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Remote Sensing of Solar Surface Radiation
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Remote Sensing of Solar Surface Radiation

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

Deadline for manuscript submissions: closed (30 June 2015) | Viewed by 62786

Special Issue Editor

Dr. Richard Müller

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Guest Editor
German Weather Service, Frankfurter Street 135, 63067 Offenbach, Germany
Interests: remote sensing of surface radiation; clouds and aerosols; sensor calibration; methods for “merging” in situ data with remote sensing data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Accurate information of solar surface radiation is needed in many fields, e.g. climate monitoring, solar energy, water and energy cycles, land surface studies, Earth system modeling. Satellite based surface radiation data is the only observational source of information in many regions of the world, which illustrates the importance of satellite based solar surface radiation. The accurate retrieval of solar surface radiation is therefore an important task and challenge, and the basic challenge is the same for all methods. Solar surface radiation is not measured directly, but has to be derived from the reflections measured by the satellite instruments. The complex interactions of radiation with the atmosphere and surface require the development and application of special methods. Such development of respective remote sensing methods started shortly after the launch of the first operational meteorological satellites. Since that time progress has been quite remarkable, but still significant improvements of the methods are needed in order to gain better accuracy and precision which is necessary for many application fields. The new generation of satellites offers more information about atmospheric states, which can be further explored for better accuracy. However, for many applications (e.g., climate monitoring, water energy cycles), methods are also needed which can be applied to the older satellites that usually have fewer spectral channels and a lower resolution. For these satellites, the specific challenge is to achieve high accuracy with fewer channels and to deal with calibration issues, such as gaps and flaws in satellite raw data.

We would like to invite you to submit articles about your recent research with respect to the following topics.

  • Remote Sensing of direct irradiance: Methods and evaluations.
  • Remote Sensing of global irradiance (solar surface irradiance): Methods and evaluations
  • Remote Sensing of daylight and PAR: Methods and evaluations.
  • Remote Sensing of UV: Methods and evaluations.
  • Remote Sensing of spectrally resolved surface irradiance: Methods and evaluations.
  • Remote Sensing of surface albedo: Methods and evaluations.
  • Comparison and evaluation of different remote sensing methods.
  • Improvement and evaluation of input data needed for the retrieval of solar surface irradiance.
  • Review articles covering one or more of these topics are also welcome.

Authors are required to check and follow specific Instructions to Authors, see https://dl.dropboxusercontent.com/u/165068305/Remote_Sensing-Additional_Instructions.pdf.

Dr. Richard Müller
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.

Published Papers (8 papers)

