Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Browser

New Challenges in Solar Radiation, Modeling and Remote Sensing

Special Issue Editors
Special Issue Information
Keywords
Related Special Issue
Published Papers

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

Deadline for manuscript submissions: closed (1 March 2023) | Viewed by 22087

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special issue New Challenges in Solar Radiation, Modeling and Remote Sensing book cover image

Share This Special Issue

Special Issue Editors

Dr. Jesús Polo

E-Mail Website
Guest Editor

Photovoltaic Solar Energy Unity (Renewable Energy Division) CIEMAT, 28040 Madrid, Spain
Interests: solar radiation; atmospheric physics; solar systems modeling; radiative transfer; remote sensing; solar power plant performance
Special Issues, Collections and Topics in MDPI journals

Dr. Dimitris Kaskaoutis

E-Mail Website
Guest Editor

Institute for Environmental Research & Sustainable Development, National Observatory of Athens, I. Metaxa & Vas. Pavlou, P. Penteli (Lofos Koufou), 15236 Athens, Greece
Interests: solar radiation; aerosols; remote sensing; dust; meteorology; climatology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Surface solar radiation is of vital importance for life on Earth, radiation–energy balance, photosynthesis and photochemical reactions, meteorological and climatic conditions, and the water cycle. Solar radiation is the most abundant renewable energy resource and, therefore, the demands for environmentally clean energy solutions and a reduction in greenhouse gas emissions have shifted global interest toward exploitation of solar energy for sustainable development in meeting electricity demands. Solar radiation measurements are necessary in the assessment of potential solar energy resources, while their scarce spatial coverage renders solar radiation modeling and remote sensing necessary for atmospheric and energy applications. The recent applications in broadening the penetration of solar systems have given rise to new demands and challenges in modeling solar radiation and regarding the availability of new and better solar radiation products. Solar cadasters or modeling of solar radiation with complex topology (rear surface of bifacial PV systems), for instance, are just two specific examples of numerous topics. This Special Issue aims to review recent developments in obtaining solar radiation measurements of higher quality and modeling (solar radiation networks, historical developments, technique comparisons, and standard comparisons between models) and remote sensing using satellite and advanced statistical techniques such as artificial neural networks for solar radiation and energy mapping from regional to global scales. Satellite remote sensing of solar radiation provides better spatial coverage, and various methods have been developed for this, with the main disadvantages being the increased uncertainties and requirements for validation using ground-based measurements or modeling data.

Dr. Jesús Polo
Dr. Dimitris Kaskaoutis
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

solar radiation
models and techniques
solar cadasters
remote sensing
modeling solar radiation with complex topology
radiative forcing
solar dimming/brightening
PV systems
solar radiation/energy mapping

Related Special Issue

New Challenges in Solar Radiation, Modeling and Remote Sensing II in Remote Sensing (3 articles)

Published Papers (11 papers)

Download All Papers

Editorial

Jump to: Research

4 pages, 205 KiB

Open AccessEditorial

Editorial on New Challenges in Solar Radiation, Modeling and Remote Sensing

by Jesús Polo and Dimitris Kaskaoutis

Remote Sens. 2023, 15(10), 2633; https://doi.org/10.3390/rs15102633 - 18 May 2023

Viewed by 994

Abstract

Accurate estimations or measurements of solar radiation are frequently required in many activities and studies in areas such as climatology, atmospheric physics and chemistry, energy and environment, ecosystems, and human health [...] Full article

(This article belongs to the Special Issue New Challenges in Solar Radiation, Modeling and Remote Sensing)

Research

Jump to: Editorial

17 pages, 48768 KiB

Open AccessArticle

Development of a Machine Learning Forecast Model for Global Horizontal Irradiation Adapted to Tibet Based on Visible All-Sky Imaging

by Lingxiao Wu, Tianlu Chen, Nima Ciren, Dui Wang, Huimei Meng, Ming Li, Wei Zhao, Jingxuan Luo, Xiaoru Hu, Shengjie Jia, Li Liao, Yubing Pan and Yinan Wang

