In remote sensing, accurate spectral characteristics are required to identify and describe the targets. Recently, miniature hyperspectral imagers (HySIs) have been flown on small satellites. The non-ideal shape of channels’ spectral response function (SRF) leads to a degraded spectrum, which is normally computed using the band average (BA) method. In this research, the system spectral shape factor (SSSF)-based method is proposed and demonstrated to restore the spectral shape and also to realize super spectral resolution. Computation of spectral radiance (SR) requires channel output and SSSF at that wavelength. SSSF is the convolution of the normalized input signal at a given wavelength and SRF. As the input spectrum is not known prior, coefficients required for SSSF computation are innovatively arrived at from the BA spectrum, which is coarsely similar to the input. The proposed method is tested using SRFs of Chandrayaan-1 and data of Airborne Visible/Infrared Imaging Spectrometer-Next Generation respectively simulating as sensor and input, respectively. The results confirm well matching of SSSF-based spectra with the original with very small deviations in SR values (0.6%), spectral angle map (0.5 deg), and signal information divergence (1×10−5). Slopes remained the same. This study opens up the possibility of optimizing sensor configurations and helps compute accurate spectra from miniature HySIs.
Data from ocean color monitoring sensors at different spectral channels are available for remote sensing of radiation as seen in the given spectral windows, which is used for deriving information on various atmospheric parameters. However, recent studies have demonstrated the potential of hyperspectral (HS) data over multispectral ocean color (MSOC) data in accurately estimating phytoplankton concentration and in monitoring the coastal dynamics. We propose system spectral shape factor (SSSF)-based approach to recover the embedded HS top-of-atmosphere (TOA) radiance (TOARAD) from the MSOC data. SSSF is defined as convolution of normalized input spectrum and sensor spectral response function (SRF). The advantage of SSSF is that it decouples magnitude and spectral shape part of sensor output and enables recovery of TOARAD. To test this method, the airborne visible/infrared imaging spectrometer-next generation data are used to simulate inputs to MSOC. SRF of ocean color monitor simulated MSOC. SSSF of TOARAD is estimated using SSSF of model-based path radiance spectrum of the pixel, which is similar in spectral shape. Methodology, developed using data from five stations, is validated with data from other five stations. The procedure is successfully repeated using SRFs of sea-viewing wide-field-of-view sensor. The recovered HS data are found to be consistent with the original spectra with very small deviations in spectral angle map (<0.012 rad) spectral information divergence (<5.8 × 10 − 5), mean percentage relative error (MPRE) of TOARAD (<0.7 % ), and MPRE of TOA water leaving radiance (<5.8 % ). This approach possibly opens up research for application of HS analysis on MSOC recovered spectra and for optimization of sensor configurations.
Accurate retrieval of top of the atmosphere (TOA) radiance is a sustained research work in ocean color remote sensing. Spectral shapes of sensor input and sensor spectral response (SRF) function may significantly affect the achievable accuracy. A model is presented here to quantify the effects of spectral shapes. We also proposed an innovative method to retrieve the TOA spectral radiance that accounts for spectral shapes. Ten-fold improvement is demonstrated in retrieval accuracy using the proposed method on multiple data sets. Input spectral shape variations are simulated using spectra of TOA radiance, path radiance, exoatmospheric solar irradiance, and integrating sphere. SRF changes are simulated using SRFs of Ocean Color Monitor-2 and Sea-Viewing Wide Field-of-View Sensor. System spectral shape factor, a new parameter, is found to capture the spectral changes and helps quantitative assessment of the inaccuracy. The proposed method will be highly beneficial in deriving accurate geophysical products from the existing and upcoming ocean color remote sensing instruments.
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