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Research

1599 KiB  
Article
Use of MODIS Images to Quantify the Radiation and Energy Balances in the Brazilian Pantanal
by Antônio H. de C. Teixeira, Carlos R. Padovani, Ricardo G. Andrade, Janice F. Leivas, Daniel De C. Victoria and Sergio Galdino
Remote Sens. 2015, 7(11), 14597-14619; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71114597 - 04 Nov 2015
Cited by 16 | Viewed by 6286
Abstract
MODIS images during the year 2012 were used for modelling of the radiation and energy balance components with the application of the SAFER algorithm (Simple Algorithm for Evapotranspiration Retrieving) in the Brazilian Pantanal area. Pixels from the main sub-regions of Barão de Melgaço [...] Read more.
MODIS images during the year 2012 were used for modelling of the radiation and energy balance components with the application of the SAFER algorithm (Simple Algorithm for Evapotranspiration Retrieving) in the Brazilian Pantanal area. Pixels from the main sub-regions of Barão de Melgaço (BR), Paiaguás (PA) and Nhecolândia (NH) were extracted in order to process microclimatic comparisons. In general, the net radiation (Rn) relied much more on the global solar radiation (RG) levels than on water conditions and ecosystem types, in accordance with the low Rn standard deviation values. The fraction of the available energy used as latent heat flux (λE) were, on average, 65, 50 and 49% for the BR, PA and NH sub-regions, respectively. Horizontal heat advection, identified by the negative values of sensible heat flux (H), made several pixels with λE values higher than those for Rn in the middle of the year. Taking the evaporative fraction (Ef) as a surface moisture indicator, the Tree-Lined Savanna (TLS) was considered the moister ecosystem class, with 58% of the available energy being used as λE, while the driest one was the modified ecosystem Anthropogenic Changes (AC), presenting a λE/Rn fraction of 0.46. According to the spatial and temporal consistencies, and after comparisons with other previous point and large-scale studies, the SAFER algorithm proved to have sensibility to quantify and compare the large-scale radiation and energy balance components in the different ecosystems of the Brazilian Pantanal. The algorithm is useful for monitoring the energy exchange dynamics among the different terrestrial and aquatic ecosystem types throughout the seasons of the year. Full article
(This article belongs to the Special Issue Remote Sensing of Solar Surface Radiation)
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779 KiB  
Article
CMSAF Radiation Data: New Possibilities for Climatological Applications in the Czech Republic
by Michal Žák, Jiří Mikšovský and Petr Pišoft
Remote Sens. 2015, 7(11), 14445-14457; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71114445 - 30 Oct 2015
Cited by 8 | Viewed by 6584
Abstract
Satellite Application Facility on Climate Monitoring (CMSAF) data have been studied in the Czech Republic for approximately 10 years. Initially, validation studies were conducted, particularly regarding the incoming solar radiation product and cloudiness data. The main focus of these studies was the surface [...] Read more.
Satellite Application Facility on Climate Monitoring (CMSAF) data have been studied in the Czech Republic for approximately 10 years. Initially, validation studies were conducted, particularly regarding the incoming solar radiation product and cloudiness data. The main focus of these studies was the surface incoming shortwave (SIS) radiation data. This paper first briefly describes the validation of CMSAF SIS data for the period of 1989–2009. The main focus is on the use and possible applications of CMSAF data. It is shown that maps of SIS radiation in combination with surface data may be useful for solar power plant operators as well as for assessing the climate variability in the Czech Republic during different years and seasons. This demonstrates that the CMSAF data can improve our understanding of local climate, especially in regions lacking traditional surface observations and/or in border regions with a scarcity of stations in the neighboring countryside. Furthermore, data from the recently released SARAH (Surface Solar Radiation Data Set-Heliosat) dataset (1983–2013) are also briefly described and their use for trend computing is demonstrated. Finally, an outlook is given in terms of further possibilities for using CMSAF data in the Czech Republic. Full article
(This article belongs to the Special Issue Remote Sensing of Solar Surface Radiation)
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2216 KiB  
Article
Net Surface Shortwave Radiation from GOES Imagery—Product Evaluation Using Ground-Based Measurements from SURFRAD
by Anand K. Inamdar and Pierre C. Guillevic
Remote Sens. 2015, 7(8), 10788-10814; https://0-doi-org.brum.beds.ac.uk/10.3390/rs70810788 - 21 Aug 2015
