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Special Issue "Solar Radiation, Modelling and Remote Sensing"

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

Deadline for manuscript submissions: closed (31 October 2018).

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

Special Issue Editors

Guest Editor
Dr. Dimitris Kaskaoutis

Institute for Environmental Research & Sustainable Development, National Observatory of Athens, I. Metaxa & Vas. Pavlou, P. Penteli (Lofos Koufou), 15236 Athens, Greece
Website | E-Mail
Interests: solar radiation; aerosols; remote sensing; dust; meteorology; climatology
Guest Editor
Dr. Jesús Polo

Photovoltaic Solar Energy Unit,Renewable Energy Division (Energy Department), Ciemat,Avda Complutense, 40 28040 Madrid, Spain
Website | E-Mail
Interests: solar radiation; remote sensing;energy meteorology for solar energy applications; atmospheric aerosols

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 the reduction of greenhouse-gas emissions have shifted the global interest towards solar-energy exploitation for sustainable development and electricity demands. Solar radiation measurements are necessary in the assessment of potential solar energy resources, while their scarce spatial coverage renders solar radiation modelling and remote sensing necessary for atmospheric and energy applications. This special issue aims to review techniques for solar radiation measurements and modelling (solar-radiation networks, historical developments, technique comparisons, standard comparisons between models) and remote sensing using satellite and advanced statistical techniques like artificial neural networks for solar radiation and energy mapping from regional to global scales. Satellite remote sensing of solar radiation provides a better spatial coverage, and various methods have been developed on this issue, with the main disadvantages being the increased uncertainties and the required validations against ground-based measurements or modelling data. An accurate knowledge of the global solar radiation and its direct and diffuse components at the ground is very important for the design of solar power systems as well as for climatological and agricultural issues.

Dr. Dimitris Kaskaoutis
Dr. Jesús Polo
Guest Editors

Manuscript Submission Information

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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 1800 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
  • Photobiology
  • Remote sensing
  • Solar-radiation attenuation
  • Radiative forcing
  • Solar dimming/brightening
  • PV systems
  • Solar radiation/energy mapping

Published Papers (12 papers)

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Editorial

Jump to: Research, Review

Open AccessEditorial
Editorial for the Special Issue “Solar Radiation, Modeling, and Remote Sensing”
Remote Sens. 2019, 11(10), 1198; https://doi.org/10.3390/rs11101198
Received: 16 May 2019 / Accepted: 17 May 2019 / Published: 20 May 2019
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Abstract
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 measurements are growing in quality and density but they are still scarce enough to properly explain the [...] Read more.
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 measurements are growing in quality and density but they are still scarce enough to properly explain the spatial and temporal variability. As a consequence, great efforts are still being devoted to improving modeling and retrievals of solar radiation data. This Special Issue reviews techniques for solar radiation modeling and remote sensing using satellite and advanced statistical techniques for solar radiation. Satellite remote sensing of solar radiation provides better spatial coverage, and various methods have been presented on this issue covering several aspects: updated models for solar radiation modeling under clear sky conditions, new approaches for retrieving solar radiation from satellite imagery and validation against ground data, forecasting solar radiation, and modeling photosynthetically active radiation. Full article
(This article belongs to the Special Issue Solar Radiation, Modelling and Remote Sensing) Printed Edition available

