Editorial Board Members’ Collection Series: Recent Progress in Atmospheric Remote Sensing

Dear Colleagues,

Remote sensing of aerosols plays a pivotal role in understanding and monitoring Earth’s atmosphere. It offers valuable insights and applications into air quality, cloud and fog formation, climate change, and human health. Aerosols can range from dust and smoke particles to pollutants emitted from industrial activities and vehicle exhausts, and cover ranges from a few nm up to about 10 microns. Given their pervasive presence and potential impacts on the ecosystem, remote sensing techniques can provide a unique vantage point for studying aerosols from the micron scale to a global scale.

One of the primary reasons for remote sensing of aerosols is that its contribution in assessing air quality can range from particle to synoptic scales. Aerosols can pose significant risks to human health and the ecosystem; therefore, it needs to be studied in detail. Remote sensing instruments on satellites, as well as ground-based in situ sensors, can retrieve aerosol physical characteristics and track aerosol dispersion while identifying their sources. Collection of aerosol observations provides monitoring conditions as well as  developing effective strategies for air pollution control that can assist in implementing targeted regulations, leading to improved public health outcomes.

Aerosols also play a vital role in climate change. They scatter and absorb as well as emit sunlight, modifying the Earth’s radiative balance. Remote sensing techniques enable the estimation of aerosol optical properties and particle size distribution. This information, together with numerical models, helps in quantifying the radiative forcing of aerosols directly or indirectly.  Observations and retrievals, as well as numerical model simulations, can lead to better assessments of aerosols’ impact on the Earth’s energy budget through temperature patterns and other physical parameters such as particle spectral mean size.  Aerosol forcing through direct and indirect impacts on climate change remains the greatest uncertainty and challenge in climate modelling and cloud formation.

The aim of this collection is to highlight (1) recent technologies being used in atmospheric remote sensing of aerosols, and (2) how these new observing systems’ measurements can be used in the analysis of cloud and climate systems, as well as ecosystems.

We are especially interested in articles focused on new applications and technologies in

• Satellite based aerosol observations and systems;
• Remote sensing platforms, retrieval techniques, and aerosol analysis;
• Aerosol in situ sensors/observations;
• Lidar observations;
• Polarimetry;
• Aerosol–cloud interactions;
• Neural networks and AI;
• Aerosol impact assessment related to climate change and weather.

Dr. Gorden Videen
Prof. Dr. Ismail Gultepe
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• aerosols
• smoke
• ash
• dust
• ice crystals
• clouds
• polarimetry
• lidar
• satellites
• neural networks and AI

Title: Generating High Spatial and Temporal Surface Albedo with Multi-Scale Wave Mix Image-to-Image Translation
Authors: SAGTHITHARAN KARALASINGHAM 1, 2, RAVINESH C DEO1, 2, DAVID CASILLAS-PEREZ3, NAWIN RAJ1, 2, AND SANCHO SALCEDO-SANZ1, 4
Affiliation: 1 School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD, 4300, Australia. 2 Centre for Applied Climate Science, University of Southern Queensland, Toowoomba, QLD, 4350, Australia. 3 Department of Signal Processing and Communications, Universidad Rey Juan Carlos, Fuenlabrada, 28942, Madrid, Spain 4 Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805, Madrid, Spain
Abstract: Ground-reflected irradiance represents the solar radiation reflected by a surface. It is crucial for bifacial and tilted solar installations that utilise reflected solar radiation to boost energy gain. Varying across daylight hours and locations, ground-reflected irradiance is mostly reliant on surface albedo and global horizontal irradiance. Though the variations of global horizontal irradiance are well-modelled, surface albedo is assumed to be constant across time by various modelling approaches. Given its importance for estimating photovoltaic power generation, this paper develops a novel deep learning approach, Multiscale-WaveMix, for the learning and prediction of surface albedo on a 1-min time scale. It combines satellite-sensed harmonized multi-band surface reflectance imagery at 30m scale from Landsat and Sentinel-2A and 2B satellites and granular ground observations from 3 surfRAD surface radiation monitoring sites as image time series for image-to-image translation between remote sensed imagery and ground observations,conditioned by land cover. The proposed model, Multiscale-WaveMix, was benchmarked against predictions from image-to-image Mix and MLP-Mix, ground observations from the monitoring sites and predictions from the National Solar Radiation Database (NSRDB). Multiscale-WaveMix outperformed other models with a Cauchy Loss of 0.0106, Signal-to-noise-ratio (PSNR) of 67.03 and Structural Similarity Index (SSIM) of 0.999 demonstrating the high potential of such modelling approaches for generating granular time series. As bifacial solar installations become dominant to fulfil increasing renewable energy demand our proposed modelling approach provides predictions on a key input, surface albedo, for intra-day energy gain modelling and bifacial deployment planning.