Improving Remote Sensing of Aerosol Optical Depth over Land by Polarimetric Measurements at 1640 nm: Airborne Test in North China

Mid-wave infrared polarization imaging system for detecting moving scene

In this Letter, a polarimetric analyzer is designed for a mid-wave infrared camera. This kind of infrared camera transforms into the mid-wave infrared polarization imaging system to measure the infrared polarization characteristics of the object in the moving scene. The polarimetric analyzer is designed by using the ultra-high-speed and high-position method to drive the polarizer to rotate uniformly at the speed of 900 rpm. The polarization state of the object scene is changed, and the mid-wave infrared camera synchronously acquires the infrared intensity image in different polarized directions, those of 0°, 120°, and 240°. Then, a Stokes vector model is established with the basic rotation angles, and a sort-iteration method is proposed to process the original infrared intensity image. Three continuously neighboring infrared intensity images are used to calculate the degree of linear polarization (DoLP) and the angle of polarization (AoP), which make the infrared polarization image the same imaging frame as the infrared intensity image. Test results show that the mid-wave infrared polarization imaging system can complete the acquisition of the DoLP and the AoP images well with the frame frequency of 45 fps, which is suitable for the infrared polarization detection of the moving scenes. The study has great potential for polarization remote sensing and marine object detection.

Retrieval of the Fine-Mode Aerosol Optical Depth over East China Using a Grouped Residual Error Sorting (GRES) Method from Multi-Angle and Polarized Satellite Data

The fine-mode aerosol optical depth (AODf) is an important parameter for the environment and climate change study, which mainly represents the anthropogenic aerosols component. The Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) instrument can detect polarized signal from multi-angle observation and the polarized signal mainly comes from the radiation contribution of the fine-mode aerosols, which provides an opportunity to obtain AODf directly. However, the currently operational algorithm of Laboratoire d’Optique Atmosphérique (LOA) has a poor AODf retrieval accuracy over East China on high aerosol loading days. This study focused on solving this issue and proposed a grouped residual error sorting (GRES) method to determine the optimal aerosol model in AODf retrieval using the traditional look-up table (LUT) approach and then the AODf retrieval accuracy over East China was improved. The comparisons between the GRES retrieved and the Aerosol Robotic Network (AERONET) ground-based AODf at Beijing, Xianghe, Taihu and Hong_Kong_PolyU sites produced high correlation coefficients (r) of 0.900, 0.933, 0.957 and 0.968, respectively. The comparisons of the GRES retrieved AODf and PARASOL AODf product with those of the AERONET observations produced a mean absolute error (MAE) of 0.054 versus 0.104 on high aerosol loading days (AERONET mean AODf at 865 nm = 0.283). An application using the GRES method for total AOD (AODt) retrieval also showed a good expandability for multi-angle aerosol retrieval of this method.

Improved atmospheric effects elimination method for pBRDF models of painted surfaces

A method for eliminating atmospheric effects in polarimetric imaging remote sensing detection was developed by combining the shadowing method and radiative transfer (RT) model. First, a polarized bidirectional reflectance distribution function (pBRDF) model of painted surfaces was constructed. Using the resulting polarimetric radiance composition, the atmospheric effects elimination method was developed and compared to Shell's method. Experiments were performed using a liquid-crystal-variable-retarder-based imaging polarimeter to obtain the surface pBRDFs. The proposed method showed better performance under different weather conditions than Shell's method. Furthermore, the error was below 4.8% in the proposed method (6.8% in Shell's method), indicating improved quantitative accuracy of the target physical parameters in remote sensing.