Cited by 22 | Viewed by 9484
Abstract
The Earth’s surface net radiation controls the energy and water exchanges between the Earth’s surface and the atmosphere, and can be derived from satellite observations. The ability to monitor the net surface radiation over large areas at high spatial and temporal resolution is [...] Read more.
The Earth’s surface net radiation controls the energy and water exchanges between the Earth’s surface and the atmosphere, and can be derived from satellite observations. The ability to monitor the net surface radiation over large areas at high spatial and temporal resolution is essential for many applications, such as weather forecasting, short-term climate prediction or water resources management. The objective of this paper is to derive the net surface radiation in the shortwave domain at high temporal (half-hourly) and spatial resolution (~1 km) using visible imagery from Geostationary Operational Environmental Satellite (GOES). The retrieval algorithm represents an adaptation to GOES data of a standard algorithm initially developed for the NASA-operated Clouds and Earth’s Radiant Energy System (CERES) scanner. The methodology relies on: (1) the estimation of top of atmosphere shortwave radiation from GOES spectral measurements; and (2) the calculation of net surface shortwave (SW) radiation accounting for atmospheric effects. Comparison of GOES-retrieved net surface shortwave radiation with ground-measurements at the National Oceanic and Atmospheric Administration’s (NOAA) Surface Radiation (SURFRAD) stations yields very good agreement with average bias lower than 5 W·m−2 and root mean square difference around 70 W·m−2. The algorithm performance is usually higher over areas characterized by low spatial variability in term of land cover type and surface biophysical properties. The technique does not involve retrieval and assessment of cloud properties and can be easily adapted to other meteorological satellites around the globe. Full article
(This article belongs to the Special Issue Remote Sensing of Solar Surface Radiation)
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749 KiB  
Article
Validation of the Surface Downwelling Solar Irradiance Estimates of the HelioClim-3 Database in Egypt
by Yehia Eissa, Mohamed Korany, Youva Aoun, Mohamed Boraiy, Magdy M. Abdel Wahab, Stephane C. Alfaro, Philippe Blanc, Mossad El-Metwally, Hosni Ghedira, Katja Hungershoefer and Lucien Wald
Remote Sens. 2015, 7(7), 9269-9291; https://0-doi-org.brum.beds.ac.uk/10.3390/rs70709269 - 21 Jul 2015
Cited by 40 | Viewed by 7956
Abstract
HelioClim-3 (HC3) is a database providing time series of the surface downwelling solar irradiance that are computed from images of the Meteosat satellites. This paper presents the validation results of the hourly global horizontal irradiance (GHI) and direct normal irradiance (DNI), i.e., [...] Read more.
HelioClim-3 (HC3) is a database providing time series of the surface downwelling solar irradiance that are computed from images of the Meteosat satellites. This paper presents the validation results of the hourly global horizontal irradiance (GHI) and direct normal irradiance (DNI), i.e., beam irradiance at normal incidence, of versions four and five of HC3 at seven Egyptian sites. The validation is performed for all-sky conditions, as well as cloud-free conditions. Both versions of HC3 provide similar performances whatever the conditions. Another comparison is made with the estimates provided by the McClear database that is restricted to cloud-free conditions. All databases capture well the temporal variability of the GHI in all conditions, McClear being superior for cloud-free cases. In cloud-free conditions for the GHI, the relative root mean square error (RMSE) are fairly similar, ranging from 6% to 15%; both HC3 databases exhibit a smaller bias than McClear. McClear offers an overall better performance for the cloud-free DNI estimates. For all-sky conditions, the relative RMSE for GHI ranges from 10% to 22%, except one station, while, for the DNI, the results are not so good for the two stations with DNI measurements. Full article
(This article belongs to the Special Issue Remote Sensing of Solar Surface Radiation)
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3338 KiB  
Article
Short-Term Forecasting of Surface Solar Irradiance Based on Meteosat-SEVIRI Data Using a Nighttime Cloud Index
by Annette Hammer, Jan Kühnert, Kailash Weinreich and Elke Lorenz
Remote Sens. 2015, 7(7), 9070-9090; https://0-doi-org.brum.beds.ac.uk/10.3390/rs70709070 - 17 Jul 2015
Cited by 21 | Viewed by 7818 | Correction
Abstract
The cloud index is a key parameter of the Heliosat method. This method is widely used to calculate solar irradiance on the Earth’s surface from Meteosat visible channel images. Moreover, cloud index images are the basis of short-term forecasting of solar irradiance and [...] Read more.
