Original scientific paper Aerosol particle thickness measurements were performed at the "Politehnika" University in Timisoara (Romania) during March 2011 using a CIMEL SUN solar photometer, as part of the global AERONET network. The aerosol optical thickness (AOT) represents a parameter of optical visibility in an atmospheric air column from the instrument towards the sun, affected by the prese...

The Medium Resolution Imaging Spectrometer (MERIS) launched in February 2002 on-board the ENVISAT spacecraft is making global observations of top-ofatmosphere (TOA) radiances. Aerosol optical properties are retrieved over land using Look-Up Table (LUT) based algorithm and surface reflectances in the blue and the red spectral regions. We compared instantaneous aerosol optical thicknesses retriev...

In this paper, the image-based retrieval algorithm of aerosol characteristics and surface reflectance is used to retrieve the AOD from SPOT images. The validation of retrieved AOD is made possible by using the AERONET data. Five SPOT satellite images are used to testify the algorithm. The results show that the RMSE of the retrieved AOD are 0.12 and 0.08 in XS1 and XS2 bands, although the larges...

Aerosol properties are retrieved from ATSR-2 data (ERS2 satellite) by utilizing the dual view for application over land and the single view for application over water. These two algorithms have been merged into a fast and efficient algorithm that allows for near real-time processing and which is suitable for operational use. It includes corrections for surface and atmospheric effects including ...

Precipitable water vapor retrievals are of major importance for assessing and understanding atmospheric radiative balance solar radiation resources. On that basis, this study presents the first PWV values measured with a novel EKO MS-711 grating spectroradiometer from direct normal irradiance in spectral range between 930 960 nm at Izaña Observatory (IZO, Spain) April December 2019. The expande...

Aerosol types in Asian capital cities were classified using a random forest (RF) satellite-based aerosol classification model during 2018–2020 an investigation of the contributions types, with or without Robotic Network (AERONET) observations. In this study, we used recently developed RF to detect and classify aerosols into four types: pure dust, dust-dominated aerosols, strongly absorbing non-...