Evaluation of Different Satellite- based Surface Solar Radiation Products Using Measured Ground Data in Different Climates of Iran

Document Type : Full length article


1 Water Science Engineering, Faculty of Agriculture, -University of Bu-Ali Sina, Hamedan, Iran.

2 ِDepartment of Water Science Engineering, Faculty of Agriculture, Bu-Ali Sina Unversity

3 Water science Engineering Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

4 Inter-University Research Institute of the Earth System in Andalusia (IISTA-CEAMA), University of Granada, Spain


Surface solar radiation (SSR) is a fundamental key variable in climatological and meteorological studies. The most accurate way to obtain SSR at the surface is to measure solar irradiances using radiometers such as pyranometer. However, ground measurements are limited due to the high cost of the calibration and the regular maintenance of the equipment. To eliminate this problem, many methods have been developed to estimate solar radiation received by the Earth's surface: including, empirical models, radiative transfer models (RTM), semi-empirical models, and models based on satellite products. One of the most popular and easily accessed methods for estimating SSR is satellite-based methods, having advantages of high temporal and spatial resolution, low cost and free access can be a good alternative source for areas without stations and areas with heterogeneous distribution of stations. This work is aimed to evaluate and validate SSR derived from three satellite-based products (CERES, CLARA and SARAH) against ground measurements over Iran daily.
Materials and methods:
Ground measured data: In this study, measured SSR was extracted from 24 radiometric stations of Iran from 2012 to 2015. The SSR ground measurements are more susceptible to error than other meteorological parameters. Therefore, quality control was applied in order to remove likely errors and outliers from the measured data. In this study, Moradi's proposed method was used for data quality control (Moradi, 2009). The studied areas were classified based on Digital Elevation Model (DEM) and UNESCO climate classification approach into three zones of arid, semi-arid and coastal climates.
Satellite-based data: The satellite-based SSR outputs including CERES, CLARA, and SARAH were extracted for the period of 2012 to 2015. The satellite scientific instrument of CERES (Cloud and the Earth's Radiant Energy System) is a component of NASA’s Earth Observation System (EOS) onboard the Terra and Aqua platforms. The CERES datasets provide global coverage available from 2000 to present with a spatial resolution of 1 degree and temporal resolution of daily and monthly (Carmona et al. 2017). The Climate Monitoring Satellite Application Facility (CM SAF) provides high-quality records for climate applications from satellites. The CM SAF CLARA products are based on observations of the AVHRR instruments onboard the NOAA and METOP polar-orbit satellites. The CLARA datasets prepare global coverage with a spatial resolution of 0.25 degrees on a regular latitude-longitude grid and daily and monthly temporal resolution available from 1982 to 2015. The CM SAF SARAH products are based on observations from the MVIRI and SEVIRI (Spinning Enhanced Visible and InfraRed Imager) instruments onboard the Meteosat First- and Second-Generation satellites. The SARAH datasets supply regional coverage (Europe, Africa, the Atlantic and parts of South America) with a spatial resolution of 0.05 degrees on a regular latitude-longitude grid and instantaneous, daily and monthly temporal resolution available from 1983 to 2017 (Riihelä et al. 2015).
Methods: Since the satellite-based products studied are generally distributed in NetCDF format, the nearest neighborhood interpolation method was used to match these data with measured data. Then, satellite-based datasets were generated for station points using CDO software. In this study, satellite-based datasets were compared against measured SSR datasets by four validation metrics. The metrics used are determination coefficient (R2), the mean bias deviation (MBD), relative mean absolute deviation (RMABD) and root mean squared error (RMSE).
Results and discussion:
Evaluation of the performance of CERES, CLARA, and SARAH products in estimating daily SSR in Iran showed that despite the proper performance of all three satellite-based products in this study, SARAH with R2= 0.93 and MBD= -0.1 W.m-2 has the highest agreement with measured SSR compared to other two products. This result is consistent with the study by Journée and Bertrand (2010), Urraca et al. (2017), Alexandri et al. (2017) and Wang et al. (2018). Also, evaluation of the monthly and seasonally variations of daily SSR of three satellite products against the measured daily SSR showed that the studied satellite products are more capable of estimating SSR under clear sky conditions (warm seasons) than cloudy conditions (cold seasons).
Spatial variations of daily SSR showed that the satellite products in the arid and semi-arid climate regions had the best performance, respectively, compared to the coastal regions. In this aspect, SARAH provided the best performance in all three study areas. According to the results, the highest agreement between ground measured SSR and SARAH was observed in the dry climate (R2= 0.94) and the lowest agreement between ground measured SSR and CERES was observed in the coastal region (R2= 0.83). Also, the largest overestimation occurred by CERES in the coastal region with MBD of 21.3 W.m-2 and the smallest underestimation by SARAH in the arid climate region with MBD of -0.1 W.m-2. Also, the smallest RMSE obtained in the arid climate region by SARAH with 20 W.m-2 and largest in the coastal region by CERES with 37.3 W.m-2. These findings are consistent with the results obtained by Thomas et al. (2016), Urraca et al. (2017) and Urraca et al. (2018). Their results showed that in the coastal regions with high humidity, the errors of satellite- based SSR estimates are very high.
Seasonal variations of RMABD showed that the maximum and minimum RMABD in arid and semi-arid climate regions occurred in winter and spring, respectively. These results are consistent with the results shown by Sanchez-Lorenzo et al. (2013) and Wang et al. (2018) that indicate the lowest ability of studied satellite products in cloudy conditions (winter) than in clear sky conditions (summer) to estimate SSR. In coastal regions, the minimum and maximum RMABD were found in spring and summer, respectively. As shown in this study by Thomas et al. (2016) and Urraca et al. (2017), humidity and water vapor in the atmosphere are some of the causes of high error in satellite products. Since the coastal areas studied in this study (Bandar Abbas and Gorgan stations) have warm and humid summers, therefore, the maximum RMABD occurred in these regions in summer and the minimum RMABD in spring with lower humidity and cloudiness than other seasons.
The results showed that SARAH with the highest spatial resolution compared to CLARA and CERES had the best performance in generating daily SSR in Iran. Also, the high error in cold seasons indicates the high impact of cloudiness in reducing the accuracy of studied satellite products in Iran. Investigation of spatial variations of daily measured SSR and studied satellite products also showed that the satellite products have the highest performance in arid and semi-arid climate regions but the lowest for the coastal regions.
Most regions of Iran located in arid and semi-arid climate region and the growing season of these zones are in accordance with the warm season. Therefore, due to the acceptable performance of satellite products to estimate SSR in arid and semi-arid climate regions and in warm seasons, accordingly, SSR outputs of satellite products can be used in agricultural studies. Also, given the free availability and high spatial and temporal resolution of the satellite products under investigation, the SSR output of these products can be a good alternative for areas where there is no access to the ground based measured SSR datasets.


