Abstract
Current atmospheric composition sensors provide a large amount of high spectral resolution data. The accurate processing of this data employs time-consuming line-by-line (LBL) radiative transfer models (RTMs). In this paper, we describe a method to accelerate hyperspectral radiative transfer models based on the clustering of the spectral radiances computed with a low-stream RTM and the regression analysis performed for the low-stream and multi-stream RTMs within each cluster. This approach, which we refer to as the Cluster Low-Streams Regression (CLSR) method, is applied for computing the radiance spectra in the O2 A-band at 760 nm and the CO2 band at 1610 nm for five atmospheric scenarios. The CLSR method is also compared with the principal component analysis (PCA)-based RTM, showing an improvement in terms of accuracy and computational performance over PCA-based RTMs. As low-stream models, the two-stream and the single-scattering RTMs are considered. We show that the error of this approach is modulated by the optical thickness of the atmosphere. Nevertheless, the CLSR method provides a performance enhancement of almost two orders of magnitude compared to the LBL model, while the error of the technique is below 0.1% for both bands.
Highlights
Radiative transfer models (RTM) are a key part of the remote sensing algorithms, which are used to retrieve atmospheric parameters from Earth observation data
For both O2 A- and CO2 bands, (i) the residuals of the single-scattering model with the principal component analysis (PCA)-based method are higher than those corresponding to the two-stream model, while (ii) the residuals of the two-stream and single-scattering models with the Cluster Low-Streams Regression (CLSR) method are comparable
We developed the Cluster Low-Streams Regression (CLSR) method for fast radiative transfer simulations of the O2 A- and CO2 absorption bands
Summary
Radiative transfer models (RTM) are a key part of the remote sensing algorithms, which are used to retrieve atmospheric parameters from Earth observation data. Due to the high spectral variability of the gas absorption coefficient k in the absorption bands, the LBL-approach is very time-consuming because it requires up to several thousands of monochromatic computations per absorption band In this regard, designed hyperspectral RTMs are required. A two-stream radiative transfer model was used as an approximate model, and the dependency of the corresponding correction factor on the optical parameters was modeled by a second-order Taylor expansion about the mean value of the optical parameters in the reduced optical data space This approach was extended to other dimensionality reduction techniques [13] and spectral ranges [14,15,16]; it was implemented in conjunction with PCA for spectral radiances [17] and with the k-distribution method [18].
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