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Satellite Remote Sensing of Fires, Smoke, Air Quality and Integration...
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Satellite Remote Sensing of Fires, Smoke, Air Quality and Integration of Earth Observation Data

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 10128

Special Issue Editor

Dr. Danilo Custódio

E-Mail Website
Guest Editor
Department of Environmental Chemistry, Helmholtz-Zentrum Geesthacht, Max-Planck-Straße 1, 21502 Geesthacht, Germany
Interests: satellite product validation; atmosphere trace species; integration of atmospheric data platforms; disruption in earth observation data

Special Issue Information

Dear Colleagues,

It is my pleasure to announce the launch of a new Special Issue on the topic of integration of earth observation data, which could essentially integrate remote sensing and in situ observations. Data integration would regard meteorology and the composition of the atmosphere, which could give insight into environmental cycling and anthropogenic forcing, on the light of validation and development of satellite products.

The integration of Earth observation data platforms, assessing their inherent uncertainties and offering quality standard tractability for retrieved data from the main satellite product, is an important and challenging task.

The aim of this Special Issue in Remote Sensing is to offer a platform to discuss the use of remote sensing, and other “downstream-algorithm data” to improve our knowledge and understanding of earth observation data, whose overall aim is to evaluate the real-time monitoring platform data quality of our environment with reference to the contamination of air, water, and terrestrial land cover.

Dr. Danilo Custódio
Guest Editor

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.

Keywords

  • satellite product validation
  • atmosphere trace species
  • integration of atmospheric data platforms
  • disruption in earth observation data
  • remote sensing

