scholarly journals Development of synthetic GOES-R ABI aerosol products

2014 ◽  
Vol 7 (9) ◽  
pp. 10131-10157 ◽  
Author(s):  
R. M. Hoff ◽  
S. Kondragunta ◽  
P. Ciren ◽  
C. Xu ◽  
H. Zhang ◽  
...  
Keyword(s):  
Simulation Experiment ◽  
Simulated Data ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
User Group ◽  
Display System ◽  
State And Local ◽  
Systems Simulation ◽  
The Web ◽  
Observing Systems

Abstract. An Observing Systems Simulation Experiment (OSSE) for GOES-R Advanced Baseline Imager (ABI) aerosol products has been carried out. The generation of simulated data involves prediction of aerosol chemical composition fields at one-hour resolution and 12 km × 12 km spacing. These data are then fed to a radiative transfer model to simulate the on-orbit radiances that the GOES-R ABI will see in six channels. This allows the ABI aerosol algorithm to be tested to produce products that will be available after launch. In cooperation with a user group of 40+ state and local air quality forecasters, the system has been tested in real-time experiments where the results mimic what the forecasters will see after 2016 when GOES-R launches. Feedback from this group has allowed refinement of the web display system for the ABI aerosol products and has creatively called for new products that were not envisaged by the satellite team.

scholarly journals A Variational Method to Retrieve the Extinction Profile in Liquid Clouds Using Multiple-Field-of-View Lidar

2012 ◽  
Vol 51 (2) ◽  
pp. 350-365 ◽  
Author(s):  
Nicola L. Pounder ◽  
Robin J. Hogan ◽  
Tamás Várnai ◽  
Alessandro Battaglia ◽  
Robert F. Cahalan
Keyword(s):  
Optical Depth ◽  
Vertical Profile ◽  
Global Radiation ◽  
Simulated Data ◽  
Field Of View ◽  
Radiation Budget ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Optimization Schemes ◽  
Multiple Field

AbstractLiquid clouds play a profound role in the global radiation budget, but it is difficult to retrieve their vertical profile remotely. Ordinary narrow-field-of-view (FOV) lidars receive a strong return from such clouds, but the information is limited to the first few optical depths. Wide-angle multiple-FOV lidars can isolate radiation that is scattered multiple times before returning to the instrument, often penetrating much deeper into the cloud than does the single-scattered signal. These returns potentially contain information on the vertical profile of the extinction coefficient but are challenging to interpret because of the lack of a fast radiative transfer model for simulating them. This paper describes a variational algorithm that incorporates a fast forward model that is based on the time-dependent two-stream approximation, and its adjoint. Application of the algorithm to simulated data from a hypothetical airborne three-FOV lidar with a maximum footprint width of 600 m suggests that this approach should be able to retrieve the extinction structure down to an optical depth of around 6 and a total optical depth up to at least 35, depending on the maximum lidar FOV. The convergence behavior of Gauss–Newton and quasi-Newton optimization schemes are compared. Results are then presented from an application of the algorithm to observations of stratocumulus by the eight-FOV airborne Cloud Thickness from Off-Beam Lidar Returns (THOR) lidar. It is demonstrated how the averaging kernel can be used to diagnose the effective vertical resolution of the retrieved profile and, therefore, the depth to which information on the vertical structure can be recovered. This work enables more rigorous exploitation of returns from spaceborne lidar and radar that are subject to multiple scattering than was previously possible.

scholarly journals Evaluation and Intercomparison of Topographic Correction Methods Based on Landsat Images and Simulated Data

2021 ◽  
Vol 13 (20) ◽  
pp. 4120
Author(s):  
Yichuan Ma ◽  
Tao He ◽  
Ainong Li ◽  
Sike Li
Keyword(s):  
Simulated Data ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Topographic Effects ◽  
Landsat Images ◽  
Topographic Correction ◽  
Surface Cover ◽  
Multi Criteria Evaluation ◽  
Pros And Cons ◽  
Simulated Images

Topographic effects in medium and high spatial resolution remote sensing images greatly limit the application of quantitative parameter retrieval and analysis in mountainous areas. Many topographic correction methods have been proposed to reduce such effects. Comparative analyses on topographic correction algorithms have been carried out, some of which drew different or even contradictory conclusions. Performances of these algorithms over different terrain and surface cover conditions remain largely unknown. In this paper, we intercompared ten widely used topographic correction algorithms by adopting multi-criteria evaluation methods using Landsat images under various terrain and surface cover conditions as well as images simulated by a 3D radiative transfer model. Based on comprehensive analysis, we found that the Teillet regression-based models had the overall best performance in terms of topographic effects’ reduction and overcorrection; however, correction bias may be introduced by Teillet regression models when surface reflectance in the uncorrected images do not follow a normal distribution. We recommend including more simulated images for a more in-depth evaluation. We also recommend that the pros and cons of topographic correction methods reported in this paper should be carefully considered for surface parameters retrieval and applications in mountain regions.

scholarly journals CANOPY MODELING OF AQUATIC VEGETATION: CONSTRUCTION OF SUBMERGED VEGETATION INDEX

2018 ◽  
Vol XLII-3 ◽  
pp. 1253-1258
Author(s):  
Z. Ma ◽  
G. Zhou
Keyword(s):  
Vegetation Index ◽  
Aquatic Vegetation ◽  
Spectral Characteristics ◽  
Vegetation Indices ◽  
Simulated Data ◽  
Radiative Transfer Model ◽  
Future Research ◽  
Transfer Model ◽  
Submerged Vegetation ◽  
Terrestrial Vegetation

The unique spectral characteristics of submerged vegetation in wetlands determine that the conventional terrestrial vegetation index cannot be directly employed to species identification and parameter inversion of submerged vegetation. Based on the Aquatic Vegetation Radiative Transfer model (AVRT), this paper attempts to construct an index suitable for submerged vegetation, the model simulated data and a scene of Sentinel-2A image in Taihu Lake, China are utilized for assessing the performance of the newly constructed indices and the existent vegetation indices. The results show that the angle index composed by 525 nm, 555 nm and 670 nm can resist the effects of water columns and is more sensitive to vegetation parameters such as LAI. Furthermore, it makes a well discrimination between submerged vegetation and water bodies in the satellite data. We hope that the new index will provide a theoretical basis for future research.

ESTIMATING THE LIMIT OF BIO-AEROSOL DETECTION WITH PASSIVE INFRARED SPECTROSCOPY

2008 ◽  
Vol 18 (03) ◽  
pp. 701-711
Author(s):  
AGUSTIN IFARRAGUERRI ◽  
AVISHAI BEN-DAVID ◽  
RICHARD G. VANDERBEEK
Keyword(s):  
Turbulence Model ◽  
Hypothesis Test ◽  
Simulated Data ◽  
Roc Curves ◽  
Detection Algorithm ◽  
Radiative Transfer Model ◽  
Spectral Radiance ◽  
Transfer Model ◽  
Infrared Measurements ◽  
Passive Infrared

To investigate the detection limits of biological aerosols using passive infrared measurements, we have developed a computational model that relies on physics-based simulations to generate a statistical sample. The simulation consists of three principal models: an atmospheric turbulence model, a radiative transfer model and a target detection model. The turbulence model is used to generate microscale atmospheric variability. Resulting temperature and density profiles, along with custom aerosol profiles, are used to generate inputs for MODTRAN5, which produces simulated atmospheric spectral radiance. The simulated data is then analyzed by using an optimal detection algorithm and a hypothesis test, resulting in receiver operating characteristic (ROC) curves.

scholarly journals Probability Index of Low Stratus and Fog at Dawn using Dual Geostationary Satellite Observations from COMS and FY-2D near the Korean Peninsula

2019 ◽  
Vol 11 (11) ◽  
pp. 1283
Author(s):  
Jung-Hyun Yang ◽  
Jung-Moon Yoo ◽  
Yong-Sang Choi ◽  
Dong Wu ◽  
Jin-Hee Jeong
Keyword(s):  
Korean Peninsula ◽  
Simulated Data ◽  
Probability Of Detection ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Detection Accuracy ◽  
High Detection Rate ◽  
High Detection ◽  
Meteorological Satellite ◽  
Probability Index

We developed a new remote sensing method for detecting low stratus and fog (LSF) at dawn in terms of probability index (PI) of LSF from simultaneous stereo observations of two geostationary-orbit satellites; the Korean Communication, Ocean, and Meteorological Satellite (COMS; 128.2°E); and the Chinese FengYun satellite (FY-2D; 86.5°E). The algorithm was validated near the Korean Peninsula between the months of April and August from April 2012 to June 2015, by using surface observations at 45 meteorological stations in South Korea. The optical features of LSF were estimated by using satellite retrievals and simulated data from the radiative transfer model. The PI was calculated using the combination of three satellite-observed variables: 1) the reflectance at 0.67 μm (R0.67) from COMS, and 2) the FY-2D R0.67 minus the COMS R0.67 (△R0.67) and 3) the FY-2D-COMS difference in the brightness temperature difference between 3.7 and 11.0 μm (ΔBTD3.7-11). The three variables, adopted from the top three probability of detection (POD) scores for their fog detection thresholds: △R0.67 (0.82) > ΔBTD3.7-11 (0.73) > R0.67 (0.70) > BTD3.7-11 (0.51). The LSF PI for this algorithm was significantly better in the two case studies compared to that using COMS only (i.e., R0.67 or BTD3.7-11), so that this improvement was due to △R0.67 and ΔBTD3.7-11. Overall, PI in the LSF spatial distribution has the merits of a high detection rate, a specific probability display, and a low rate of seasonality and variability in detection accuracy. Therefore, PI would be useful for monitoring LSF in near-real-time, and to further its forecast ability, using next-generation satellites.

Probing clouds in planets with a simple radiative transfer model: the Jupiter case

2012 ◽  
Vol 33 (6) ◽  
pp. 1611-1624 ◽  
Author(s):  
Iñigo Mendikoa ◽  
Santiago Pérez-Hoyos ◽  
Agustín Sánchez-Lavega
Keyword(s):  
Radiative Transfer ◽  
Radiative Transfer Model ◽  
Transfer Model

scholarly journals A Proof-of-Concept Algorithm for the Retrieval of Total Column Amount of Trace Gases in a Multi-Dimensional Atmosphere

2021 ◽  
Vol 13 (2) ◽  
pp. 270
Author(s):  
Adrian Doicu ◽  
Dmitry S. Efremenko ◽  
Thomas Trautmann
Keyword(s):  
Trace Gases ◽  
Three Dimensional ◽  
Principal Component ◽  
Radiative Transfer Model ◽  
Computational Time ◽  
Transfer Model ◽  
Proof Of Concept ◽  
Discrete Ordinate ◽  
Distribution Method ◽  
Total Column

An algorithm for the retrieval of total column amount of trace gases in a multi-dimensional atmosphere is designed. The algorithm uses (i) certain differential radiance models with internal and external closures as inversion models, (ii) the iteratively regularized Gauss–Newton method as a regularization tool, and (iii) the spherical harmonics discrete ordinate method (SHDOM) as linearized radiative transfer model. For efficiency reasons, SHDOM is equipped with a spectral acceleration approach that combines the correlated k-distribution method with the principal component analysis. The algorithm is used to retrieve the total column amount of nitrogen for two- and three-dimensional cloudy scenes. Although for three-dimensional geometries, the computational time is high, the main concepts of the algorithm are correct and the retrieval results are accurate.

scholarly journals Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rehman S. Eon ◽  
Charles M. Bachmann
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Moisture Content ◽  
Layer Thickness ◽  
Soil Moisture Content ◽  
Hyperspectral Imagery ◽  
Barrier Island ◽  
Water Layer ◽  
Radiative Transfer Model ◽  
Transfer Model

AbstractThe advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.

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