Research

Jump to: Editorial, Review

Open AccessArticle
Impact of Insolation Data Source on Remote Sensing Retrievals of Evapotranspiration over the California Delta
Remote Sens. 2019, 11(3), 216; https://doi.org/10.3390/rs11030216
Received: 19 November 2018 / Revised: 21 December 2018 / Accepted: 4 January 2019 / Published: 22 January 2019
Cited by 1 | PDF Full-text (5182 KB) | HTML Full-text | XML Full-text
Abstract
The energy delivered to the land surface via insolation is a primary driver of evapotranspiration (ET)—the exchange of water vapor between the land and atmosphere. Spatially distributed ET products are in great demand in the water resource management community for real-time operations and [...] Read more.
The energy delivered to the land surface via insolation is a primary driver of evapotranspiration (ET)—the exchange of water vapor between the land and atmosphere. Spatially distributed ET products are in great demand in the water resource management community for real-time operations and sustainable water use planning. The accuracy and deliverability of these products are determined in part by the characteristics and quality of the insolation data sources used as input to the ET models. This paper investigates the practical utility of three different insolation datasets within the context of a satellite-based remote sensing framework for mapping ET at high spatiotemporal resolution, in an application over the Sacramento–San Joaquin Delta region in California. The datasets tested included one reanalysis product: The Climate System Forecast Reanalysis (CFSR) at 0.25° spatial resolution, and two remote sensing insolation products generated with geostationary satellite imagery: a product for the continental United States at 0.2°, developed by the University of Wisconsin Space Sciences and Engineering Center (SSEC) and a coarser resolution (1°) global Clouds and the Earth’s Radiant Energy System (CERES) product. The three insolation data sources were compared to pyranometer data collected at flux towers within the Delta region to establish relative accuracy. The satellite products significantly outperformed CFSR, with root-mean square errors (RMSE) of 2.7, 1.5, and 1.4 MJ·m−2·d−1 for CFSR, CERES, and SSEC, respectively, at daily timesteps. The satellite-based products provided more accurate estimates of cloud occurrence and radiation transmission, while the reanalysis tended to underestimate solar radiation under cloudy-sky conditions. However, this difference in insolation performance did not translate into comparable improvement in the ET retrieval accuracy, where the RMSE in daily ET was 0.98 and 0.94 mm d−1 using the CFSR and SSEC insolation data sources, respectively, for all the flux sites combined. The lack of a notable impact on the aggregate ET performance may be due in part to the predominantly clear-sky conditions prevalent in central California, under which the reanalysis and satellite-based insolation data sources have comparable accuracy. While satellite-based insolation data could improve ET retrieval in more humid regions with greater cloud-cover frequency, over the California Delta and climatologically similar regions in the western U.S., the CFSR data may suffice for real-time ET modeling efforts. Full article
(This article belongs to the Special Issue Solar Radiation, Modelling and Remote Sensing) Printed Edition available
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Open AccessArticle
Characterizing Variability of Solar Irradiance in San Antonio, Texas Using Satellite Observations of Cloudiness
Remote Sens. 2018, 10(12), 2016; https://doi.org/10.3390/rs10122016
Received: 29 October 2018 / Revised: 9 December 2018 / Accepted: 9 December 2018 / Published: 12 December 2018
Cited by 2 | PDF Full-text (2298 KB) | HTML Full-text | XML Full-text
Abstract
Since the main attenuation of solar irradiance reaching the earth’s surface is due to clouds, it has been hypothesized that global horizontal irradiance attenuation and its temporal variability at a given location could be characterized simply by cloud properties at that location. This [...] Read more.