Remote Sens. 2023, 15(9), 2340; https://doi.org/10.3390/rs15092340 - 28 Apr 2023

Cited by 2 | Viewed by 1374

Abstract

The Qinghai-Tibet Plateau is rich in renewable solar energy resources. Under the background of China’s “dual-carbon” strategy, it is of great significance to develop a global horizontal irradiation (GHI) prediction model suitable for Tibet. In the radiation balance budget process of the Earth-atmosphere system, clouds, aerosols, air molecules, water vapor, ozone, CO₂ and other components have a direct influence on the solar radiation flux received at the surface. For the descending solar shortwave radiation flux in Tibet, the attenuation effect of clouds is the key variable of the first order. Previous studies have shown that using Artificial intelligence (AI) models to build GHI prediction models is an advanced and effective research method. However, regional localization optimization of model parameters is required according to radiation characteristics in different regions. This study established a set of AI prediction models suitable for Tibet based on ground-based solar shortwave radiation flux observation and cloud cover observation data of whole sky imaging in the Yangbajing area, with the key parameters sensitively tested and optimized. The results show that using the cloud cover as a model input variable can significantly improve the prediction accuracy, and the RMSE of the prediction accuracy is reduced by more than 20% when the forecast horizon is 1 h compared with a model without the cloud cover input. This conclusion is applicable to a scenario with a forecast horizon of less than 4 h. In addition, when the forecast horizon is 1 h, the RMSE of the random forest and long short-term memory models with a 10-min step decreases by 46.1% and 55.8%, respectively, compared with a 1-h step. These conclusions provide a reference for studying GHI prediction models based on ground-based cloud images and machine learning. Full article

(This article belongs to the Special Issue New Challenges in Solar Radiation, Modeling and Remote Sensing)

► Show Figures

Figure 1

22 pages, 3002 KiB

Open AccessArticle

Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques

by Miguel López-Cuesta, Ricardo Aler-Mur, Inés María Galván-León, Francisco Javier Rodríguez-Benítez and Antonio David Pozo-Vázquez

Remote Sens. 2023, 15(9), 2328; https://doi.org/10.3390/rs15092328 - 28 Apr 2023

Cited by 5 | Viewed by 1337

Abstract

Accurate solar radiation nowcasting models are critical for the integration of the increasing solar energy in power systems. This work explored the benefits obtained by the blending of four all-sky-imagers (ASI)-based models, two satellite-images-based models and a data-driven model. Two blending approaches (general and horizon) and two blending models (linear and random forest (RF)) were evaluated. The relative contribution of the different forecasting models in the blended-models-derived benefits was also explored. The study was conducted in Southern Spain; blending models provide one-minute resolution 90 min-ahead GHI and DNI forecasts. The results show that the general approach and the RF blending model present higher performance and provide enhanced forecasts. The improvement in rRMSE values obtained by model blending was up to 30% for GHI (40% for DNI), depending on the forecasting horizon. The greatest improvement was found at lead times between 15 and 30 min, and was negligible beyond 50 min. The results also show that blending models using only the data-driven model and the two satellite-images-based models (one using high resolution images and the other using low resolution images) perform similarly to blending models that used the ASI-based forecasts. Therefore, it was concluded that suitable model blending might prevent the use of expensive (and highly demanding, in terms of maintenance) ASI-based systems for point nowcasting. Full article

(This article belongs to the Special Issue New Challenges in Solar Radiation, Modeling and Remote Sensing)

► Show Figures

Figure 1

17 pages, 5124 KiB

Open AccessArticle

Domain Hybrid Day-Ahead Solar Radiation Forecasting Scheme

by Jinwoong Park, Sungwoo Park, Jonghwa Shim and Eenjun Hwang

Remote Sens. 2023, 15(6), 1622; https://doi.org/10.3390/rs15061622 - 17 Mar 2023

Cited by 3 | Viewed by 1256

Abstract

Recently, energy procurement by renewable energy sources has increased. In particular, as solar power generation has a high penetration rate among them, solar radiation predictions at the site are attracting much attention for efficient operation. Various approaches have been proposed to forecast solar radiation accurately. Recently, hybrid models have been proposed to improve performance through forecasting in the frequency domain using past solar radiation. Since solar radiation data have a pattern, forecasting in the frequency domain can be effective. However, forecasting performance deteriorates on days when the weather suddenly changes. In this paper, we propose a domain hybrid forecasting model that can respond to weather changes and exhibit improved performance. The proposed model consists of two stages. In the first stage, forecasting is performed in the frequency domain using wavelet transform, complete ensemble empirical mode decomposition, and multilayer perceptron, while forecasting in the sequence domain is accomplished using light gradient boosting machine. In the second stage, a multilayer perceptron-based domain hybrid model is constructed using the forecast values of the first stage as the input. Compared with the frequency-domain model, our proposed model exhibits an improvement of up to 36.38% in the normalized root-mean-square error. Full article

(This article belongs to the Special Issue New Challenges in Solar Radiation, Modeling and Remote Sensing)

