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Remote Sensing
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Remote Sensing of the Earth’s Radiation Budget
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Remote Sensing of the Earth’s Radiation Budget

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 4533

Special Issue Editors

Dr. Tianxing Wang

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Guest Editor
School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519000, China
Interests: remote sensing of earth’s radiation balance
Special Issues, Collections and Topics in MDPI journals
Dr. Husi Letu

E-Mail Website
Guest Editor
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: climate change; surface energy consumption; modelling of cloud microphysical processes and remote sensing radiative transfer
Dr. Dongdong Wang

E-Mail Website
Guest Editor
Institute of Remote Sensing and Geographic Information Systems, Peking University, Beijing, China
Interests: quantitative remote sensing; earth radiation budget; remote sensing data integration
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jie Cheng

E-Mail Website
Guest Editor
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: thermal infrared remote sensing; atmospheric radiation and surface energy balance
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tao He

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: atmospheric physics; precipitation; climate modeling; climate variability; fluorescence; nanomaterials; optics and lasers; material characterization; air quality; environment
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaotong Zhang

E-Mail Website
Guest Editor
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: remote sensing of radiation balance and energy budget sphere; data fusion and mining; data spatio-temporal analysis; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The radiation balance (radiation budget) of the earth–atmosphere system is currently one of the hottest research areas in the field of global warming. Shortwave radiation (Solar radiation) is the primary energy source of the earth–atmosphere system; the Earth’s surface, clouds and atmosphere absorbs/reflects shortwave radiation and simultaneously emits longwave radiation to the space, resulting in net surface/ TOA radiation. The radiation components serve a vital role in geophysical processes, land ecological models, and matter and energy cycles. Accurate and spatio-temporally continuous radiation products are of great importance to climate change, ecology, water cycle, carbon cycle, and energy balance studies as well as various land applications.

Remote sensing provide an effective and promising method for objectively detecting the Earth’s radiation budget and changes at both surface and the TOA levels. Although tremendous efforts have been made to derive shortwave and longwave radiation components from space, accurate estimation of the Earth’s radiation budget and the associated variations are still very challenging especially under cloudy sky and rugged terrain conditions.

Considering the numerous technical problems faced by remote sensing in deriving radiation and the urgent demand for radiation products in the community, this Special Issue aims to publish original research articles concerning the observation of both shortwave and longwave radiation components using the state-of-the-art remote sensing techniques as well as related analysis methods.

This Special Issue mainly focuses on contributions that address topics including but not limited to:

  • Radiation-related radiative transfer modelling;
  • Estimation of shortwave components;
  • Estimation of Longwave components;
  • Derivation of Surface and TOA albedo;
  • Land surface temperature and emissivities retrieval;
  • Estimation of outgoing longwave radiation at TOA;
  • Cloud and aerosol effect on the radiation;
  • Radiation modelling over the rugged terrain;
  • Radiation validation and inter-comparisons;
  • Long-term radiation products from space;
  • Applications of radiation products.

Dr. Tianxing Wang
Dr. Husi Letu
Dr. Dongdong Wang
Prof. Dr. Jie Cheng
Dr. Tao He
Dr. Xiaotong Zhang
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.

Keywords

  • shortwave radiation
  • longwave radiation
  • net radiation
  • aerosol and cloud
  • topographic effect
  • top-of-atmosphere (TOA) radiation
  • albedo
  • LST
  • validation

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

Published Papers (2 papers)

