A New Active/Passive Microwave Radiative Transfer Model for Snow (SMRT) to Foster Inter-Comparisons of Model Components

2018 ◽  
Author(s):  
Ghislain Picard ◽  
Melody Sandells ◽  
Henning Lowe
Keyword(s):  
Radiative Transfer ◽  
Passive Microwave ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Model Components

Towards improving radiometry-derived snow depths with SNOWPACK and SMRT

2020 ◽  
Author(s):  
Robbie Mallett ◽  
Julienne Stroeve ◽  
Michel Tsamados ◽  
Glen Liston
Keyword(s):  
Radiative Transfer ◽  
Sea Ice ◽  
Model Development ◽  
Light Flux ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Gradient Ratio ◽  
Strong Control

<p>The depth of overlying snow on sea ice exerts a strong control on atmosphere-ocean heat and light flux and introduces major uncertainties in the remote sensing of sea ice thickness. Satellite-mounted microwave radiometers have enabled retrieval of snow depths over first year ice, but such retrievals are subject to a wide margin of error due to spatial variation in snow stratigraphy and roughness.</p><p>Here we model the microwave signature of snow on sea ice using a recently released sea ice variant of the snowpack evolution model, SNOWPACK (Wever et al., 2020). By advecting parcels of sea ice using ice motion vectors and exposing them to the relevant atmospheric forcing using ERA5 reanalysis, we model the accumulation of snow and the development of snowpack stratigraphy.</p><p>We then pass these modelled snowpacks to the Snow Microwave Radiative Transfer model (Picard et al., 2018) to estimate their microwave emission characteristics. By using relationships from the literature relating the ratios of the 37GHz and 19GHz channels, we calculate whether the traditional “gradient ratio” method (Markus and Cavalieri, 1998) over- or underestimates the depth of snow at a particular point based on our modelling. We then adjust the observed gradient ratio based on the model results in an attempt to better characterise snow depths.</p><p> </p><p>References</p><p>Wever, Nander, et al. "Version 1 of a sea ice module for the physics-based, detailed, multi-layer SNOWPACK model." <em>Geoscientific Model Development</em> 13.1 (2020): 99-119.</p><p>Picard, Ghislain, Melody Sandells, and Henning Löwe. "SMRT: An active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1. 0)." <em>Geoscientific Model Development </em>11.7 (2018): 2763-2788.</p><p>Markus, Thorsten, and Donald J. Cavalieri. "Snow depth distribution over sea ice in the Southern Ocean from satellite passive microwave data." <em>Antarctic sea ice: physical processes, interactions and variability </em>74 (1998): 19-39.</p>

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.

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