► Show Figures

Graphical abstract

25 pages, 4880 KiB

Open AccessArticle

Estimation of Perceived Temperature of Road Workers Using Radiation and Meteorological Observation Data

by Hankyung Lee, Hyuk-Gi Kwon, Sukhee Ahn, Hojin Yang and Chaeyeon Yi

Remote Sens. 2023, 15(4), 1065; https://doi.org/10.3390/rs15041065 - 15 Feb 2023

Cited by 2 | Viewed by 1239

Abstract

During summer heat waves, road workers are easily exposed to heat stress and faced with a high risk of thermal diseases and death, and thus preventive measures are required for their safety at the work site. To prepare response measures, it is necessary to estimate workers’ perceived temperature (PT) according to exposure time, road environment, clothing type, and work intensity. This study aimed to examine radiation (short-wave radiation and long-wave radiation) and other meteorological factors (temperature, humidity, and wind) in an actual highway work environment in summer and to estimate PT using the observation data. Analysis of radiation and meteorological factors on the road according to pavement type and weather revealed that more heat was released from asphalt than from concrete. Regression model analysis indicated that compared with young workers (aged 25–30 years), older workers (aged ≥ 60 years) showed a rapid increase in PT as the temperature increased. The temperatures that people actually feel on concrete and asphalt roads in heat wave conditions can be predicted using the PT values calculated by the regression models. Our findings can serve as a basis for measures to prevent workers from thermal diseases at actual road work sites. Full article

(This article belongs to the Special Issue New Challenges in Solar Radiation, Modeling and Remote Sensing)

► Show Figures

Graphical abstract

10 pages, 8876 KiB

Open AccessCommunication

Solar Potential Uncertainty in Building Rooftops as a Function of Digital Surface Model Accuracy

by Jesús Polo and Redlich J. García

Remote Sens. 2023, 15(3), 567; https://doi.org/10.3390/rs15030567 - 17 Jan 2023

Cited by 4 | Viewed by 1889

Abstract

Solar cadasters are excellent tools for determining the most suitable rooftops and areas for PV deployment in urban environments. There are several open models that are available to compute the solar potential in cities. The Solar Energy on Building Envelopes (SEBE) is a powerful model incorporated in a geographic information system (QGIS). The main input for these tools is the digital surface model (DSM). The accuracy of the DSM can contribute significantly to the uncertainty of the solar potential, since it is the basis of the shading and sky view factor computation. This work explores the impact of two different methodologies for creating a DSM to the solar potential. Solar potential is estimated for a small area in a university campus in Madrid using photogrammetry from google imagery and LiDAR data to compute different DSM. Large differences could be observed in the building edges and in the areas with a more complex and diverse topology that resulted in significant differences in the solar potential. The RSMD at a measuring point in the building rooftop can range from 10% to 50% in the evaluation of results. However, the flat and clear areas are much less affected by these differences. A combination of both techniques is suggested as future work to create an accurate DSM. Full article

(This article belongs to the Special Issue New Challenges in Solar Radiation, Modeling and Remote Sensing)

► Show Figures

Figure 1

18 pages, 13513 KiB

Open AccessArticle

Study on Radiative Flux of Road Resolution during Winter Based on Local Weather and Topography

by Hyuk-Gi Kwon, Hojin Yang and Chaeyeon Yi

Remote Sens. 2022, 14(24), 6379; https://doi.org/10.3390/rs14246379 - 16 Dec 2022

Cited by 1 | Viewed by 1417

Abstract

Large-scale traffic accidents caused by black ice on roads have increased rapidly; hence, there is an urgent need to prepare safety measures for their prevention. Here, we used local weather road observations and the linkage between weather prediction and a radiation flux model (LDAPS-SOLWEIG) to calculate prediction information regarding habitual shade areas, sky view factor (SVF), and downward shortwave radiative flux by road direction and lane. Using the LDAPS-SOLWEIG model system, a set of real-time weather prediction data (temperature, humidity, wind speed, and insolation at 1.5 km resolution) was applied, and 5 m resolution radiative flux prediction data, with road resolution blocked by local weather and topography, were calculated. We found that the habitual shaded area can be divided by the direction and lane of the road according to the height and shape of the terrain around the road. The downward shortwave radiation flux data from local meteorological observation data and that calculated from the LDAPS-SOLWEIG model system were compared. When road-freezing occurred on a case day, the RMSE was 20.41 W·m⁻², MB was −5.04 W·m⁻², and r was 0.78. The calculated information, habitual shaded area, and SVF can highlight road sections vulnerable to winter freezing and can be helpful in the special management of these areas. Full article