Since the main attenuation of solar irradiance reaching the earth’s surface is due to clouds, it has been hypothesized that global horizontal irradiance attenuation and its temporal variability at a given location could be characterized simply by cloud properties at that location. This hypothesis is tested using global horizontal irradiance measurements at two stations in San Antonio, Texas, and satellite estimates of cloud types and cloud layers from the Geostationary Operational Environmental Satellite (GOES) Surface and Insolation Product. A modified version of an existing solar attenuation variability index, albeit having a better physical foundation, is used. The analysis is conducted for different cloud conditions and solar elevations. It is found that under cloudy-sky conditions, there is less attenuation under water clouds than those under opaque ice clouds (optically thick ice clouds) and multilayered clouds. For cloud layers, less attenuation was found for the low/mid layers than for the high layer. Cloud enhancement occurs more frequently for water clouds and less frequently for mixed phase and cirrus clouds and it occurs with similar frequency at all three levels. The temporal variability of solar attenuation is found to decrease with an increasing temporal sampling interval and to be largest for water clouds and smallest for multilayered and partly cloudy conditions. This work presents a first step towards estimating solar energy potential in the San Antonio area indirectly using available estimates of cloudiness from GOES satellites. Full article
(This article belongs to the Special Issue Solar Radiation, Modelling and Remote Sensing) Printed Edition available
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Open AccessFeature PaperArticle
Earth-Observation-Based Estimation and Forecasting of Particulate Matter Impact on Solar Energy in Egypt
Remote Sens. 2018, 10(12), 1870; https://doi.org/10.3390/rs10121870
Received: 24 October 2018 / Revised: 16 November 2018 / Accepted: 21 November 2018 / Published: 23 November 2018
Cited by 1 | PDF Full-text (12214 KB) | HTML Full-text | XML Full-text
Abstract
This study estimates the impact of dust aerosols on surface solar radiation and solar energy in Egypt based on Earth Observation (EO) related techniques. For this purpose, we exploited the synergy of monthly mean and daily post processed satellite remote sensing observations from [...] Read more.
This study estimates the impact of dust aerosols on surface solar radiation and solar energy in Egypt based on Earth Observation (EO) related techniques. For this purpose, we exploited the synergy of monthly mean and daily post processed satellite remote sensing observations from the MODerate resolution Imaging Spectroradiometer (MODIS), radiative transfer model (RTM) simulations utilizing machine learning, in conjunction with 1-day forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). As cloudy conditions in this region are rare, aerosols in particular dust, are the most common sources of solar irradiance attenuation, causing performance issues in the photovoltaic (PV) and concentrated solar power (CSP) plant installations. The proposed EO-based methodology is based on the solar energy nowcasting system (SENSE) that quantifies the impact of aerosol and dust on solar energy potential by using the aerosol optical depth (AOD) in terms of climatological values and day-to-day monitoring and forecasting variability from MODIS and CAMS, respectively. The forecast accuracy was evaluated at various locations in Egypt with substantial PV and CSP capacity installed and found to be within 5–12% of that obtained from the satellite observations, highlighting the ability to use such modelling approaches for solar energy management and planning (M&P). Particulate matter resulted in attenuation by up to 64–107 kWh/m2 for global horizontal irradiance (GHI) and 192–329 kWh/m2 for direct normal irradiance (DNI) annually. This energy reduction is climatologically distributed between 0.7% and 12.9% in GHI and 2.9% to 41% in DNI with the maximum values observed in spring following the frequent dust activity of Khamaseen. Under extreme dust conditions the AOD is able to exceed 3.5 resulting in daily energy losses of more than 4 kWh/m2 for a 10 MW system. Such reductions are able to cause financial losses that exceed the daily revenue values. This work aims to show EO capabilities and techniques to be incorporated and utilized in solar energy studies and applications in sun-privileged locations with permanent aerosol sources such as Egypt. Full article
(This article belongs to the Special Issue Solar Radiation, Modelling and Remote Sensing) Printed Edition available
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Open AccessArticle
Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq
Remote Sens. 2018, 10(10), 1651; https://doi.org/10.3390/rs10101651
Received: 27 August 2018 / Revised: 26 September 2018 / Accepted: 15 October 2018 / Published: 17 October 2018
Cited by 4 | PDF Full-text (7605 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Several sectors need global horizontal irradiance (GHI) data for various purposes. However, the availability of a long-term time series of high quality in situ GHI measurements is limited. Therefore, several studies have tried to estimate GHI by re-analysing climate data or satellite images. [...] Read more.
Several sectors need global horizontal irradiance (GHI) data for various purposes. However, the availability of a long-term time series of high quality in situ GHI measurements is limited. Therefore, several studies have tried to estimate GHI by re-analysing climate data or satellite images. Validation is essential for the later use of GHI data in the regions with a scarcity of ground-recorded data. This study contributes to previous studies that have been carried out in the past to validate HelioClim-3 version 5 (HC3v5) and the Copernicus Atmosphere Monitoring Service, using radiation service version 3 (CRSv3) data of hourly GHI from satellite-derived datasets (SDD) with nine ground stations in northeast Iraq, which have not been used previously. The validation is carried out with station data at the pixel locations and two other data points in the vicinity of each station, which is something that is rarely seen in the literature. The temporal and spatial trends of the ground data are well captured by the two SDDs. Correlation ranges from 0.94 to 0.97 in all-sky and clear-sky conditions in most cases, while for cloudy-sky conditions, it is between 0.51–0.72 and 0.82–0.89 for the clearness index. The bias is negative for most of the cases, except for three positive cases. It ranges from −7% to 4%, and −8% to 3% for the all-sky and clear-sky conditions, respectively. For cloudy-sky conditions, the bias is positive, and differs from one station to another, from 16% to 85%. The root mean square error (RMSE) ranges between 12–20% and 8–12% for all-sky and clear-sky conditions, respectively. In contrast, the RMSE range is significantly higher in cloudy-sky conditions: above 56%. The bias and RMSE for the clearness index are nearly the same as those for the GHI for all-sky conditions. The spatial variability of hourly GHI SDD differs only by 2%, depending on the station location compared to the data points around each station. The variability of two SDDs is quite similar to the ground data, based on the mean and standard deviation of hourly GHI in a month. Having station data at different timescales and the small number of stations with GHI records in the region are the main limitations of this analysis. Full article
(This article belongs to the Special Issue Solar Radiation, Modelling and Remote Sensing) Printed Edition available
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Open AccessArticle
Improvement in Surface Solar Irradiance Estimation Using HRV/MSG Data
Remote Sens. 2018, 10(8), 1288; https://doi.org/10.3390/rs10081288
Received: 10 July 2018 / Revised: 10 August 2018 / Accepted: 13 August 2018 / Published: 15 August 2018
Cited by 5 | PDF Full-text (5117 KB) | HTML Full-text | XML Full-text
Abstract
The Advanced Model for the Estimation of Surface Solar Irradiance (AMESIS) was developed at the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy (IMAA-CNR) to derive surface solar irradiance from SEVIRI radiometer on board the MSG geostationary satellite. [...] Read more.
The Advanced Model for the Estimation of Surface Solar Irradiance (AMESIS) was developed at the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy (IMAA-CNR) to derive surface solar irradiance from SEVIRI radiometer on board the MSG geostationary satellite. The operational version of AMESIS has been running continuously at IMAA-CNR over all of Italy since 2017 in support to the monitoring of photovoltaic plants. The AMESIS operative model provides two different estimations of the surface solar irradiance: one is obtained considering only the low-resolution channels (SSI_VIS), while the other also takes into account the high-resolution HRV channel (SSI_HRV). This paper shows the difference between these two products against simultaneous ground-based observations from a network of 63 pyranometers for different sky conditions (clear, overcast and partially cloudy). Comparable statistical scores have been obtained for both AMESIS products in clear and cloud situation. In terms of bias and correlation coefficient over partially cloudy sky, better performances are found for SSI_HRV (0.34 W/m2 and 0.995, respectively) than SSI_VIS (−33.69 W/m2 and 0.862) at the expense of the greater run-time necessary to process HRV data channel. Full article
(This article belongs to the Special Issue Solar Radiation, Modelling and Remote Sensing) Printed Edition available
