An Atmospheric Correction Using High Resolution Numerical Weather Prediction Models for Satellite-Borne Single-Channel Mid-Wavelength and Thermal Infrared Imaging Sensors

Abstract

This paper presents a single-channel atmospheric correction method for remotely sensed infrared (wavelength of 3–15 μm) images with various observation angles. The method is based on basic radiative transfer equations with a simple absorption-focused regression model to calculate the optical thickness of each atmospheric layer. By employing a simple regression model and re-organization of atmospheric profiles by considering viewing geometry, the proposed method conducts atmospheric correction at every pixel of a numerical weather prediction model in a single step calculation. The Visible Infrared Imaging Radiometer Suite (VIIRS) imaging channel (375 m) I4 (3.55~3.93 μm) and I5 (10.50~12.40 μm) bands were used as mid-wavelength and thermal infrared images to demonstrate the effectiveness of the proposed single-channel atmospheric correction method. The estimated sea surface temperatures (SSTs) obtained by the proposed method with high resolution numerical weather prediction models were compared with sea-truth temperature data from ocean buoys, multichannel-based SST products from VIIRS/MODIS, and results from MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5), for validation. High resolution (1.5 km and 12 km) numerical weather prediction (NWP) models distributed by the Korea Meteorological Administration (KMA) were employed as input atmospheric data. Nighttime SST estimations with the I4 band showed a root mean squared error (RMSE) of 0.95 °C, similar to that of the VIIRS product (RMSE: 0.92 °C) and lower than that of the MODIS product (RMSE: 1.74 °C), while estimations with the I5 band showed an RMSE of 1.81 °C. RMSEs from MODTRAN simulations were similar (within 0.2 °C) to those of the proposed method (I4: 0.81 °C, I5: 1.67 °C). These results demonstrated the competitive performance of a regression-based method using high-resolution numerical weather prediction (NWP) models for atmospheric correction of single-channel infrared imaging sensors.

Remote Sensing Systems for Ocean: A Review (Part 1: Passive Systems)

“…Moreover, very limited, if any, sea surface emission in the TIR spectrum reaches the radiometer in cloudy conditions. Therefore, it is essential to apply cloud masking and atmospheric correction to TIR data [79], [80]. Considering these challenges, the main limitations of employing TIR data are the presence of cloud and the necessity of an accurate atmospheric correction model [81].…”

“…Regarding cloud masking, several methods have been developed based on thresholding [82], Bayes' Theorem [83], and alternating decision tree [84] to identify and mask cloudy pixels. Furthermore, in clear-sky conditions, a substantial portion of the sea surface emission may be interfered with the atmospheric molecules, necessitating performing atmospheric correction to compensate for these issues [79].…”

Atmospheric Correction of Thermal Infrared Landsat Images Using High-Resolution Vertical Profiles Simulated by WRF Model

“…Mesoscale atmospheric models use global (re)analysis as initial and boundary conditions for local applications [24,25]. Lee et al (2020) [26] used high-resolution Korean NWP models as input atmospheric data for AC and sea surface temperature estimation with VIIRS sensor bands.…”