(This article belongs to the Special Issue New Challenges in Solar Radiation, Modeling and Remote Sensing)

► Show Figures

Graphical abstract

20 pages, 14492 KiB

Open AccessArticle

Estimation of Daily Average Shortwave Solar Radiation under Clear-Sky Conditions by the Spatial Downscaling and Temporal Extrapolation of Satellite Products in Mountainous Areas

by Yanli Zhang and Linhong Chen

Remote Sens. 2022, 14(11), 2710; https://doi.org/10.3390/rs14112710 - 05 Jun 2022

Cited by 4 | Viewed by 1866

Abstract

The downward surface shortwave radiation (DSSR) received by an inclined surface can be estimated accurately based on the mountain radiation transfer model by using the digital elevation model (DEM) and high-resolution optical remote sensing images. However, it is still challenging to obtain the high-resolution daily average DSSR affected by the atmosphere and local topography in mountain areas. In this study, the spatial downscaling and temporal extrapolation methods were explored separately to estimate the high-resolution daily average DSSR under clear-sky conditions based on Himawari-8, Sentinel-2 satellite radiation products and DEM data. The upper and middle reaches of the Heihe River Basin (UM-HRB) and the Laohugou area of Qilian Mountain (LGH) were used as the study areas because there are many ground observation stations in the UM-HRB that are convenient for DSSR spatial downscaling studies and the high-resolution instantaneous DSSR datasets published for the LHG are helpful for DSSR temporal extrapolation studies. The verification results show that both methods of spatial downscaling and temporal extrapolation can effectively estimate the daily average DSSR. A total of 3002 measurements from six observation sites showed that the 50 m downscaled results of the Himawari-8 10-min 5 km radiation products had quite a high correlation with the ground-based measurements from the UM-HRB. The coefficient of determination (R²) exceeded 0.96. The mean bias error (MBE) and the root-mean-squared error (RMSE) were about 41.57 W/m² (or 8.22%) and 49.25 W/m² (or 9.73%), respectively. The fifty-two measurements from two stations in the LHG indicated that the temporal extrapolated results of the Sentinel-2 10 m instantaneous DSSR datasets published previously performed well, giving R², MBE, and RMSE values of 0.65, 41.06 W/m² (or 7.89%) and 88.90 W/m² (or 17.07%), respectively. By comparing the estimation results of the two methods in the LHG, it was found that although the temporal extrapolation method of instantaneous high-resolution radiation products can more finely describe the spatial heterogeneity of solar radiation in complex terrain areas, the overall accuracy is lower than that achieved with the spatial downscaling approach. Full article

(This article belongs to the Special Issue New Challenges in Solar Radiation, Modeling and Remote Sensing)

► Show Figures

Figure 1

26 pages, 7671 KiB

Open AccessArticle

Estimating Hourly Surface Solar Irradiance from GK2A/AMI Data Using Machine Learning Approach around Korea

by Jae-Cheol Jang, Eun-Ha Sohn and Ki-Hong Park

Remote Sens. 2022, 14(8), 1840; https://doi.org/10.3390/rs14081840 - 11 Apr 2022

Cited by 9 | Viewed by 1994

Abstract

Surface solar irradiance (SSI) is a crucial component in climatological and agricultural applications. Because the use of renewable energy is crucial, the importance of SSI has increased. In situ measurements are often used to investigate SSI; however, their availability is limited in spatial coverage. To precisely estimate the distribution of SSI with fine spatiotemporal resolutions, we used the GEOstationary Korea Multi-Purpose SATellite 2A (GEO-KOMPSAT 2A, GK2A) equipped with the Advanced Meteorological Imager (AMI). To obtain an optimal model for estimating hourly SSI around Korea using GK2A/AMI, the convolutional neural network (CNN) model as a machine learning (ML) technique was applied. Through statistical verification, CNN showed a high accuracy, with a root mean square error (RMSE) of 0.180 MJ m⁻², a bias of −0.007 MJ m⁻², and a Pearson’s R of 0.982. The SSI obtained through a ML approach showed an accuracy higher than the GK2A/AMI operational SSI product. The CNN SSI was evaluated by comparing it with the in situ SSI from the Ieodo Ocean Research Station and from flux towers over land; these in situ SSI values were not used for training the model. We investigated the error characteristics of the CNN SSI regarding environmental conditions including local time, solar zenith angle, in situ visibility, and in situ cloud amount. Furthermore, monthly and annual mean daily SSI were calculated for the period from 1 January 2020 to 31 January 2022, and regional characteristics of SSI around Korea were analyzed. This study addressed the availability of satellite-derived SSI to resolve the limitations of in situ measurements. This could play a principal role in climatological and renewable energy applications. Full article

