Hyperspectral and multispectral remote sensing of aerosols based on surface spectral reconstruction by PCA

A series of studies of hyperspectral remote sensing had been carried out to develop a hyperspectral remote sensing technique for aerosol retrieval in the previous works, including the theoretical framework, information content analysis and application to the real data, in which a hyperspectral inversion algorithm was developed to simultaneously retrieved the aerosol and surface properties, and the surface reflectance spectra were decomposed into different principal components, thus only several weighting coefficients of principal components (PCs) were needed to be retrieved. In this study, based on the optimal estimation (OE) framework, we extend the OE-based hyperspectral inversion algorithm to multispectral remote sensing, and the synthetic multispectral intensities of Polarized Scanning Atmospheric Corrector (PSAC) centered in 410, 443, 555, 670, 865, 1610 and 2250 nm are used to test the inversion framework. Principal component analysis (PCA) has been conducted for the spectral dataset of 4 typical surface types with 7 channels of PSAC, in which the PC’s contribution and spectra, the spectral reconstruction results and constraints of PC’s weighting coeffects are discussed. Unified Linearized Vector Radiative Transfer Model (UNL-VRTM) is used as the forward model, and 1% Gaussian distribution errors has been added to the simulated radiance at the top of the atmosphere for multispectral inversion test. The iterative process of multispectral normalized intensities and the reconstructed surface reflectance during the OE iteration are investigated, and the normalized cost function values are well convergent. This study can provide key support to the development of OE-based inversion algorithms for multispectral remote sensing

[1]  Teruyuki Nakajima,et al.  Development of a Two-Channel Aerosol Retrieval Algorithm on a Global Scale Using NOAA AVHRR , 1999 .

[2]  W. V. Hoyningen-Huene,et al.  Retrieval of aerosol optical thickness over land surfaces from top‐of‐atmosphere radiance , 2003 .

[3]  Paul Ginoux,et al.  A Long-Term Record of Aerosol Optical Depth from TOMS Observations and Comparison to AERONET Measurements , 2002 .

[4]  F. Maignan,et al.  Remote sensing of aerosols over land surfaces from POLDER‐ADEOS‐1 polarized measurements , 2001 .

[5]  Sonoyo Mukai,et al.  Polarimetric remote sensing of atmospheric aerosols: Instruments, methodologies, results, and perspectives , 2019, Journal of Quantitative Spectroscopy and Radiative Transfer.

[6]  S. Hook,et al.  The ASTER spectral library version 2.0 , 2009 .

[7]  Yi Wang,et al.  Passive remote sensing of altitude and optical depth of dust plumes using the oxygen A and B bands: First results from EPIC/DSCOVR at Lagrange‐1 point , 2017, Geophysical research letters.

[8]  Nadine Gobron,et al.  Using angular and spectral shape similarity constraints to improve MISR aerosol and surface retrievals over land , 2005 .

[9]  Jun Wang,et al.  Detecting layer height of smoke aerosols over vegetated land and water surfaces via oxygen absorption bands: hourly results from EPIC/DSCOVR in deep space , 2019, Atmospheric Measurement Techniques.

[10]  Yongli Wang,et al.  A decomposition method for large-scale box constrained optimization , 2014, Appl. Math. Comput..

[11]  Xiaoguang Xu,et al.  Retrieval of aerosol microphysical properties from AERONET photopolarimetric measurements: 1. Information content analysis , 2015 .

[12]  Li Sun,et al.  An active set quasi-Newton method with projected search for bound constrained minimization , 2009, Comput. Math. Appl..

[13]  Weizhen Hou,et al.  An algorithm for hyperspectral remote sensing of aerosols: 2. Information content analysis for aerosol parameters and principal components of surface spectra , 2017 .

[14]  Weizhen Hou,et al.  An algorithm for hyperspectral remote sensing of aerosols: 3. Application to the GEO-TASO data in KORUS-AQ field campaign , 2020 .