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Open AccessArticle
Modeling Photosynthetically Active Radiation from Satellite-Derived Estimations over Mainland Spain
Remote Sens. 2018, 10(6), 849; https://doi.org/10.3390/rs10060849
Received: 4 April 2018 / Revised: 23 May 2018 / Accepted: 24 May 2018 / Published: 30 May 2018
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Abstract
A model based on the known high correlation between photosynthetically active radiation (PAR) and global horizontal irradiance (GHI) was implemented to estimate PAR from GHI measurements in this present study. The model has been developed using satellite-derived GHI and PAR estimations. Both variables [...] Read more.
A model based on the known high correlation between photosynthetically active radiation (PAR) and global horizontal irradiance (GHI) was implemented to estimate PAR from GHI measurements in this present study. The model has been developed using satellite-derived GHI and PAR estimations. Both variables can be estimated using Kato bands, provided by Satellite Application Facility on Climate Monitoring (CM-SAF), and its ratio may be used as the variable of interest in order to obtain the model. The study area, which was located in mainland Spain, has been split by cluster analysis into regions with similar behavior, according to this ratio. In each of these regions, a regression model estimating PAR from GHI has been developed. According to the analysis, two regions are distinguished in the study area. These regions belong to the two climates dominating the territory: an Oceanic climate on the northern edge; and a Mediterranean climate with hot summer in the rest of the study area. The models obtained for each region have been checked against the ground measurements, providing correlograms with determination coefficients higher than 0.99. Full article
(This article belongs to the Special Issue Solar Radiation, Modelling and Remote Sensing) Printed Edition available
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Open AccessArticle
Nowcasting Surface Solar Irradiance with AMESIS via Motion Vector Fields of MSG-SEVIRI Data
Remote Sens. 2018, 10(6), 845; https://doi.org/10.3390/rs10060845
Received: 3 May 2018 / Revised: 25 May 2018 / Accepted: 27 May 2018 / Published: 29 May 2018
Cited by 7 | PDF Full-text (13710 KB) | HTML Full-text | XML Full-text
Abstract
In this study, we compare different nowcasting techniques based upon the calculation of motion vector fields derived from spectral channels of Meteosat Second Generation—Spinning Enhanced Visible and InfraRed Imager (MSG-SEVIRI). The outputs of the nowcasting techniques are used as inputs to the Advanced [...] Read more.
In this study, we compare different nowcasting techniques based upon the calculation of motion vector fields derived from spectral channels of Meteosat Second Generation—Spinning Enhanced Visible and InfraRed Imager (MSG-SEVIRI). The outputs of the nowcasting techniques are used as inputs to the Advanced Model for Estimation of Surface solar Irradiance from Satellite (AMESIS), for predicting surface solar irradiance up to 2 h in advance. In particular, the first part of the methodology consists in projecting the time evolution of each MSG-SEVIRI channel (for every pixel in the spatial domain) through extrapolation of a displacement vector field obtained by matching similar patterns within two successive MSG-SEVIRI data images. Different ways to implement the above method result in substantial differences in the predicted trajectory, leading to different performances depending on the time interval of interest. All the nowcasting techniques considered here systematically outperform the simple persistence method for all MSG-SEVIRI channels and for each case study used in this work; importantly, this occurs across the entire 2 h period of the forecast. In the second part of the algorithm, the predicted irradiance maps computed with AMESIS from the forecasted radiances, are shown to be in good agreement with irradiances derived from MSG measured radiances and improve on numerical weather model predictions, thus providing a feasible alternative for nowcasting surface solar radiation. The results show that the mean values for correlation, bias, and root mean square error vary across the time interval, ranging between 0.94, −1 W/m 2 , 61 W/m 2 after 15 min, and 0.73, −18 W/m 2 , 147 W/m 2 after 2 h, respectively. Full article
(This article belongs to the Special Issue Solar Radiation, Modelling and Remote Sensing) Printed Edition available
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Graphical abstract

Open AccessArticle
High Turbidity Solis Clear Sky Model: Development and Validation
Remote Sens. 2018, 10(3), 435; https://doi.org/10.3390/rs10030435
Received: 8 January 2018 / Revised: 15 February 2018 / Accepted: 8 March 2018 / Published: 10 March 2018
Cited by 4 | PDF Full-text (31050 KB) | HTML Full-text | XML Full-text
Abstract
The Solis clear sky model is a spectral scheme based on radiative transfer calculations and the Lambert–Beer relation. Its broadband version is a simplified fast analytical version; it is limited to broadband aerosol optical depths lower than 0.45, which is a weakness when [...] Read more.
The Solis clear sky model is a spectral scheme based on radiative transfer calculations and the Lambert–Beer relation. Its broadband version is a simplified fast analytical version; it is limited to broadband aerosol optical depths lower than 0.45, which is a weakness when applied in countries with very high turbidity such as China or India. In order to extend the use of the original simplified version of the model for high turbidity values, we developed a new version of the broadband Solis model based on radiative transfer calculations, valid for turbidity values up to 7, for the three components, global, beam, and diffuse, and for the four aerosol types defined by Shettle and Fenn. A validation of low turbidity data acquired in Geneva shows slightly better results than the previous version. On data acquired at sites presenting higher turbidity data, the bias stays within ±4% for the beam and the global irradiances, and the standard deviation around 5% for clean and stable condition data and around 12% for questionable data and variable sky conditions. Full article
(This article belongs to the Special Issue Solar Radiation, Modelling and Remote Sensing) Printed Edition available
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Open AccessArticle
Validation of the SARAH-E Satellite-Based Surface Solar Radiation Estimates over India
Remote Sens. 2018, 10(3), 392; https://doi.org/10.3390/rs10030392
Received: 15 December 2017 / Revised: 28 February 2018 / Accepted: 2 March 2018 / Published: 3 March 2018
Cited by 4 | PDF Full-text (4861 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
We evaluate the accuracy of the satellite-based surface solar radiation dataset called Surface Solar Radiation Data Set - Heliosat (SARAH-E) against in situ measurements over a variety of sites in India between 1999 and 2014. We primarily evaluate the daily means of surface [...] Read more.
We evaluate the accuracy of the satellite-based surface solar radiation dataset called Surface Solar Radiation Data Set - Heliosat (SARAH-E) against in situ measurements over a variety of sites in India between 1999 and 2014. We primarily evaluate the daily means of surface solar radiation. The results indicate that SARAH-E consistently overestimates surface solar radiation, with a mean bias of 21.9 W/m2. The results are complicated by the fact that the estimation bias is stable between 1999 and 2009 with a mean of 19.6 W/m2 but increases sharply thereafter as a result of rapidly decreasing (dimming) surface measurements of solar radiation. In addition, between 1999 and 2009, both in situ measurements and SARAH-E estimates described a statistically significant (at 95% confidence interval) trend of approximately −0.6 W/m2/year, but diverged strongly afterward. We investigated the cause of decreasing solar radiation at one site (Pune) by simulating clear-sky irradiance with local measurements of water vapor and aerosols as input to a radiative transfer model. The relationship between simulated and measured irradiance appeared to change post-2009, indicating that measured changes in the clear-sky aerosol loading are not sufficient to explain the rapid dimming in measured total irradiance. Besides instrumentation biases, possible explanations in the diverging measurements and retrievals of solar radiation may be found in the aerosol climatology used for SARAH-E generation. However, at present, we have insufficient data to conclusively identify the cause of the increasing retrieval bias. Users of the datasets are advised to be aware of the increasing bias when using the post-2009 data. Full article
(This article belongs to the Special Issue Solar Radiation, Modelling and Remote Sensing) Printed Edition available
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Open AccessArticle
Retrieval of Reflected Shortwave Radiation at the Top of the Atmosphere Using Himawari-8/AHI Data
Remote Sens. 2018, 10(2), 213; https://doi.org/10.3390/rs10020213
Received: 30 November 2017 / Revised: 8 January 2018 / Accepted: 25 January 2018 / Published: 1 February 2018
Cited by 4 | PDF Full-text (54911 KB) | HTML Full-text | XML Full-text
Abstract
This study developed a retrieval algorithm for reflected shortwave radiation at the top of the atmosphere (RSR). This algorithm is based on Himawari-8/AHI (Advanced Himawari Imager) whose sensor characteristics and observation area are similar to the next-generation Geostationary Korea Multi-Purpose Satellite/Advanced Meteorological Imager [...] Read more.
This study developed a retrieval algorithm for reflected shortwave radiation at the top of the atmosphere (RSR). This algorithm is based on Himawari-8/AHI (Advanced Himawari Imager) whose sensor characteristics and observation area are similar to the next-generation Geostationary Korea Multi-Purpose Satellite/Advanced Meteorological Imager (GK-2A/AMI). This algorithm converts the radiance into reflectance for six shortwave channels and retrieves the RSR with a regression coefficient look-up-table according to geometry of the solar-viewing (solar zenith angle, viewing zenith angle, and relative azimuth angle) and atmospheric conditions (surface type and absence/presence of clouds), and removed sun glint with high uncertainty. The regression coefficients were calculated using numerical experiments from the radiative transfer model (SBDART), and ridge regression for broadband albedo at the top of the atmosphere (TOA albedo) and narrowband reflectance considering anisotropy. The retrieved RSR were validated using Terra, Aqua, and S-NPP/CERES data on the 15th day of every month from July 2015 to February 2017. The coefficient of determination (R2) between AHI and CERES for scene analysis was higher than 0.867 and the Bias and root mean square error (RMSE) were −21.34–5.52 and 51.74–59.28 Wm−2. The R2, Bias, and RMSE for the all cases were 0.903, −2.34, and 52.12 Wm−2, respectively. Full article
(This article belongs to the Special Issue Solar Radiation, Modelling and Remote Sensing) Printed Edition available
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Review

Jump to: Editorial, Research

Open AccessReview
Modeling Solar Radiation in the Forest Using Remote Sensing Data: A Review of Approaches and Opportunities
Remote Sens. 2018, 10(5), 694; https://doi.org/10.3390/rs10050694
Received: 22 March 2018 / Revised: 24 April 2018 / Accepted: 26 April 2018 / Published: 1 May 2018
Cited by 4 | PDF Full-text (4305 KB) | HTML Full-text | XML Full-text
Abstract
Solar radiation, the radiant energy from the sun, is a driving variable for numerous ecological, physiological, and other life-sustaining processes in the environment. Traditional methods to quantify solar radiation are done either directly (e.g., quantum sensors), or indirectly (e.g., hemispherical photography). This study, [...] Read more.
Solar radiation, the radiant energy from the sun, is a driving variable for numerous ecological, physiological, and other life-sustaining processes in the environment. Traditional methods to quantify solar radiation are done either directly (e.g., quantum sensors), or indirectly (e.g., hemispherical photography). This study, however, evaluates literature which utilized remote sensing (RS) technologies to estimate various forms of solar radiation or components, thereof under or within forest canopies. Based on the review, light detection and ranging (LiDAR) has, so far, been preferably used for modeling light under tree canopies. Laser system’s capability of generating 3D canopy structure at high spatial resolution makes it a reasonable choice as a source of spatial information about light condition in various parts of forest ecosystem. The majority of those using airborne laser system (ALS) commonly adopted the volumetric-pixel (voxel) method or the laser penetration index (LPI) for modeling the radiation, while terrestrial laser system (TLS) is preferred for canopy reconstruction and simulation. Furthermore, most of the studies focused only on global radiation, and very few on the diffuse fraction. It was also found out that most of these analyses were performed in the temperate zone, with a smaller number of studies made in tropical areas. Nonetheless, with the continuous advancement of technology and the RS datasets becoming more accessible and less expensive, these shortcomings and other difficulties of estimating the spatial variation of light in the forest are expected to diminish. Full article
(This article belongs to the Special Issue Solar Radiation, Modelling and Remote Sensing) Printed Edition available
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