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Research

16 pages, 5687 KiB  
Article
Machine Learning Models for Approximating Downward Short-Wave Radiation Flux over the Ocean from All-Sky Optical Imagery Based on DASIO Dataset
by Mikhail Krinitskiy, Vasilisa Koshkina, Mikhail Borisov, Nikita Anikin, Sergey Gulev and Maria Artemeva
Remote Sens. 2023, 15(7), 1720; https://doi.org/10.3390/rs15071720 - 23 Mar 2023
Cited by 3 | Viewed by 1820
Abstract
Downward short-wave (SW) solar radiation is the only essential energy source powering the atmospheric dynamics, ocean dynamics, biochemical processes, and so forth on our planet. Clouds are the main factor limiting the SW flux over the land and the Ocean. For the accurate [...] Read more.
Downward short-wave (SW) solar radiation is the only essential energy source powering the atmospheric dynamics, ocean dynamics, biochemical processes, and so forth on our planet. Clouds are the main factor limiting the SW flux over the land and the Ocean. For the accurate meteorological measurements of the SW flux one needs expensive equipment-pyranometers. For some cases where one does not need golden-standard quality of measurements, we propose estimating incoming SW radiation flux using all-sky optical RGB imagery which is assumed to incapsulate the whole information about the downward SW flux. We used DASIO all-sky imagery dataset with corresponding SW downward radiation flux measurements registered by an accurate pyranometer. The dataset has been collected in various regions of the World Ocean during several marine campaigns from 2014 to 2021, and it will be updated. We demonstrate the capabilities of several machine learning models in this problem, namely multilinear regression, Random Forests, Gradient Boosting and convolutional neural networks (CNN). We also applied the inverse target frequency (ITF) re-weighting of the training subset in an attempt of improving the SW flux approximation quality. We found that the CNN is capable of approximating downward SW solar radiation with higher accuracy compared to existing empiric parameterizations and known algorithms based on machine learning methods for estimating downward SW flux using remote sensing (MODIS) imagery. The estimates of downward SW radiation flux using all-sky imagery may be of particular use in case of the need for the fast radiative budgets assessment of a site. Full article
(This article belongs to the Special Issue Remote Sensing of the Earth’s Radiation Budget)
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22 pages, 5191 KiB  
Article
The Spatio-Temporal Variability in the Radiative Forcing of Light-Absorbing Particles in Snow of 2003–2018 over the Northern Hemisphere from MODIS
by Jiecan Cui, Xiaoying Niu, Yang Chen, Yuxuan Xing, Shirui Yan, Jin Zhao, Lijun Chen, Shuaixi Xu, Dongyou Wu, Tenglong Shi, Xin Wang and Wei Pu
Remote Sens. 2023, 15(3), 636; https://doi.org/10.3390/rs15030636 - 21 Jan 2023
Cited by 4 | Viewed by 1978
Abstract
Light-absorbing particles (LAPs) deposited on snow can significantly reduce surface albedo and contribute to positive radiative forcing. This study firstly estimated and attributed the spatio-temporal variability in the radiative forcing (RF) of LAPs in snow over the northern hemisphere during the snow-covered period [...] Read more.
Light-absorbing particles (LAPs) deposited on snow can significantly reduce surface albedo and contribute to positive radiative forcing. This study firstly estimated and attributed the spatio-temporal variability in the radiative forcing (RF) of LAPs in snow over the northern hemisphere during the snow-covered period 2003–2018 by employing Moderate Resolution Imaging Spectroradiometer (MODIS) data, coupled with snow and atmospheric radiative transfer modelling. In general, the RF for the northern hemisphere shows a large spatial variability over the whole snow-covered areas and periods, with the highest value (12.7 W m−2) in northeastern China (NEC) and the lowest (1.9 W m−2) in Greenland (GRL). The concentration of LAPs in snow is the dominant contributor to spatial variability in RF in spring (~73%) while the joint spatial contributions of snow water equivalent (SWE) and solar irradiance (SI) are the most important (>50%) in winter. The average northern hemisphere RF gradually increases from 2.1 W m−2 in December to 4.1 W m−2 in May and the high-value area shifts gradually northwards from mid-altitude to high-latitude over the same period, which is primarily due to the seasonal variability of SI (~58%). More interestingly, our data reveal a significant decrease in RF over high-latitude Eurasia (HEUA) of −0.04 W m−2 a−1 and northeastern China (NEC) of −0.14 W m−2 a−1 from 2003 to 2018. By employing a sensitivity test, we find the concurrent decline in the concentration of LAPs in snow accounted for the primary responsibility for the decrease in RF over these two areas, which is further confirmed by in situ observations. Full article
(This article belongs to the Special Issue Remote Sensing of the Earth’s Radiation Budget)
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