[15]  Yoram J. Kaufman,et al.  Monitoring of aerosol forcing of climate from space: analysis of measurement requirements , 2004, Journal of Quantitative Spectroscopy and Radiative Transfer.

[16]  Zhengqiang Li,et al.  Study on errors propagation in synchronous atmospheric correction for HJ-2 satellites , 2019, Applied Optics and Photonics China.

[17]  Lorraine A. Remer,et al.  Suomi‐NPP VIIRS aerosol algorithms and data products , 2013 .

[18]  Zhengqiang Li,et al.  Improving Remote Sensing of Aerosol Microphysical Properties by Near‐Infrared Polarimetric Measurements Over Vegetated Land: Information Content Analysis , 2017 .

[19]  Zhengqiang Li,et al.  Optimal Estimation Retrieval of Aerosol Fine-Mode Fraction from Ground-Based Sky Light Measurements , 2019, Atmosphere.

[20]  P. Levelt,et al.  Aerosols and surface UV products from Ozone Monitoring Instrument observations: An overview , 2007 .

[21]  E. Vermote,et al.  Second‐generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance , 2007 .

[22]  Jun Wang,et al.  Sense size‐dependent dust loading and emission from space using reflected solar and infrared spectral measurements: An observation system simulation experiment , 2017 .

[23]  J. Veefkind,et al.  Regional Distribution of Aerosol over Land, Derived from ATSR-2 and GOME , 2000 .

[24]  Zhengqiang Li,et al.  Retrieval of Aerosol Microphysical Properties Based on the Optimal Estimation Method: Information Content Analysis for Satellite Polarimetric Remote Sensing Measurements , 2018 .

[25]  Zhengqiang Li,et al.  Remote sensing of atmospheric fine particulate matter (PM2.5) mass concentration near the ground from satellite observation , 2015 .

[26]  Jun Wang,et al.  Directional Polarimetric Camera (DPC): Monitoring aerosol spectral optical properties over land from satellite observation , 2018, Journal of Quantitative Spectroscopy and Radiative Transfer.

[27]  Didier Tanré,et al.  Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations , 2010 .

[28]  Xiong Liu,et al.  A numerical testbed for remote sensing of aerosols, and its demonstration for evaluating retrieval synergy from a geostationary satellite constellation of GEO-CAPE and GOES-R , 2014 .

[29]  O. Boucher,et al.  A satellite view of aerosols in the climate system , 2002, Nature.

[30]  Jun Wang,et al.  An algorithm for hyperspectral remote sensing of aerosols: 1. Development of theoretical framework , 2016 .

[31]  Jin Huang,et al.  Enhanced Deep Blue aerosol retrieval algorithm: The second generation , 2013 .

[32]  Jun Wang,et al.  Retrieval of aerosol microphysical properties from AERONET photopolarimetric measurements: 2. A new research algorithm and case demonstration , 2015 .

[33]  Jorge Nocedal,et al.  Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization , 1997, TOMS.

[34]  Jacques Piazzola,et al.  Aerosol remote sensing over the ocean using MSG-SEVIRI visible images , 2009 .

[35]  Weizhen Hou,et al.  Aerosol retrieval study from multiangle polarimetric satellite data based on optimal estimation method , 2020 .

[36]  Chi Xu,et al.  Study on the spectral reconstruction of typical surface types based on spectral library and principal component analysis , 2019, Symposium on Novel Photoelectronic Detection Technology and Application.

[37]  Weizhen Hou,et al.  A pilot study of shortwave spectral fingerprints of smoke aerosols above liquid clouds , 2018, Journal of Quantitative Spectroscopy and Radiative Transfer.

[38]  Jorge Nocedal,et al.  A Limited Memory Algorithm for Bound Constrained Optimization , 1995, SIAM J. Sci. Comput..

[39]  Otto Hasekamp,et al.  Retrieval of aerosol microphysical and optical properties over land using a multimode approach , 2018, Atmospheric Measurement Techniques.