Polarized reflectance characteristics of plant canopies including atmospheric aerosol optical properties

2009 ◽  
Author(s):  
Xinli Hu ◽  
Xingfa Gu ◽  
Tao Yu ◽  
Qingyan Meng
Keyword(s):  
Optical Properties ◽  
Atmospheric Aerosol ◽  
Aerosol Optical Properties ◽  
Plant Canopies ◽  
Polarized Reflectance

Innovative Instrumentation for the Study of Atmospheric Aerosol Optical Properties

2018 ◽  
pp. 47-56
Author(s):  
Vera Bernardoni ◽  
Alice C. Forello ◽  
Federico Mariani ◽  
Bruno Paroli ◽  
Marco A. C. Potenza ◽  
...  
Keyword(s):  
Optical Properties ◽  
Atmospheric Aerosol ◽  
Aerosol Optical Properties

scholarly journals Real Time Derivation of Atmospheric Aerosol Optical Properties by Concurrent Measurements of Optical and Sampling Instruments

2018 ◽  
Vol 07 (02) ◽  
pp. 140-155 ◽  
Author(s):  
Jamrud Aminuddin ◽  
Shin’ichiro Okude ◽  
Nofel Lagrosas ◽  
Naohiro Manago ◽  
Hiroaki Kuze
Keyword(s):  
Optical Properties ◽  
Real Time ◽  
Atmospheric Aerosol ◽  
Aerosol Optical Properties

scholarly journals Calibrated sky imager for aerosol optical properties determination

2008 ◽  
Vol 8 (6) ◽  
pp. 19989-20018
Author(s):  
A. Cazorla ◽  
J. E. Shields ◽  
M. E. Karr ◽  
A. Burden ◽  
F. J. Olmo ◽  
...  
Keyword(s):  
Neural Network ◽  
Optical Properties ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Standard Method ◽  
Atmospheric Aerosol ◽  
Aerosol Optical Properties ◽  
The Neural Network ◽  
Physical Laboratory ◽  
Sky Imager

Abstract. The calibrated ground-based sky imager developed in the Marine Physical Laboratory, the Whole Sky Imager (WSI), has been tested to determine optical properties of the atmospheric aerosol. Different neural network-based models calculate the aerosol optical depth (AOD) for three wavelengths using the radiance extracted from the principal plane of sky images from the WSI as input parameters. The models use data from a CIMEL CE318 photometer for training and validation and the wavelengths used correspond to the closest wavelengths in both instruments. The spectral dependency of the AOD, characterized by the Ångström exponent α in the interval 440–870, is also derived using the standard AERONET procedure and also with a neural network-based model using the values obtained with a CIMEL CE318. The deviations between the WSI derived AOD and the AOD retrieved by AERONET are within the nominal uncertainty assigned to the AERONET AOD calculation (±0.01), in 80% of the cases. The explanation of data variance by the model is over 92% in all cases. In the case of α, the deviation is within the uncertainty assigned to the AERONET α (±0.1) in 50% for the standard method and 84% for the neural network-based model. The explanation of data variance by the model is 63% for the standard method and 77% for the neural network-based model.

scholarly journals The Campaign on Atmospheric Aerosol Research Network of China: CARE-China

2015 ◽  
Vol 96 (7) ◽  
pp. 1137-1155 ◽  
Author(s):  
Jinyuan Xin ◽  
Yuesi Wang ◽  
Yuepeng Pan ◽  
Dongsheng Ji ◽  
Zirui Liu ◽  
...  
Keyword(s):  
Optical Properties ◽  
Atmospheric Aerosol ◽  
Environmental Changes ◽  
Regional Climate ◽  
Monitoring Network ◽  
Research Network ◽  
Chemical Components ◽  
Aerosol Optical Properties ◽  
Anthropogenic Aerosol ◽  
Main Components

Abstract Based on a network of field stations belonging to the Chinese Academy of Sciences (CAS), the Campaign on Atmospheric Aerosol Research network of China (CARE-China) was recently established as the country’s first monitoring network for the study of the spatiotemporal distribution of aerosol physical characteristics, chemical components, and optical properties, as well as aerosol gaseous precursors. The network comprises 36 stations in total and adopts a unified approach in terms of the instrumentation, experimental standards, and data specifications. This ongoing project is intended to provide an integrated research platform to monitor online PM2.5 concentrations, nine-size aerosol concentrations and chemical component distributions, nine-size secondary organic aerosol (SOA) component distributions, gaseous precursor concentrations (including SO2, NOx, CO, O3, and VOCs), and aerosol optical properties. The data will be used to identify the sources of regional aerosols, the relative contributions from nature and anthropogenic emissions, the formation of secondary aerosols, and the effects of aerosol component distributions on aerosol optical properties. The results will reduce the levels of uncertainty involved in the quantitative assessment of aerosol effects on regional climate and environmental changes and ultimately provide insight into how to mitigate anthropogenic aerosol emissions in China. The present paper provides a detailed description of the instrumentation, methodologies, and experimental procedures used across the network, as well as a case study of observations taken from one station and the distribution of main components of aerosol over China during 2012.

scholarly journals Analysis of Atmospheric Aerosol Optical Properties in the Northeast Brazilian Atmosphere with Remote Sensing Data from MODIS and CALIOP/CALIPSO Satellites, AERONET Photometers and a Ground-Based Lidar

Atmosphere ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 594 ◽  
Author(s):  
Aline M. de Oliveira ◽  
Cristina T. Souza ◽  
Nara P. M. de Oliveira ◽  
Aline K. S. Melo ◽  
Fabio J. S. Lopes ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Atmospheric Chemistry ◽  
Atmospheric Aerosol ◽  
Remote Sensing Data ◽  
Orthogonal Polarization ◽  
Aerosol Optical Properties ◽  
Modis Aod ◽  
Ground Based Lidar ◽  
Aerosol Types

A 12-year analysis, from 2005 to 2016, of atmospheric aerosol optical properties focusing for the first time on Northeast Brazil (NEB) was performed based on four different remote sensing datasets: the Moderate Resolution Imaging Spectroradiometer (MODIS), the Aerosol Robotic Network (AERONET), the Cloud-Aerosol LIDAR with Orthogonal Polarization (CALIOP) and a ground-based Lidar from Natal. We evaluated and identified distinct aerosol types, considering Aerosol Optical Depth (AOD) and Angström Exponent (AE). All analyses show that over the NEB, a low aerosol scenario prevails, while there are two distinct seasons of more elevated AOD that occur every year, from August to October and January to March. According to MODIS, AOD values ranges from 0.04 to 0.52 over the region with a mean of 0.20 and occasionally isolated outliers of up to 1.21. Aerosol types were identified as sea spray, biomass burning, and dust aerosols mostly transported from tropical Africa. Three case studies on days with elevated AOD were performed. All cases identified the same aerosol types and modeled HYSPLIT backward trajectories confirmed their source-dependent origins. This analysis is motivated by the implementation of an atmospheric chemistry model with an advanced data assimilation system that will use the observational database over NEB with the model to overcome high uncertainties in the model results induced by still unvalidated emission inventories.

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