(This article belongs to the Special Issue New Challenges in Solar Radiation, Modeling and Remote Sensing)

► Show Figures

Graphical abstract

22 pages, 9341 KiB

Open AccessArticle

Can Forest Fires Be an Important Factor in the Reduction in Solar Power Production in India?

by Umesh Chandra Dumka, Panagiotis G. Kosmopoulos, Piyushkumar N. Patel and Rahul Sheoran

Remote Sens. 2022, 14(3), 549; https://doi.org/10.3390/rs14030549 - 24 Jan 2022

Cited by 8 | Viewed by 4181

Abstract

The wildfires over the central Indian Himalayan region have attracted the significant attention of environmental scientists. Despite their major and disastrous effects on the environment and air quality, studies on the forest fires’ impacts from a renewable energy point of view are lacking for this region. Therefore, for the first time, we examine the impact of massive forest fires on the reduction in solar energy production over the Indian subcontinent via remote sensing techniques. For this purpose, we used data from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO), the Satellite Application Facility on support to Nowcasting/Very Short-Range Forecasting Meteosat Second Generation (SAFNWC/MSG) in conjunction with radiative transfer model (RTM) simulation, in addition to 1-day aerosol forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). The energy production during the first quarter of 2021 was found to reach 650 kWh/m² and the revenue generated was about INR (Indian rupee) 79.5 million. During the study period, the total attenuation due to aerosols and clouds was estimated to be 116 and 63 kWh/m² for global and beam horizontal irradiance (GHI and BHI), respectively. The financial loss due to the presence of aerosols was found to be INR 8 million, with the corresponding loss due to clouds reaching INR 14 million for the total Indian solar plant’s capacity potential (40 GW). This analysis of daily energy and financial losses can help the grid operators in planning and scheduling power generation and supply during the period of fires. The findings of the present study will drastically increase the awareness among the decision makers in India about the indirect effects of forest fires on renewable energy production, and help promote the reduction in carbon emissions and greenhouse gases in the air, along with the increase in mitigation processes and policies. Full article

(This article belongs to the Special Issue New Challenges in Solar Radiation, Modeling and Remote Sensing)

► Show Figures

Graphical abstract

30 pages, 10360 KiB

Open AccessArticle

Numerical Assessment of Downward Incoming Solar Irradiance in Smoke Influenced Regions—A Case Study in Brazilian Amazon and Cerrado

by Madeleine S. G. Casagrande, Fernando R. Martins, Nilton E. Rosário, Francisco J. L. Lima, André R. Gonçalves, Rodrigo S. Costa, Maurício Zarzur, Marcelo P. Pes and Enio Bueno Pereira

Remote Sens. 2021, 13(22), 4527; https://doi.org/10.3390/rs13224527 - 11 Nov 2021

Cited by 9 | Viewed by 2757

Abstract

Smoke aerosol plumes generated during the biomass burning season in Brazil suffer long-range transport, resulting in large aerosol optical depths over an extensive domain. As a consequence, downward surface solar irradiance, and in particular the direct component, can be significantly reduced. Accurate solar energy assessments considering the radiative contribution of biomass burning aerosols are required to support Brazil’s solar power sector. This work presents the 2nd generation of the radiative transfer model BRASIL-SR, developed to improve the aerosol representation and reduce the uncertainties in surface solar irradiance estimates in cloudless hazy conditions and clean conditions. Two numerical experiments allowed to assess the model’s skill using observational or regional MERRA-2 reanalysis AOD data in a region frequently affected by smoke. Four ground measurement sites provided data for the model output validation. Results for DNI obtained using

δ

-Eddington scaling and without scaling are compared, with the latter presenting the best skill in all sites and for both experiments. An increase in the relative error of DNI results obtained with

δ

-Eddington optical depth scaling as AOD increases is evidenced. For DNI, MBD deviations ranged from −2.3 to −0.5%, RMSD between 2.3 and 4.7% and OVER between 0 and 5.3% when using in-situ AOD data. Overall, our results indicate a good skill of BRASIL-SR for the estimation of both GHI and DNI. Full article

(This article belongs to the Special Issue New Challenges in Solar Radiation, Modeling and Remote Sensing)

► Show Figures

Graphical abstract

Journal Menu

Journal Browser

New Challenges in Solar Radiation, Modeling and Remote Sensing

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Related Special Issue

Published Papers (11 papers)

Editorial

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI