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Assessment of Renewable Energy Resources with Remote Sensing
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Assessment of Renewable Energy Resources with Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 August 2020) | Viewed by 45131

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

Special Issue Editor

Prof. Dr. Fernando Ramos Martins

E-Mail Website
Guest Editor
Institute of Marine Science, Federal University of São Paulo (IMar/UNIFESP), Santos 11070-102, SP, Brazil
Interests: renewable energy assessment and forecasting; energy transition; earth system science; climate change; numerical modeling; data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The socioeconomic development of a region or country is intrinsically associated with energy consumption. We expect continuous growth in energy demand in countries with a robust emerging economy in the next decade, to support the enhancement of the quality of life and essential services such as food, health, education, transportation, and entertainment.

On the other hand, energy consumption has been pointed out as one of the factors responsible for the growing concentration of greenhouse gases (GHG) in the atmosphere due to fossil fuels being used as the primary source of energy. Concerns about climate change associated with the high GHG concentrations in the atmosphere have been driving the development of increasingly efficient technologies to exploit renewable energy resources with low GHG emissions.

However, technological developments in energy conversion processes are not a unique challenge that will boost renewable energy resources like solar energy, wind energy, hydroelectric energy, and wave energy. Reliable data and knowledge on renewable energy resource assessment are also required and essential to ensure sustainable expansion considering environmental, financial and energetic security.

Renewable energy resources have an intrinsic relationship with local atmospheric conditions and the regional climate. Even small and fast changes in meteorological conditions can cause significant variability in power generation at different time and space scales. Methodologies based on the remote sensing of the atmosphere are the primary source of information for the development of numerical models that aim at supporting the planning and operation of an electric system with a substantial contribution of intermittent energy sources.

The Special Issue focuses on disseminating scientific knowledge and technological developments for the assessment and forecasting of renewable energy resources using remote sensing techniques in order to support the expansion of the exploitation of renewable energy resources, especially in regions where energy demand is rapidly expanding.

Dr. Fernando Ramos Martins
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

  • Renewable energy assessment and forecasting
  • Remote sensing applied to the energy sector
  • Climate and weather influence on renewable power resources
  • Seasonal and spatial complementarity of renewable power
  • Resources
  • Renewable energy power plants management
  • Geographical Information Systems applied to the energy sector

Published Papers (12 papers)

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Editorial

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6 pages, 569 KiB  
Editorial
Editorial for the Special Issue: Assessment of Renewable Energy Resources with Remote Sensing
by Fernando Ramos Martins
Remote Sens. 2020, 12(22), 3748; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223748 - 14 Nov 2020
Cited by 2 | Viewed by 1822
Abstract
The development of renewable energy sources plays a fundamental role in the transition towards a low carbon economy. Considering that renewable energy resources have an intrinsic relationship with meteorological conditions and climate patterns, methodologies based on the remote sensing of the atmosphere are [...] Read more.
The development of renewable energy sources plays a fundamental role in the transition towards a low carbon economy. Considering that renewable energy resources have an intrinsic relationship with meteorological conditions and climate patterns, methodologies based on the remote sensing of the atmosphere are fundamental sources of information to support the energy sector in planning and operation procedures. This Special Issue is intended to provide a highly recognized international forum to present recent advances in remote sensing to data acquisition required by the energy sector. After a review, a total of eleven papers were accepted for publication. The contributions focus on solar, wind, and geothermal energy resource. This editorial presents a brief overview of each contribution. Full article
(This article belongs to the Special Issue Assessment of Renewable Energy Resources with Remote Sensing)
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Research

