aerosol and cloud optical thickness (AOD and COT), and their impact on GHI, was of the order of 10 % as compared to the ground-based measurements. The proposed system aims to be utilized through studies and real-time applications which are related to solar energy production planning and use.
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Chakhvashvili, Erekle, Bastian Siegmann, Onno Muller, Jochem Verrelst, Juliane Bendig, Thorsten Kraska, and Uwe Rascher. "Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy." Remote Sensing 14, no. 5 (March 3, 2022): 1247. http://dx.doi.org/10.3390/rs14051247.
Повний текст джерелаАнотація:
Mapping crop variables at different growth stages is crucial to inform farmers and plant breeders about the crop status. For mapping purposes, inversion of canopy radiative transfer models (RTMs) is a viable alternative to parametric and non-parametric regression models, which often lack transferability in time and space. Due to the physical nature of RTMs, inversion outputs can be delivered in sound physical units that reflect the underlying processes in the canopy. In this study, we explored the capabilities of the coupled leaf–canopy RTM PROSAIL applied to high-spatial-resolution (0.015 m) multispectral unmanned aerial vehicle (UAV) data to retrieve the leaf chlorophyll content (LCC), leaf area index (LAI) and canopy chlorophyll content (CCC) of sweet and silage maize throughout one growing season. Two different retrieval methods were tested: (i) applying the RTM inversion scheme to mean reflectance data derived from single breeding plots (mean reflectance approach) and (ii) applying the same inversion scheme to an orthomosaic to separately retrieve the target variables for each pixel of the breeding plots (pixel-based approach). For LCC retrieval, soil and shaded pixels were removed by applying simple vegetation index thresholding. Retrieval of LCC from UAV data yielded promising results compared to ground measurements (sweet maize RMSE = 4.92 µg/m2, silage maize RMSE = 3.74 µg/m2) when using the mean reflectance approach. LAI retrieval was more challenging due to the blending of sunlit and shaded pixels present in the UAV data, but worked well at the early developmental stages (sweet maize RMSE = 0.70 m2/m2, silage RMSE = 0.61 m2/m2 across all dates). CCC retrieval significantly benefited from the pixel-based approach compared to the mean reflectance approach (RMSEs decreased from 45.6 to 33.1 µg/m2). We argue that high-resolution UAV imagery is well suited for LCC retrieval, as shadows and background soil can be precisely removed, leaving only green plant pixels for the analysis. As for retrieving LAI, it proved to be challenging for two distinct varieties of maize that were characterized by contrasting canopy geometry.
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Scharringhausen, M., A. C. Aikin, J. P. Burrows, and M. Sinnhuber. "First space-borne measurements of the altitude distribution of mesospheric magnesium species." Atmospheric Chemistry and Physics Discussions 7, no. 2 (April 2, 2007): 4597–656. http://dx.doi.org/10.5194/acpd-7-4597-2007.
Повний текст джерелаАнотація:
Abstract. We present a joint retrieval as well as first results for mesospheric air density and mesospheric Magnesium species (Mg and Mg+) using limb data from the SCIAMACHY instrument on board the European ENVISAT satellite. Metallic species like neutral Mg, ionized Mg+ and others (Fe, Si, Li, etc.) ablate from meteoric dust, enter the gas phase and occur at high altitudes (≥70 km). Emissions from these species are clearly observed in the SCIAMACHY limb measurements. These emissions are used to retrieve total and thermospheric column densities as well as altitude-resolved profiles of metallic species in the altitude range of 70–92 km. In this paper, neutral Magnesium as well as its ionized counterpart Mg+ is considered. These species feature resonance fluorescence in the wavelength range 279 and 285 nm and thus have a rather simple excitation process. A radiative transfer model (RTM) for the mesosphere has been developed and validated. Based on a ray tracing kernel, radiances in a large wavelength range from 240–300 nm covering limb as well as nadir geometry can be calculated. The forward model has been validated and shows good agreement with established models in the given wavelength range and a large altitude range. The RTM has been coupled to a retrieval based on Optimal Estimation. Air density is retrieved from Rayleigh backscatter light. Mesospheric Mg and Mg+ number densities are retrieved from their emission signals observed in the limb scans of SCIAMACHY. Other species like iron, silicon, OH and NO can be investigated in principle with the same algorithm. Based on the retrieval presented here, SCIAMACHY offers the opportunity to investigate mesospheric species on a global scale and with good vertical resolution for the first time.
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Scharringhausen, M., A. C. Aikin, J. P. Burrows, and M. Sinnhuber. "Space-borne measurements of mesospheric magnesium species – a retrieval algorithm and preliminary profiles." Atmospheric Chemistry and Physics 8, no. 7 (April 4, 2008): 1963–83. http://dx.doi.org/10.5194/acp-8-1963-2008.
Повний текст джерелаАнотація:
Abstract. We present a joint retrieval as well as first results for mesospheric air density and mesospheric Magnesium species (Mg and Mg+) using limb data from the SCIAMACHY instrument on board the European ENVISAT satellite.considered. These species feature Metallic species like neutral Mg, ionized Mg+ and others (Fe, Si, Li, etc.) ablate from meteoric dust, enter the gas phase and occur at high altitudes (≥70 km). Emissions from these species are clearly observed in the SCIAMACHY limb measurements. These emissions are used to retrieve total and thermospheric column densities as well as preliminary profiles of metallic species in the altitude range of 70–92 km. In this paper, neutral Magnesium as well as its ionized counterpart Mg+ is considered. These species feature resonance fluorescence in the wavelength range 279 and 285 nm and thus have a rather simple excitation process. A radiative transfer model (RTM) for the mesosphere has been developed and validated. Based on a ray tracing kernel, radiances in a large wavelength range from 240–300 nm covering limb as well as nadir geometry can be calculated. The forward model has been validated and shows good agreement with established models in the given wavelength range and a large altitude range. The RTM has been coupled to a retrieval based on Optimal Estimation. Air density is retrieved from Rayleigh backscattered light. Mesospheric Mg and Mg+ number densities are retrieved from their emission signals observed in the limb scans of SCIAMACHY. Other species like iron, silicon, OH and NO can be investigated in principle with the same algorithm. Based on the retrieval presented here, SCIAMACHY offers the opportunity to investigate mesospheric species on a global scale and with good vertical resolution for the first time.
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Krieger, A., and S. Wolf. "Unbiased Monte Carlo continuum radiative transfer in optically thick regions." Astronomy & Astrophysics 635 (March 2020): A148. http://dx.doi.org/10.1051/0004-6361/201937355.
Повний текст джерелаАнотація:
Radiative transfer describes the propagation of electromagnetic radiation through an interacting medium. This process is often simulated by the use of the Monte Carlo method, which involves the probabilistic determination and tracking of simulated photon packages. In the regime of high optical depths, this approach encounters difficulties since a proper representation of the various physical processes can only be achieved by considering high numbers of simulated photon packages. As a consequence, the demand for computation time rises accordingly and thus practically puts a limit on the optical depth of models that can be simulated. Here we present a method that aims to solve the problem of high optical depths in dusty media, which relies solely on the use of unbiased Monte Carlo radiative transfer. For that end, we identified and precalculated repeatedly occuring and simulated processes, stored their outcome in a multidimensional cumulative distribution function, and immediately replaced the basic Monte Carlo transfer during a simulation by that outcome. During the precalculation, we generated emission spectra as well as deposited energy distributions of photon packages traveling from the center of a sphere to its rim. We carried out a performance test of the method to confirm its validity and gain a boost in computation speed by up to three orders of magnitude. We then applied the method to a simple model of a viscously heated circumstellar disk, and we discuss the necessity of finding a solution for the optical depth problem with regard to a proper temperature calculation. We find that the impact of an incorrect treatment of photon packages in highly optically thick regions extents even to optically thin regions, thus, changing the overall observational appearance of the disk.
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Mendiguren, G., M. Pilar Martín, H. Nieto, J. Pacheco-Labrador, and S. Jurdao. "Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site." Biogeosciences 12, no. 18 (September 29, 2015): 5523–35. http://dx.doi.org/10.5194/bg-12-5523-2015.
Повний текст джерелаАнотація:
Abstract. This study evaluates three different metrics of water content of an herbaceous cover in a Mediterranean wooded grassland (dehesa) ecosystem. Fuel moisture content (FMC), equivalent water thickness (EWT) and canopy water content (CWC) were estimated from proximal sensing and MODIS satellite imagery. Dry matter (Dm) and leaf area index (LAI) connect the three metrics and were also analyzed. Metrics were derived from field sampling of grass cover within a 500 m MODIS pixel. Hand-held hyperspectral measurements and MODIS images were simultaneously acquired and predictive empirical models were parametrized. Two methods of estimating FMC and CWC using different field protocols were tested in order to evaluate the consistency of the metrics and the relationships with the predictive empirical models. In addition, radiative transfer models (RTM) were used to produce estimates of CWC and FMC, which were compared with the empirical ones. Results revealed that, for all metrics spatial variability was significantly lower than temporal. Thus we concluded that experimental design should prioritize sampling frequency rather than sample size. Dm variability was high which demonstrates that a constant annual Dm value should not be used to predict EWT from FMC as other previous studies did. Relative root mean square error (RRMSE) evaluated the performance of nine spectral indices to compute each variable. Visible Atmospherically Resistant Index (VARI) provided the lowest explicative power in all cases. For proximal sensing, Global Environment Monitoring Index (GEMI) showed higher statistical relationships both for FMC (RRMSE = 34.5 %) and EWT (RRMSE = 27.43 %) while Normalized Difference Infrared Index (NDII) and Global Vegetation Monitoring Index (GVMI) for CWC (RRMSE = 30.27 % and 31.58 % respectively). When MODIS data were used, results showed an increase in R2 and Enhanced Vegetation Index (EVI) as the best predictor for FMC (RRMSE = 33.81 %) and CWC (RRMSE = 27.56 %) and GEMI for EWT (RRMSE = 24.6 %). Differences in the viewing geometry of the platforms can explain these differences as the portion of vegetation observed by MODIS is larger than when using proximal sensing including the spectral response from scattered trees and its shadows. CWC was better predicted than the other two water content metrics, probably because CWC depends on LAI, that shows a notable seasonal variation in this ecosystem. Strong statistical relationship was found between empirical models using indices sensible to chlorophyll activity (NDVI or EVI which are not directly related to water content) due to the close relationship between LAI, water content and chlorophyll activity in grassland cover, which is not true for other types of vegetation such as forest or shrubs. The empirical methods tested outperformed FMC and CWC products based on radiative transfer model inversion.
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Shellito, Peter J., Sujay V. Kumar, Joseph A. Santanello, Patricia Lawston-Parker, John D. Bolten, Michael H. Cosh, David D. Bosch, et al. "Assessing the Impact of Soil Layer Depth Specification on the Observability of Modeled Soil Moisture and Brightness Temperature." Journal of Hydrometeorology 21, no. 9 (September 1, 2020): 2041–60. http://dx.doi.org/10.1175/jhm-d-19-0280.1.
Повний текст джерелаАнотація:
AbstractThe utility of hydrologic land surface models (LSMs) can be enhanced by using information from observational platforms, but mismatches between the two are common. This study assesses the degree to which model agreement with observations is affected by two mechanisms in particular: 1) physical incongruities between the support volumes being characterized and 2) inadequate or inconsistent parameterizations of physical processes. The Noah and Noah-MP LSMs by default characterize surface soil moisture (SSM) in the top 10 cm of the soil column. This depth is notably different from the 5-cm (or less) sensing depth of L-band radiometers such as NASA’s Soil Moisture Active Passive (SMAP) satellite mission. These depth inconsistencies are examined by using thinner model layers in the Noah and Noah-MP LSMs and comparing resultant simulations to in situ and SMAP soil moisture. In addition, a forward radiative transfer model (RTM) is used to facilitate direct comparisons of LSM-based and SMAP-based L-band Tb retrievals. Agreement between models and observations is quantified using Kolmogorov–Smirnov distance values, calculated from empirical cumulative distribution functions of SSM and Tb time series. Results show that agreement of SSM and Tb with observations depends primarily on systematic biases, and the sign of those biases depends on the particular subspace being analyzed (SSM or Tb). This study concludes that the role of increased soil layer discretization on simulated soil moisture and Tb is secondary to the influence of component parameterizations, the effects of which dominate systematic differences with observations.
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RAJEEVAN, M. "Model calculations of non-cloud radiative forcing due to anthropogenic sulphate aerosol." MAUSAM 49, no. 1 (December 16, 2021): 45–58. http://dx.doi.org/10.54302/mausam.v49i1.3598.
Повний текст джерелаАнотація:
Anthropogenic sulphate aerosol particles scatter incoming solar radiation thereby perturbing the radiative budget, hence climate. We have used a three dimensional radiative transfer model together with the sulphate concentration fields simulated by two independent chemistry-transport models to calculate the annual cycle of the radiative forcing due to anthropogenic sulphate aerosol. The calculated forcing pattern shows large peaks over the eastern United States, southeast Europe and eastern China. The calculated annual global-mean radiative forcing is -0.50 Wm-2 for Langner and Rodhe (1991) data and -0.49 Wm-2 for Penner et el. [1994 (a&b)] data. The forcing was found to vary with season, with a larger forcing during northern hemispheric summer than winter. Sulphate aerosol also appreciably perturbs the lower tropospheric heating rates over northern hemispheric mid-latitudes. The forcing was also found to be sensitive to the global cloud cover and to the optical properties of the aerosol. The possible sources of the differences in magnitude with previous estimates are discussed. Over northern hemispheric mid-latitudes, the negative radiative forcing due to the direct effect of aerosols appreciably offsets the positive forcing due to increase in greenhouse gases.
A 26-layer radiative-convective model (RCM) was also used to examine the equilibrium temperature profiles due to sulphate aerosols and increase in greenhouse gases. It was found that the effect of sulphate aerosols is the cooling of surface-troposphere system. Sulphate aerosols reduce the tropospheric warming and enhance the stratospheric cooling caused by increase in greenhouse gases.
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Ali, Abebe Mohammed, Roshanak Darvishzadeh, Andrew Skidmore, Marco Heurich, Marc Paganini, Uta Heiden, and Sander Mücher. "Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes." Remote Sensing 12, no. 11 (June 1, 2020): 1788. http://dx.doi.org/10.3390/rs12111788.
Повний текст джерелаАнотація:
Accurate measurement of canopy chlorophyll content (CCC) is essential for the understanding of terrestrial ecosystem dynamics through monitoring and evaluating properties such as carbon and water flux, productivity, light use efficiency as well as nutritional and environmental stresses. Information on the amount and distribution of CCC helps to assess and report biodiversity indicators related to ecosystem processes and functional aspects. Therefore, measuring CCC continuously and globally from earth observation data is critical to monitor the status of the biosphere. However, generic and robust methods for regional and global mapping of CCC are not well defined. This study aimed at examining the spatiotemporal consistency and scalability of selected methods for CCC mapping across biomes. Four methods (i.e., radiative transfer models (RTMs) inversion using a look-up table (LUT), the biophysical processor approach integrated into the Sentinel application platform (SNAP toolbox), simple ratio vegetation index (SRVI), and partial least square regression (PLSR)) were evaluated. Similarities and differences among CCC products generated by applying the four methods on actual Sentinel-2 data in four biomes (temperate forest, tropical forest, wetland, and Arctic tundra) were examined by computing statistical measures and spatiotemporal consistency pairwise comparisons. Pairwise comparison of CCC predictions by the selected methods demonstrated strong agreement. The highest correlation (R2 = 0.93, RMSE = 0.4371 g/m2) was obtained between CCC predictions of PROSAIL inversion by LUT and SNAP toolbox approach in a wetland when a single Sentinel-2 image was used. However, when time-series data were used, it was PROSAIL inversion against SRVI (R2 = 0.88, RMSE = 0.19) that showed greatest similarity to the single date predictions (R2 = 0.83, RMSE = 0.17 g/m2) in this biome. Generally, the CCC products obtained using the SNAP toolbox approach resulted in a systematic over/under-estimation of CCC. RTMs inversion by LUT (INFORM and PROSAIL) resulted in a non-biased, spatiotemporally consistent prediction of CCC with a range closer to expectations. Therefore, the RTM inversion using LUT approaches particularly, INFORM for ‘forest’ and PROSAIL for ‘short vegetation’ ecosystems, are recommended for CCC mapping from Sentinel-2 data for worldwide mapping of CCC. Additional validation of the two RTMs with field data of CCC across biomes is required in the future.
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Marquardt Collow, Allison B., Mark A. Miller, Lynne C. Trabachino, Michael P. Jensen, and Meng Wang. "Radiative heating rate profiles over the southeast Atlantic Ocean during the 2016 and 2017 biomass burning seasons." Atmospheric Chemistry and Physics 20, no. 16 (August 28, 2020): 10073–90. http://dx.doi.org/10.5194/acp-20-10073-2020.
Повний текст джерелаАнотація:
Abstract. Marine boundary layer clouds, including the transition from stratocumulus to cumulus, are poorly represented in numerical weather prediction and general circulation models. Further uncertainties in the cloud structure arise in the presence of biomass burning carbonaceous aerosol, as is the case over the southeast Atlantic Ocean, where biomass burning aerosol is transported from the African continent. As the aerosol plume progresses across the southeast Atlantic Ocean, radiative heating within the aerosol layer has the potential to alter the thermodynamic environment and therefore the cloud structure; however, limited work has been done to quantify this along the trajectory of the aerosol plume in the region. The deployment of the first Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF1) in support of the Layered Atlantic Smoke Interactions with Clouds field campaign provided a unique opportunity to collect observations of cloud and aerosol properties during two consecutive biomass burning seasons during July through October of 2016 and 2017 over Ascension Island (7.96∘ S, 14.35∘ W). Using observed profiles of temperature, humidity, and clouds from the field campaign alongside aerosol optical properties from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), as input for the Rapid Radiation Transfer Model (RRTM), profiles of the radiative heating rate due to aerosols and clouds were computed. Radiative heating is also assessed across the southeast Atlantic Ocean using an ensemble of back trajectories from the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Idealized experiments using the RRTM with and without aerosols and a range of values for the single-scattering albedo (SSA) demonstrate that shortwave (SW) heating within the aerosol layer above Ascension Island can locally range between 2 and 8 K d−1 depending on the aerosol optical properties, though impacts of the aerosol can be felt elsewhere in the atmospheric column. When considered under clear conditions, the aerosol has a cooling effect at the TOA, and based on the observed cloud properties at Ascension Island, the cloud albedo is not large enough to overcome this. Shortwave radiative heating due to biomass burning aerosol is not balanced by additional longwave (LW) cooling, and the net radiative impact results in a stabilization of the lower troposphere. However, these results are extremely sensitive to the single-scattering albedo assumptions in models.
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Joly, Lilian, Olivier Coopmann, Vincent Guidard, Thomas Decarpenterie, Nicolas Dumelié, Julien Cousin, Jérémie Burgalat, et al. "The development of the Atmospheric Measurements by Ultra-Light Spectrometer (AMULSE) greenhouse gas profiling system and application for satellite retrieval validation." Atmospheric Measurement Techniques 13, no. 6 (June 12, 2020): 3099–118. http://dx.doi.org/10.5194/amt-13-3099-2020.
Повний текст джерелаАнотація:
Abstract. We report in this paper the development of an embedded ultralight spectrometer (<3 kg) based on tuneable diode laser absorption spectroscopy (with a sampling rate of 24 Hz) in the mid-infrared spectral region. This instrument is dedicated to in situ measurements of the vertical profile concentrations of three main greenhouse gases – carbon dioxide (CO2), methane (CH4) and water vapour (H2O) – via standard weather and tethered balloons. The plug and play instrument is compact, robust, cost-effective, and autonomous. The instrument also has low power consumption and is non-intrusive. It was first calibrated during an in situ experiment on an ICOS (Integrated Carbon Observation System) site for several days, then used in two experiments with several balloon flights of up to 30 km altitude in the Reims region of France in 2017–2018 in collaboration with Météo-France CNRM (Centre National de Recherches Météorologiques). This paper shows the valuable interest of the data measured by the AMULSE (Atmospheric Measurements by Ultra-Light Spectrometer) instrument during the APOGEE (Atmospheric Profiles of Greenhouse Gases) measurement experiment, specifically for the vertical profiles of CO2 and CH4, measurements of which remain very sparse. We have carried out several experiments showing that the measured profiles have several applications: the validation of simulations of infrared satellite observations, evaluating the quality of chemical profiles from chemistry transport models (CTMs) and evaluating the quality of retrieved chemical profiles from the assimilation of infrared satellite observations. The results show that the simulations of infrared satellite observations from IASI (Infrared Atmospheric Sounding Interferometer) and CrIS (Cross-track Infrared Sounder) instruments performed in operational mode for numerical weather prediction (NWP) by the radiative transfer model (RTM) RTTOV (Radiative Transfer for the TIROS Operational Vertical Sounder) are of good quality. We also show that the MOCAGE (Modèle de Chimie Atmosphérique de Grande Echelle) and CAMS (Copernicus Atmospheric Monitoring Service) CTMs modelled ozone profiles fairly accurately and that the CAMS CTM represents the methane in the troposphere well compared to MOCAGE. Finally, the measured in situ ozone profiles allowed us to show the good quality of the retrieved ozone profiles by assimilating ozone-sensitive infrared spectral radiances from the IASI and CrIS.