The cloud index is a key parameter of the Heliosat method. This method is widely used to calculate solar irradiance on the Earth’s surface from Meteosat visible channel images. Moreover, cloud index images are the basis of short-term forecasting of solar irradiance and photovoltaic power production. For this purpose, cloud motion vectors are derived from consecutive images, and the motion of clouds is extrapolated to obtain forecasted cloud index images. The cloud index calculation is restricted to the daylight hours, as long as SEVIRI HR-VIS images are used. Hence, this forecast method cannot be used before sunrise. In this paper, a method is introduced that can be utilized a few hours before sunrise. The cloud information is gained from the brightness temperature difference (BTD) of the 10.8 µm and 3.9 µm SEVIRI infrared channels. A statistical relation is developed to assign a cloud index value to either the BTD or the brightness temperature T10:8, depending on the cloud class to which the pixel belongs (fog and low stratus, clouds with temperatures less than 232 K, other clouds). Images are composed of regular HR-VIS cloud index values that are used to the east of the terminator and of nighttime BTD-derived cloud index values used to the west of the terminator, where the Sun has not yet risen. The motion vector algorithm is applied to the images and delivers a forecast of irradiance at sunrise and in the morning. The forecasted irradiance is validated with ground measurements of global horizontal irradiance, and the advantage of the new approach is shown. The RMSE of forecasted irradiance based on the presented nighttime cloud index for the morning hours is between 3 and 70 W/m2, depending on the time of day. This is an improvement against the previous precision range of the forecast based on the daytime cloud index between 70 and 85 W/m2. Full article
(This article belongs to the Special Issue Remote Sensing of Solar Surface Radiation)
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1504 KiB  
Article
Digging the METEOSAT Treasure—3 Decades of Solar Surface Radiation
by Richard Müller, Uwe Pfeifroth, Christine Träger-Chatterjee, Jörg Trentmann and Roswitha Cremer
Remote Sens. 2015, 7(6), 8067-8101; https://0-doi-org.brum.beds.ac.uk/10.3390/rs70608067 - 18 Jun 2015
Cited by 118 | Viewed by 9481
Abstract
Solar surface radiation data of high quality is essential for the appropriate monitoring and analysis of the Earth's radiation budget and the climate system. Further, they are crucial for the efficient planning and operation of solar energy systems. However, well maintained surface measurements [...] Read more.
Solar surface radiation data of high quality is essential for the appropriate monitoring and analysis of the Earth's radiation budget and the climate system. Further, they are crucial for the efficient planning and operation of solar energy systems. However, well maintained surface measurements are rare in many regions of the world and over the oceans. There, satellite derived information is the exclusive observational source. This emphasizes the important role of satellite based surface radiation data. Within this scope, the new satellite based CM-SAF SARAH (Solar surfAce RAdiation Heliosat) data record is discussed as well as the retrieval method used. The SARAH data are retrieved with the sophisticated SPECMAGIC method, which is based on radiative transfer modeling. The resulting climate data of solar surface irradiance, direct irradiance (horizontal and direct normal) and clear sky irradiance are covering 3 decades. The SARAH data set is validated with surface measurements of the Baseline Surface Radiation Network (BSRN) and of the Global Energy and Balance Archive (GEBA). Comparison with BSRN data is performed in order to estimate the accuracy and precision of the monthly and daily means of solar surface irradiance. The SARAH solar surface irradiance shows a bias of 1.3 \(W/m^2\) and a mean absolute bias (MAB) of 5.5 \(W/m^2\) for monthly means. For direct irradiance the bias and MAB is 1 \(W/m^2\) and 8.2 \(W/m^2\) respectively. Thus, the uncertainty of the SARAH data is in the range of the uncertainty of ground based measurements. In order to evaluate the uncertainty of SARAH based trend analysis the time series of SARAH monthly means are compared to GEBA. It has been found that SARAH enables the analysis of trends with an uncertainty of 1 \(W/m^2/dec\); a remarkable good result for a satellite based climate data record. SARAH has been also compared to its legacy version, the satellite based CM-SAF MVIRI climate data record. Overall, SARAH shows a significant higher accuracy and homogeneity than its legacy version. With its high accuracy and temporal and spatial resolution SARAH is well suited for regional climate monitoring and analysis as well as for solar energy applications. Full article
(This article belongs to the Special Issue Remote Sensing of Solar Surface Radiation)