Jump to: Editorial, Other

21 pages, 4616 KiB  
Article
Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning
by Alexandra I. Khalyasmaa, Stanislav A. Eroshenko, Valeriy A. Tashchilin, Hariprakash Ramachandran, Teja Piepur Chakravarthi and Denis N. Butusov
Remote Sens. 2020, 12(20), 3420; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203420 - 18 Oct 2020
Cited by 16 | Viewed by 3604
Abstract
This article highlights the industry experience of the development and practical implementation of a short-term photovoltaic forecasting system based on machine learning methods for a real industry-scale photovoltaic power plant implemented in a Russian power system using remote data acquisition. One of the [...] Read more.
This article highlights the industry experience of the development and practical implementation of a short-term photovoltaic forecasting system based on machine learning methods for a real industry-scale photovoltaic power plant implemented in a Russian power system using remote data acquisition. One of the goals of the study is to improve photovoltaic power plants generation forecasting accuracy based on open-source meteorological data, which is provided in regular weather forecasts. In order to improve the robustness of the system in terms of the forecasting accuracy, we apply newly derived feature introduction, a factor obtained as a result of feature engineering procedure, characterizing the relationship between photovoltaic power plant energy production and solar irradiation on a horizontal surface, thus taking into account the impacts of atmospheric and electrical nature. The article scrutinizes the application of different machine learning algorithms, including Random Forest regressor, Gradient Boosting Regressor, Linear Regression and Decision Trees regression, to the remotely obtained data. As a result of the application of the aforementioned approaches together with hyperparameters, tuning and pipelining of the algorithms, the optimal structure, parameters and the application sphere of different regressors were identified for various testing samples. The mathematical model developed within the framework of the study gave us the opportunity to provide robust photovoltaic energy forecasting results with mean accuracy over 92% for mostly-sunny sample days and over 83% for mostly cloudy days with different types of precipitation. Full article
(This article belongs to the Special Issue Assessment of Renewable Energy Resources with Remote Sensing)
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19 pages, 8555 KiB  
Article
The Global Wind Resource Observed by Scatterometer
by Ian R. Young, Ebru Kirezci and Agustinus Ribal
Remote Sens. 2020, 12(18), 2920; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12182920 - 09 Sep 2020
Cited by 12 | Viewed by 3258
Abstract
A 27-year-long calibrated multi-mission scatterometer data set is used to determine the global basin-scale and near-coastal wind resource. In addition to mean and percentile values, the analysis also determines the global values of both 50- and 100-year return period wind speeds. The analysis [...] Read more.
A 27-year-long calibrated multi-mission scatterometer data set is used to determine the global basin-scale and near-coastal wind resource. In addition to mean and percentile values, the analysis also determines the global values of both 50- and 100-year return period wind speeds. The analysis clearly shows the seasonal variability of wind speeds and the differing response of the two hemispheres. The maximum wind speeds in each hemisphere are comparable but there is a much larger seasonal cycle in the northern hemisphere. As a result, the southern hemisphere has a more consistent year-round wind climate. Hence, coastal regions of southern Africa, southern Australia, New Zealand and southern South America appear particularly suited to coastal and offshore wind energy projects. The extreme value analysis shows that the highest extreme wind speeds occur in the North Atlantic Ocean with extreme wind regions concentrated along the western boundaries of the North Atlantic and North Pacific Oceans and the Indian Ocean sector of the Southern Ocean. The signature of tropical cyclones is clearly observed in each of the well-known tropical cyclone basins. Full article
(This article belongs to the Special Issue Assessment of Renewable Energy Resources with Remote Sensing)
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27 pages, 14406 KiB  
Article
Enhancement of Cloudless Skies Frequency over a Large Tropical Reservoir in Brazil
by André R. Gonçalves, Arcilan T. Assireu, Fernando R. Martins, Madeleine S. G. Casagrande, Enrique V. Mattos, Rodrigo S. Costa, Robson B. Passos, Silvia V. Pereira, Marcelo P. Pes, Francisco J. L. Lima and Enio B. Pereira
Remote Sens. 2020, 12(17), 2793; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12172793 - 27 Aug 2020
Cited by 6 | Viewed by 3512
Abstract
Several studies show the effects of lake breezes on cloudiness over natural lakes and large rivers, but only few contain information regarding large flooded areas of hydroelectric dams. Most Brazilian hydropower plants have large water reservoirs that may induce significant changes in the [...] Read more.
Several studies show the effects of lake breezes on cloudiness over natural lakes and large rivers, but only few contain information regarding large flooded areas of hydroelectric dams. Most Brazilian hydropower plants have large water reservoirs that may induce significant changes in the local environment. In this work, we describe the prevailing breeze mechanism in a Brazilian tropical hydropower reservoir to assess its impacts on local cloudiness and incoming surface solar irradiation. GOES-16 visible imagery, ISCCP database products, and ground measurement sites operated by INMET and LABREN/INPE provided data for the statistical analysis. We evaluate the cloudiness frequency assuming two distinct perspectives: spatial distribution by comparing cloudiness over the water surface and areas nearby its shores, and time analysis by comparing cloudiness prior and after reservoir completion. We also evaluated the solar irradiance enhancement over the water surface compared to the border and land areas surrounding the hydropower reservoir. The results pointed out daily average cloudiness increases moving away from the reservoir in any of the four cardinal directions. When looking at the afternoon-only cloudiness (14 h to 16 h local time), 4% fewer clouds were observed over the flooded area during summer (DJF). This difference reaches 8% during autumn (MAM) and spring (SON). Consequently, the irradiance enhancement at the water surface compared to external areas was around 1.75% for daily average and 4.59% for the afternoon-only average. Our results suggest that floating solar PV power plants in hydropower reservoirs can be an excellent option to integrate both renewable energy resources into a hybrid power generation due to the high solar irradiance in Brazilian territory combined with the prevailing breeze mechanism in large tropical water reservoirs. Full article
(This article belongs to the Special Issue Assessment of Renewable Energy Resources with Remote Sensing)
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28 pages, 9697 KiB  
Article
A Computational Workflow for Generating A Voxel-Based Design Approach Based on Subtractive Shading Envelopes and Attribute Information of Point Cloud Data
by Miktha Farid Alkadri, Francesco De Luca, Michela Turrin and Sevil Sariyildiz
Remote Sens. 2020, 12(16), 2561; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12162561 - 09 Aug 2020
Cited by 9 | Viewed by 4338
Abstract
This study proposes a voxel-based design approach based on the subtractive mechanism of shading envelopes and attributes information of point cloud data in tropical climates. In particular, the proposed method evaluates a volumetric sample of new buildings based on predefined shading performance criteria. [...] Read more.
This study proposes a voxel-based design approach based on the subtractive mechanism of shading envelopes and attributes information of point cloud data in tropical climates. In particular, the proposed method evaluates a volumetric sample of new buildings based on predefined shading performance criteria. With the support of geometric and radiometric information stored in point cloud, such as position (XYZ), color (RGB), and reflection intensity (I), an integrated computational workflow between passive design strategy and 3D scanning technology is developed. It aims not only to compensate for some pertinent aspects of the current 3D site modeling, such as vegetation and surrounding buildings, but also to investigate surface characteristics of existing contexts, such as visible sun vectors and material properties. These aspects are relevant for conducting a comprehensively environmental simulation, while averting negative microclimatic impacts when locating the new building into the existing context. Ultimately, this study may support architects for taking decision-making in conceptual design stage based on the real contextual conditions. Full article
(This article belongs to the Special Issue Assessment of Renewable Energy Resources with Remote Sensing)
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21 pages, 7012 KiB  
Article
Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island
by Jinwoong Park, Jihoon Moon, Seungmin Jung and Eenjun Hwang
Remote Sens. 2020, 12(14), 2271; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12142271 - 15 Jul 2020
Cited by 30 | Viewed by 3523
Abstract
Smart islands have focused on renewable energy sources, such as solar and wind, to achieve energy self-sufficiency. Because solar photovoltaic (PV) power has the advantage of less noise and easier installation than wind power, it is more flexible in selecting a location for [...] Read more.
Smart islands have focused on renewable energy sources, such as solar and wind, to achieve energy self-sufficiency. Because solar photovoltaic (PV) power has the advantage of less noise and easier installation than wind power, it is more flexible in selecting a location for installation. A PV power system can be operated more efficiently by predicting the amount of global solar radiation for solar power generation. Thus far, most studies have addressed day-ahead probabilistic forecasting to predict global solar radiation. However, day-ahead probabilistic forecasting has limitations in responding quickly to sudden changes in the external environment. Although multistep-ahead (MSA) forecasting can be used for this purpose, traditional machine learning models are unsuitable because of the substantial training time. In this paper, we propose an accurate MSA global solar radiation forecasting model based on the light gradient boosting machine (LightGBM), which can handle the training-time problem and provide higher prediction performance compared to other boosting methods. To demonstrate the validity of the proposed model, we conducted a global solar radiation prediction for two regions on Jeju Island, the largest island in South Korea. The experiment results demonstrated that the proposed model can achieve better predictive performance than the tree-based ensemble and deep learning methods. Full article
(This article belongs to the Special Issue Assessment of Renewable Energy Resources with Remote Sensing)
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17 pages, 9129 KiB  
Article
Characterizing Geological Heterogeneities for Geothermal Purposes through Combined Geophysical Prospecting Methods
by Cristina Sáez Blázquez, Pedro Carrasco García, Ignacio Martín Nieto, Miguel Ángel Maté-González, Arturo Farfán Martín and Diego González-Aguilera
Remote Sens. 2020, 12(12), 1948; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12121948 - 17 Jun 2020
Cited by 10 | Viewed by 2918
Abstract
Geothermal energy is becoming essential to deal with the catastrophic effect of climate change. Although the totality of the Earth’s crust allows the exploitation of shallow geothermal resources, it is important to identify those areas with higher thermal possibilities. In this sense, geophysical [...] Read more.
Geothermal energy is becoming essential to deal with the catastrophic effect of climate change. Although the totality of the Earth’s crust allows the exploitation of shallow geothermal resources, it is important to identify those areas with higher thermal possibilities. In this sense, geophysical prospecting plays a vital role in the recognition and estimation of potential geothermal resources. This research evaluates the geothermal conditions of a certain area located in the center of Spain. The evaluation is mainly based on geological and geophysical studies and, in particular, the Time Domain Electromagnetic Method and the Electrical Resistivity Tomography. Once we analyzed the geology and the historical thermal evidence near the study area, our geophysical results were used to define the geothermal possibilities from a double perspective. In relation to anomalous heat gradient, the identification of a fault and the contact with impermeable granitic materials at the depth of 180 m denotes a potential location for the extraction of groundwater. Regarding the common ground-source heat-pump uses, the analysis has allowed the determination of the most appropriate area for the location of the geothermal well field. Finally, the importance of accurately defining the position of the drillings was confirmed by using software GES-CAL. Full article
(This article belongs to the Special Issue Assessment of Renewable Energy Resources with Remote Sensing)
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17 pages, 22904 KiB  
Article
Real-Time Automatic Cloud Detection Using a Low-Cost Sky Camera
by Joaquín Alonso-Montesinos
Remote Sens. 2020, 12(9), 1382; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091382 - 27 Apr 2020
Cited by 17 | Viewed by 6931
Abstract
Characterizing the atmosphere is one of the most complex studies one can undertake due to the non-linearity and phenomenological variability. Clouds are also among the most variable atmospheric constituents, changing their size and shape over a short period of time. There are several [...] Read more.
Characterizing the atmosphere is one of the most complex studies one can undertake due to the non-linearity and phenomenological variability. Clouds are also among the most variable atmospheric constituents, changing their size and shape over a short period of time. There are several sectors in which the study of cloudiness is of vital importance. In the renewable field, the increasing development of solar technology and the emerging trend for constructing and operating solar plants across the earth’s surface requires very precise control systems that provide optimal energy production management. Similarly, airports are hubs where cloud coverage is required to provide high-precision periodic observations that inform airport operators about the state of the atmosphere. This work presents an autonomous cloud detection system, in real time, based on the digital image processing of a low-cost sky camera. An algorithm was developed to identify the clouds in the whole image using the relationships established between the channels of the RGB and Hue, Saturation, Value (HSV) color spaces. The system’s overall success rate is approximately 94% for all types of sky conditions; this is a novel development which makes it possible to identify clouds from a ground perspective without the use of radiometric parameters. Full article
(This article belongs to the Special Issue Assessment of Renewable Energy Resources with Remote Sensing)
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24 pages, 9859 KiB  
Article
Coastal Wind Measurements Using a Single Scanning LiDAR
by Susumu Shimada, Jay Prakash Goit, Teruo Ohsawa, Tetsuya Kogaki and Satoshi Nakamura
Remote Sens. 2020, 12(8), 1347; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081347 - 24 Apr 2020
Cited by 14 | Viewed by 4602
Abstract
A wind measurement campaign using a single scanning light detection and ranging (LiDAR) device was conducted at the Hazaki Oceanographical Research Station (HORS) on the Hazaki coast of Japan to evaluate the performance of the device for coastal wind measurements. The scanning LiDAR [...] Read more.
A wind measurement campaign using a single scanning light detection and ranging (LiDAR) device was conducted at the Hazaki Oceanographical Research Station (HORS) on the Hazaki coast of Japan to evaluate the performance of the device for coastal wind measurements. The scanning LiDAR was deployed on the landward end of the HORS pier. We compared the wind speed and direction data recorded by the scanning LiDAR to the observations obtained from a vertical profiling LiDAR installed at the opposite end of the pier, 400 m from the scanning LiDAR. The best practice for offshore wind measurements using a single scanning LiDAR was evaluated by comparing results from a total of nine experiments using several different scanning settings. A two-parameter velocity volume processing (VVP) method was employed to retrieve the horizontal wind speed and direction from the radial wind speed. Our experiment showed that, at the current offshore site with a negligibly small vertical wind speed component, the accuracy of the scanning LiDAR wind speeds and directions was sensitive to the azimuth angle setting, but not to the elevation angle setting. In addition to the validations for the 10-minute mean wind speeds and directions, the application of LiDARs for the measurement of the turbulence intensity (TI) was also discussed by comparing the results with observations obtained from a sonic anemometer, mounted at the seaward end of the HORS pier, 400 m from the scanning LiDAR. The standard deviation obtained from the scanning LiDAR measurement showed a greater fluctuation than that obtained from the sonic anemometer measurement. However, the difference between the scanning LiDAR and sonic measurements appeared to be within an acceptable range for the wind turbine design. We discuss the variations in data availability and accuracy based on an analysis of the carrier-to-noise ratio (CNR) distribution and the goodness of fit for curve fitting via the VVP method. Full article
(This article belongs to the Special Issue Assessment of Renewable Energy Resources with Remote Sensing)
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17 pages, 3530 KiB  
Article
Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area
by Román Mondragón, Joaquín Alonso-Montesinos, David Riveros-Rosas, Mauro Valdés, Héctor Estévez, Adriana E. González-Cabrera and Wolfgang Stremme
Remote Sens. 2020, 12(7), 1212; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071212 - 09 Apr 2020
Cited by 6 | Viewed by 2994
Abstract
Nowadays, it is of great interest to know and forecast the solar energy resource that will be constantly available in order to optimize its use. The generation of electrical energy using CSP (concentrated solar power) plants is mostly affected by atmospheric changes. Therefore, [...] Read more.
Nowadays, it is of great interest to know and forecast the solar energy resource that will be constantly available in order to optimize its use. The generation of electrical energy using CSP (concentrated solar power) plants is mostly affected by atmospheric changes. Therefore, forecasting solar irradiance is essential for planning a plant’s operation. Solar irradiance/atmospheric (clouds) interaction studies using satellite and sky images can help to prepare plant operators for solar surface irradiance fluctuations. In this work, we present three methodologies that allow us to estimate direct normal irradiance (DNI). The study was carried out at the Solar Irradiance Observatory (SIO) at the Geophysics Institute (UNAM) in Mexico City using corresponding images obtained with a sky camera and starting from a clear sky model. The multiple linear regression and polynomial regression models as well as the neural networks model designed in the present study, were structured to work under all sky conditions (cloudy, partly cloudy and cloudless), obtaining estimation results with 82% certainty for all sky types. Full article
(This article belongs to the Special Issue Assessment of Renewable Energy Resources with Remote Sensing)
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23 pages, 3019 KiB  
Article
Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models
by Guojiang Xiong, Jing Zhang, Dongyuan Shi, Lin Zhu, Xufeng Yuan and Gang Yao
Remote Sens. 2019, 11(23), 2795; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11232795 - 26 Nov 2019
Cited by 26 | Viewed by 2986
Abstract
Extracting accurate values for involved unknown parameters of solar photovoltaic (PV) models is very important for modeling PV systems. In recent years, the use of metaheuristic algorithms for this problem tends to be more popular and vibrant due to their efficacy in solving [...] Read more.
Extracting accurate values for involved unknown parameters of solar photovoltaic (PV) models is very important for modeling PV systems. In recent years, the use of metaheuristic algorithms for this problem tends to be more popular and vibrant due to their efficacy in solving highly nonlinear multimodal optimization problems. The whale optimization algorithm (WOA) is a relatively new and competitive metaheuristic algorithm. In this paper, an improved variant of WOA referred to as MCSWOA, is proposed to the parameter extraction of PV models. In MCSWOA, three improved components are integrated together: (i) Two modified search strategies named WOA/rand/1 and WOA/current-to-best/1 inspired by differential evolution are designed to balance the exploration and exploitation; (ii) a crossover operator based on the above modified search strategies is introduced to meet the search-oriented requirements of different dimensions; and (iii) a selection operator instead of the “generate-and-go” operator used in the original WOA is employed to prevent the population quality getting worse and thus to guarantee the consistency of evolutionary direction. The proposed MCSWOA is applied to five PV types. Both single diode and double diode models are used to model these five PV types. The good performance of MCSWOA is verified by various algorithms. Full article
(This article belongs to the Special Issue Assessment of Renewable Energy Resources with Remote Sensing)
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Other

