Study on optimization of atmospheric correction process based on 6S radiative transfer model

2021 ◽  
Author(s):  
Han Li ◽  
Yueting Chen ◽  
Huajun Feng ◽  
Zhihai Xu ◽  
Qi Li
Keyword(s):  
Radiative Transfer ◽  
Atmospheric Correction ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Correction Process

scholarly journals A Radiative Transfer Model-Based Multi-Layered Regression Learning to Estimate Shadow Map in Hyperspectral Images

2019 ◽  
Vol 1 (3) ◽  
pp. 904-927 ◽  
Author(s):  
Usman A. Zahidi ◽  
Ayan Chatterjee ◽  
Peter W. T. Yuen
Keyword(s):  
Radiative Transfer ◽  
Hyperspectral Image ◽  
Mathematical Formulation ◽  
False Negative ◽  
Atmospheric Correction ◽  
Ground Truth ◽  
Radiative Transfer Model ◽  
Surface Model ◽  
Transfer Model ◽  
Regression Learning

The application of Empirical Line Method (ELM) for hyperspectral Atmospheric Compensation (AC) premises the underlying linear relationship between a material’s reflectance and appearance. ELM solves the Radiative Transfer (RT) equation under specialized constraint by means of in-scene white and black calibration panels. The reflectance of material is invariant to illumination. Exploiting this property, we articulated a mathematical formulation based on the RT model to create cost functions relating variably illuminated regions within a scene. In this paper, we propose multi-layered regression learning-based recovery of radiance components, i.e., total ground-reflected radiance and path radiance from reflectance and radiance images of the scene. These decomposed components represent terms in the RT equation and enable us to relate variable illumination. Therefore, we assume that Hyperspectral Image (HSI) radiance of the scene is provided and AC can be processed on it, preferably with QUick Atmospheric Correction (QUAC) algorithm. QUAC is preferred because it does not account for surface models. The output from the proposed algorithm is an intermediate map of the scene on which our mathematically derived binary and multi-label threshold is applied to classify shadowed and non-shadowed regions. Results from a satellite and airborne NADIR imagery are shown in this paper. Ground truth (GT) is generated by ray-tracing on a LIDAR-based surface model in the form of contour data, of the scene. Comparison of our results with GT implies that our algorithm’s binary classification shadow maps outperform other existing shadow detection algorithms in true positive, which is the detection of shadows when it is in ground truth. It also has the lowest false negative i.e., detecting non-shadowed region as shadowed, compared to existing algorithms.

scholarly journals The remote sensing of radiative forcing by light-absorbing particles (LAPs) in seasonal snow over northeastern China

2019 ◽  
Vol 19 (15) ◽  
pp. 9949-9968 ◽  
Author(s):  
Wei Pu ◽  
Jiecan Cui ◽  
Tenglong Shi ◽  
Xuelei Zhang ◽  
Cenlin He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Radiative Forcing ◽  
Atmospheric Correction ◽  
Northeastern China ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Spatial Variance ◽  
Snow Albedo ◽  
Sky Conditions

Abstract. Light-absorbing particles (LAPs) deposited on snow can decrease snow albedo and affect climate through snow-albedo radiative forcing. In this study, we use MODIS observations combined with a snow-albedo model (SNICAR – Snow, Ice, and Aerosol Radiative) and a radiative transfer model (SBDART – Santa Barbara DISORT Atmospheric Radiative Transfer) to retrieve the instantaneous spectrally integrated radiative forcing at the surface by LAPs in snow (RFMODISLAPs) under clear-sky conditions at the time of MODIS Aqua overpass across northeastern China (NEC) in January–February from 2003 to 2017. RFMODISLAPs presents distinct spatial variability, with the minimum (22.3 W m−2) in western NEC and the maximum (64.6 W m−2) near industrial areas in central NEC. The regional mean RFMODISLAPs is ∼45.1±6.8 W m−2 in NEC. The positive (negative) uncertainties of retrieved RFMODISLAPs due to atmospheric correction range from 14 % to 57 % (−14 % to −47 %), and the uncertainty value basically decreases with the increased RFMODISLAPs. We attribute the variations of radiative forcing based on remote sensing and find that the spatial variance of RFMODISLAPs in NEC is 74.6 % due to LAPs and 21.2 % and 4.2 % due to snow grain size and solar zenith angle. Furthermore, based on multiple linear regression, the BC dry and wet deposition and snowfall could explain 84 % of the spatial variance of LAP contents, which confirms the reasonability of the spatial patterns of retrieved RFMODISLAPs in NEC. We validate RFMODISLAPs using in situ radiative forcing estimates. We find that the biases in RFMODISLAPs are negatively correlated with LAP concentrations and range from ∼5 % to ∼350 % in NEC.

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