scholarly journals Application of Sentinel-2A data for pasture biomass monitoring using a physically based radiative transfer model

2018 ◽  
Vol 218 ◽  
pp. 207-220 ◽  
Author(s):  
S.M. Punalekar ◽  
A. Verhoef ◽  
T.L. Quaife ◽  
D. Humphries ◽  
L. Bermingham ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Physically Based ◽  
Biomass Monitoring ◽  
Sentinel 2A

scholarly journals Assessment of Radiative Forcing by Light-Absorbing Particles in Snow from In Situ Observations with Radiative Transfer Modeling

2018 ◽  
Vol 19 (8) ◽  
pp. 1397-1409 ◽  
Author(s):  
S. McKenzie Skiles ◽  
Thomas H. Painter
Keyword(s):  
Time Series ◽  
Radiative Transfer ◽  
Radiative Forcing ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Full Time ◽  
Snow Hydrology ◽  
Physically Based ◽  
Rf Values ◽  
Colorado Rockies

Abstract It is well established that episodic deposition of dust on mountain snow reduces snow albedo and impacts snow hydrology in the western United States, particularly in the Colorado Rockies, which are headwaters for the Colorado River. Until recently the snow observations needed to physically quantify radiative forcing (RF) by dust on snow were lacking, and analysis of impacts used a semiempirical relationship between snow optical properties and observed surface reflectance. Here, we present a physically based daily time series of RF by dust and black carbon (BC) in snow at Senator Beck Basin Study Area, Colorado. Over the 2013 ablation season (March–May), a snow–aerosol radiative transfer model was forced with near daily measured snow property inputs (density, effective grain size, and dust/BC concentrations) and validated with coincidentally measured spectral albedo. Over the measurement period, instantaneous RF by dust and BC in snow ranged from 0.25 to 525 W m−2, with daily averages ranging from 0 to 347 W m−2. Dust dominated particulate mass, accounting for more than 90% of RF. The semiempirical RF values, which constitute the continuous long-term record, compared well to the physically based RF values; over the full time series, daily reported semiempirical RF values were 8 W m−2 higher on average, with a root-mean-square difference of 16 W m−2.

scholarly journals Physically Based Approach for Combined Atmospheric and Topographic Corrections

2019 ◽  
Vol 11 (10) ◽  
pp. 1218 ◽  
Author(s):  
Federico Santini ◽  
Angelo Palombo
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Test Cases ◽  
Remote Sensors ◽  
Sensing Applications ◽  
Physically Based ◽  
Remote Sensing Applications ◽  
The Impact

The enhanced spectral and spatial resolutions of the remote sensors have increased the need for highly performing preprocessing procedures. In this paper, a comprehensive approach, which simultaneously performs atmospheric and topographic corrections and includes second order corrections such as adjacency effects, was presented. The method, developed under the assumption of Lambertian surfaces, is physically based and uses MODTRAN 4 radiative transfer model. The use of MODTRAN 4 for the estimates of the radiative quantities was widely discussed in the paper and the impact on remote sensing applications was shown through a series of test cases.

A Coupled Atmosphere–Ocean Radiative Transfer System Using the Analytic Four-Stream Approximation

2007 ◽  
Vol 64 (10) ◽  
pp. 3681-3694 ◽  
Author(s):  
Wei-Liang Lee ◽  
K. N. Liou
Keyword(s):  
Radiative Transfer ◽  
Coupled Model ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Computationally Efficient ◽  
Reflection And Transmission ◽  
Scattering Coefficients ◽  
Total Transmission ◽  
Physically Based ◽  
Relative Differences

Abstract A coupled atmosphere–ocean radiative transfer model based on the analytic four-stream approximation has been developed. It is shown that this radiation model is computationally efficient and at the same time can achieve acceptable accuracy for flux and heating rate calculations in the atmosphere and the oceans. To take into account the reflection and transmission of the wind-blown air–water interface, a Monte Carlo method has been employed to simulate the traveling of photons and to compute the reflectance and transmittance of direct and diffuse solar fluxes at the ocean surface. For the ocean part, existing bio-optical models, which correlate the concentration of chlorophyll and the absorption and scattering coefficients of phytoplankton and other matters, have been integrated into this coupled model. Comparing to the values computed by more discrete streams illustrates that the relative accuracies of the surface albedo and total transmission in the ocean determined from the present model are generally within 5%, except in cases of the solar zenith angle larger than 80°. Observational data have also been used to validate this model and the results show that the relative differences of downward and upward shortwave fluxes and albedo are within 10% of the observed values. This computationally efficient and physically based radiative transfer model is well suited for consistent flux calculations in a coupled atmosphere–ocean dynamic system.

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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