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

17 pages, 3739 KiB  
Article
Atmospheric Formaldehyde Monitored by TROPOMI Satellite Instrument throughout 2020 over São Paulo State, Brazil
by Arthur Dias Freitas and Adalgiza Fornaro
Remote Sens. 2022, 14(13), 3032; https://doi.org/10.3390/rs14133032 - 24 Jun 2022
Cited by 5 | Viewed by 2550
Abstract
We aimed to study the daily formaldehyde (HCHO) columns over urban and forested areas in São Paulo State, Brazil, from rhe TROPOMI spectrometer onboard the Sentinel-5P satellite during 2020. Nineteen specific areas were defined in four regions: 3 areas in each of two [...] Read more.
We aimed to study the daily formaldehyde (HCHO) columns over urban and forested areas in São Paulo State, Brazil, from rhe TROPOMI spectrometer onboard the Sentinel-5P satellite during 2020. Nineteen specific areas were defined in four regions: 3 areas in each of two preserved Atlantic Forests (PEMD and PETAR), 3 in a sugarcane growing region (NERG) and 10 in the Metropolitan Area of São Paulo (MASP), among which 2 areas are in the Morro Grande reserve, which is a significant remnant of Atlantic Forest outside the densely urbanized area of MASP. An analysis of variance and Tukey’s test showed that the mean annual columns over the Morro Grande reserve (1.69±1.05×10−4 mol/m² and 1.73±1.07×10−4 mol/m²) presented greater statistical similarity with the forest and rural areas of the state (<1.70×10−4 mol/m²) than with MASP (>2.00×10−4 mol/m²), indicating few effects from megacity anthropogenic emissions. Case studies addressing selected days in 2020 showed that fires in and around the state were related to episodes of maximum density of HCHO columns. The results showed significant seasonality, with lower concentrations during summer (wet season) and higher concentrations during winter and spring (dry and transition dry–wet seasons). Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Fires, Smoke, Air Quality and Integration of Earth Observation Data)
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19 pages, 8034 KiB  
Article
Land Use Quantile Regression Modeling of Fine Particulate Matter in Australia
by Peng Wu and Yongze Song
Remote Sens. 2022, 14(6), 1370; https://doi.org/10.3390/rs14061370 - 11 Mar 2022
Cited by 7 | Viewed by 3196
Abstract
Small data samples are still a critical challenge for spatial predictions. Land use regression (LUR) is a widely used model for spatial predictions with observations at a limited number of locations. Studies have demonstrated that LUR models can overcome the limitation exhibited by [...] Read more.
Small data samples are still a critical challenge for spatial predictions. Land use regression (LUR) is a widely used model for spatial predictions with observations at a limited number of locations. Studies have demonstrated that LUR models can overcome the limitation exhibited by other spatial prediction models which usually require greater spatial densities of observations. However, the prediction accuracy and robustness of LUR models still need to be improved due to the linear regression within the LUR model. To improve LUR models, this study develops a land use quantile regression (LUQR) model for more accurate spatial predictions for small data samples. The LUQR is an integration of the LUR and quantile regression, which both have advantages in predictions with a small data set of samples. In this study, the LUQR model is applied in predicting spatial distributions of annual mean PM2.5concentrations across the Greater Sydney Region, New South Wales, Australia, with observations at 19 valid monitoring stations in 2020. Cross validation shows that the goodness-of-fit can be improved by 25.6–32.1% by LUQR models when compared with LUR, and prediction root mean squared error (RMSE) and mean absolute error (MAE) can be reduced by 10.6–13.4% and 19.4–24.7% by LUQR models, respectively. This study also indicates that LUQR is a more robust model for the spatial prediction with small data samples than LUR. Thus, LUQR has great potentials to be widely applied in spatial issues with a limited number of observations. Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Fires, Smoke, Air Quality and Integration of Earth Observation Data)
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25 pages, 32218 KiB  
Article
Assessing Desert Dust Indirect Effects on Cloud Microphysics through a Cloud Nucleation Scheme: A Case Study over the Western Mediterranean
by Konstantinos Tsarpalis, Petros Katsafados, Anastasios Papadopoulos and Nikolaos Mihalopoulos
Remote Sens. 2020, 12(21), 3473; https://doi.org/10.3390/rs12213473 - 22 Oct 2020
Cited by 8 | Viewed by 2876
Abstract
In this study, the performance and characteristics of the advanced cloud nucleation scheme of Fountoukis and Nenes, embedded in the fully coupled Weather Research and Forecasting/Chemistry (WRF/Chem) model, are investigated. Furthermore, the impact of dust particles on the distribution of the cloud condensation [...] Read more.
In this study, the performance and characteristics of the advanced cloud nucleation scheme of Fountoukis and Nenes, embedded in the fully coupled Weather Research and Forecasting/Chemistry (WRF/Chem) model, are investigated. Furthermore, the impact of dust particles on the distribution of the cloud condensation nuclei (CCN) and the way they modify the pattern of the precipitation are also examined. For the simulation of dust particle concentration, the Georgia Tech Goddard Global Ozone Chemistry Aerosol Radiation and Transport of Air Force Weather Agency (GOCART-AFWA) is used as it includes components for the representation of dust emission and transport. The aerosol activation parameterization scheme of Fountoukis and Nenes has been implemented in the six-class WRF double-moment (WDM6) microphysics scheme, which treats the CCN distribution as a prognostic variable, but does not take into account the concentration of dust aerosols. Additionally, the presence of dust particles that may facilitate the activation of CCN into cloud or rain droplets has also been incorporated in the cumulus scheme of Grell and Freitas. The embedded scheme is assessed through a case study of significant dust advection over the Western Mediterranean, characterized by severe rainfall. Inclusion of CCN based on prognostic dust particles leads to the suppression of precipitation over hazy areas. On the contrary, precipitation is enhanced over areas away from the dust event. The new prognostic CCN distribution improves in general the forecasting skill of the model as bias scores, the root mean square error (RMSE), false alarm ratio (FAR) and frequencies of missed forecasts (FOM) are limited when modelled data are compared against satellite, LIDAR and aircraft observations. Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Fires, Smoke, Air Quality and Integration of Earth Observation Data)
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