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26779 KiB  
Article
Validation of CM SAF Surface Solar Radiation Datasets over Finland and Sweden
by Aku Riihelä, Thomas Carlund, Jörg Trentmann, Richard Müller and Anders V. Lindfors
Remote Sens. 2015, 7(6), 6663-6682; https://0-doi-org.brum.beds.ac.uk/10.3390/rs70606663 - 26 May 2015
Cited by 38 | Viewed by 7577
Abstract
Accurate determination of the amount of incoming solar radiation at Earth’s surface is important for both climate studies and solar power applications. Satellite-based datasets of solar radiation offer wide spatial and temporal coverage, but careful validation of their quality is a necessary prerequisite [...] Read more.
Accurate determination of the amount of incoming solar radiation at Earth’s surface is important for both climate studies and solar power applications. Satellite-based datasets of solar radiation offer wide spatial and temporal coverage, but careful validation of their quality is a necessary prerequisite for reliable utilization. Here we study the retrieval quality of one polar-orbiting satellite-based dataset (CLARA-A1) and one geostationary satellite-based dataset (SARAH), using in situ observations of solar radiation from the Finnish and Swedish meteorological measurement networks as reference. Our focus is on determining dataset quality over high latitudes as well as evaluating daily mean retrievals, both of which are aspects that have drawn little focus in previous studies. We find that both datasets are generally capable of retrieving the levels and seasonal cycles of solar radiation in Finland and Sweden well, with some limitations. SARAH exhibits a slight negative bias and increased retrieval uncertainty near the coverage edge, but in turn offers better precision (less scatter) in the daily mean retrievals owing to the high sampling rate of geostationary imaging. Full article
(This article belongs to the Special Issue Remote Sensing of Solar Surface Radiation)
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5772 KiB  
Article
Comparative Assessment of Satellite-Retrieved Surface Net Radiation: An Examination on CERES and SRB Datasets in China
by Xin Pan, Yuanbo Liu and Xingwang Fan
Remote Sens. 2015, 7(4), 4899-4918; https://0-doi-org.brum.beds.ac.uk/10.3390/rs70404899 - 21 Apr 2015
Cited by 30 | Viewed by 6346
Abstract
Surface net radiation plays an important role in land–atmosphere interactions. The net radiation can be retrieved from satellite radiative products, yet its accuracy needs comprehensive assessment. This study evaluates monthly surface net radiation generated from the Clouds and the Earth’s Radiant Energy System [...] Read more.
Surface net radiation plays an important role in land–atmosphere interactions. The net radiation can be retrieved from satellite radiative products, yet its accuracy needs comprehensive assessment. This study evaluates monthly surface net radiation generated from the Clouds and the Earth’s Radiant Energy System (CERES) and the Surface Radiation Budget project (SRB) products, respectively, with quality-controlled radiation data from 50 meteorological stations in China for the period from March 2000 to December 2007. Our results show that surface net radiation is generally overestimated for CERES (SRB), with a bias of 26.52 W/m2 (18.57 W/m2) and a root mean square error of 34.58 W/m2 (29.49 W/m2). Spatially, the satellite-retrieved monthly mean of surface net radiation has relatively small errors for both CERES and SRB at inland sites in south China. Substantial errors are found at northeastern sites for two datasets, in addition to coastal sites for CERES. Temporally, multi-year averaged monthly mean errors are large at sites in western China in spring and summer, and in northeastern China in spring and winter. The annual mean error fluctuates for SRB, but decreases for CERES between 2000 and 2007. For CERES, 56% of net radiation errors come from net shortwave (NSW) radiation and 44% from net longwave (NLW) radiation. The errors are attributable to environmental parameters including surface albedo, surface water vapor pressure, land surface temperature, normalized difference vegetation index (NDVI) of land surface proxy, and visibility for CERES. For SRB, 65% of the errors come from NSW and 35% from NLW radiation. The major influencing factors in a descending order are surface water vapor pressure, surface albedo, land surface temperature, NDVI, and visibility. Our findings offer an insight into error patterns in satellite-retrieved surface net radiation and should be valuable to improving retrieval accuracy of surface net radiation. Moreover, our study on radiation data of China provides a case example for worldwide validation. Full article
(This article belongs to the Special Issue Remote Sensing of Solar Surface Radiation)
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