Jump to: Editorial, Research

9 pages, 2402 KiB  
Letter
On the Land-Sea Contrast in the Surface Solar Radiation (SSR) in the Baltic Region
by Anders V. Lindfors, Axel Hertsberg, Aku Riihelä, Thomas Carlund, Jörg Trentmann and Richard Müller
Remote Sens. 2020, 12(21), 3509; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12213509 - 26 Oct 2020
Cited by 5 | Viewed by 2330
Abstract
The climatological surface solar radiation (SSR; also called global radiation), which is largely dependent on cloud conditions, is an important indicator of the solar energy production potential. In the Baltic area, previous studies have indicated lower cloud amounts over seas than over land, [...] Read more.
The climatological surface solar radiation (SSR; also called global radiation), which is largely dependent on cloud conditions, is an important indicator of the solar energy production potential. In the Baltic area, previous studies have indicated lower cloud amounts over seas than over land, in particular during the summer. However, the existing literature on the SSR climate or how it translates into solar energy potential has not paid much attention to how the SSR behaves quantitatively in relation to the coastline. In this paper, we have studied the climatological land–sea contrast of the SSR over the Baltic area. For this, we used two satellite climate data records, CLARA-A2 and SARAH-2, together with a coastline data base and ground-based pyranometer measurements of the SSR. We analyzed the behaviour of the climatological mean SSR over the period 2003–2013 as a function of the distance to the coastline. The results show that off-shore locations on average receive higher SSR than inland areas and that the land–sea contrast in the SSR is strongest during the summer. Furthermore, the land–sea contrast in the summer time SSR exhibits similar behavior in various parts of the Baltic. For CLARA-A2, which shows better agreement with the ground-based measurements than SARAH-2, the annual SSR is 8% higher 20 km off the coastline than 20 km inland. For summer, i.e., June–August, this difference is 10%. The observed land–sea contrast in the SSR is further shown to correspond closely to the behavior of clouds. Here, convective clouds play an important role as they tend to form over inland areas rather than over the seas during the summer part of the year. Full article
(This article belongs to the Special Issue Assessment of Renewable Energy Resources with Remote Sensing)
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