scholarly journals Investigation of the effect of surface heterogeneity and topography on the radiation environment of Palmer Station, Antarctica, with a hybrid 3-D radiative transfer model

1998 ◽  
Vol 103 (D6) ◽  
pp. 6161-6176 ◽  
Author(s):  
Paul Ricchiazzi ◽  
Catherine Gautier
Keyword(s):  
Radiative Transfer ◽  
Surface Heterogeneity ◽  
Radiation Environment ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Palmer Station ◽  
Effect Of Surface

scholarly journals On the Radiative Transfer Model for Soil Moisture across Space, Time and Hydro-Climates

2020 ◽  
Vol 12 (16) ◽  
pp. 2645
Author(s):  
Maheshwari Neelam ◽  
Binayak P. Mohanty
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Surface Temperature ◽  
Land Surface ◽  
Surface Heterogeneity ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Land Surface Heterogeneity ◽  
Vegetation Characteristics ◽  
Physical Variables

A framework is proposed for understanding the efficacy of the microwave radiative transfer model (RTM) of soil moisture with different support scales, seasonality (time), hydroclimates, and aggregation (scaling) methods. In this paper, the sensitivity of brightness temperature TB (H- and V-polarization) to physical variables (soil moisture, soil texture, surface roughness, surface temperature, and vegetation characteristics) is studied. Our results indicate that the sensitivity of brightness temperature (V- or H-polarization) is determined by the upscaling method and heterogeneity observed in the physical variables. Under higher heterogeneity, the TB sensitivity to vegetation and roughness followed a logarithmic function with an increasing support scale, while an exponential function is followed under lower heterogeneity. Surface temperature always followed an exponential function under all conditions. The sensitivity of TB at H- or V- polarization to soil and vegetation characteristics varied with the spatial scale (extent and support) and the amount of biomass observed. Thus, choosing an H- or V-polarization algorithm for soil moisture retrieval is a tradeoff between support scales, and land surface heterogeneity. For largely undisturbed natural environments such as SGP’97 and SMEX04, the sensitivity of TB to variables remains nearly uniform and is not influenced by extent, support scales, or an upscaling method. On the contrary, for anthropogenically-manipulated environments such as SMEX02 and SMAPVEX12, the sensitivity to variables is highly influenced by the distribution of land surface heterogeneity and upscaling methods.

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 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.

scholarly journals Potential of Hyperspectral Thermal Infrared Spaceborne Measurements to Retrieve Ice Cloud Physical Properties: Case Study of IASI and IASI-NG

2020 ◽  
Vol 13 (1) ◽  
pp. 116
Author(s):  
Lucie Leonarski ◽  
Laurent C.-Labonnote ◽  
Mathieu Compiègne ◽  
Jérôme Vidot ◽  
Anthony J. Baran ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Signal To Noise Ratio ◽  
Thermal Infrared ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Water Path ◽  
Ice Cloud ◽  
Ice Water Path ◽  
Ice Water

The present study aims to quantify the potential of hyperspectral thermal infrared sounders such as the Infrared Atmospheric Sounding Interferometer (IASI) and the future IASI next generation (IASI-NG) for retrieving the ice cloud layer altitude and thickness together with the ice water path. We employed the radiative transfer model Radiative Transfer for TOVS (RTTOV) to simulate cloudy radiances using parameterized ice cloud optical properties. The radiances have been computed from an ice cloud profile database coming from global operational short-range forecasts at the European Center for Medium-range Weather Forecasts (ECMWF) which encloses the normal conditions, typical variability, and extremes of the atmospheric properties over one year (Eresmaa and McNally (2014)). We performed an information content analysis based on Shannon’s formalism to determine the amount and spectral distribution of the information about ice cloud properties. Based on this analysis, a retrieval algorithm has been developed and tested on the profile database. We considered the signal-to-noise ratio of each specific instrument and the non-retrieved atmospheric and surface parameter errors. This study brings evidence that the observing system provides information on the ice water path (IWP) as well as on the layer altitude and thickness with a convergence rate up to 95% and expected errors that decrease with cloud opacity until the signal saturation is reached (satisfying retrievals are achieved for clouds whose IWP is between about 1 and 300 g/m2).

scholarly journals Fast Hyper-Spectral Radiative Transfer Model Based on the Double Cluster Low-Streams Regression Method

2021 ◽  
Vol 13 (3) ◽  
pp. 434
Author(s):  
Ana del Águila ◽  
Dmitry S. Efremenko
Keyword(s):  
Radiative Transfer ◽  
Single Scattering ◽  
Atmospheric Composition ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Absorption Bands ◽  
Acceleration Techniques ◽  
Scattering Approximation ◽  
Fine Resolution ◽  
Single Scattering Approximation

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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