تازیک، ا.؛ رضایی، ع.؛ آبکار، ع.؛ علوی‏پناه، س. ک؛ جهان‏تاب، ز. و رحمتی، ع. (1394). برآورد تابش کل لحظه‏ای طول موج کوتاه خورشید با استفاده از تصاویر ماهواره‏ای سنجندة مودیس (مطالعة موردی: مناطق مرکزی ایران)، مجلة سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی، ۶(۲): 17-30.
رحیمی خوب، ع.؛ صابری، پ.؛ بهبهانی، س.م.ر. و نظری‏فر، م.ه. (1390). برآورد تابش خورشید رسیده به زمین با استفاده از تصاویر ماهوارة نوا و روابط آماری در جنوب شرق تهران، مجلة علوم آب و خاک، ۱۵(۵۶): 79-89.
مجرد، ف.؛ فتح‏نیا، ا.ا. و رجایی، س. (1394). برآورد تابش خورشیدی دریافتی سطح زمین در استان کرمانشاه، مطالعات جغرافیایی مناطق خشک، 5(۱۹): ۵۵-۶۹.
صداقت مصعبی، ب.؛ آقاشریعتمداری، ز.؛ حجابی، س. و قربانی، خ. (1398). ارزیابی کارایی مدل‏های برآورد تابش خورشید در سطح زمین با استفاده از تصاویر ماهواره‏ای، تحقیقات آب و خاک ایران، 50(۸): 1963-1973.
Alexandri, G.; Georgoulias, A.K.; Meleti, C.; Balis, D.; Kourtidis, K.A.; Sanchez-Lorenzo, A.; Trentmann, J. and Zanis, P. (2017). A high resolution satellite view of surface solar radiation over the climatically sensitive region of Eastern Mediterranean, Journal of Atmospheric Research, 188: 107-121.
Almorox, J.; Ovando, G.; Sayago, S. and Bocco, M. (2017). Assessment of surface solar irradiance retrieved by CERES, International Journal of Remote Sensing,  38(12): 3669-3683.
Carmona, F.; Orte, P.F.; Rivas, R.; Wolfram, E. and Kruse, E. (2018). Development and analysis of a new solar radiation atlas for Argentina from ground-based measurements and CERES_SYN1deg data, The Egyptian Journal of Remote Sensing and Space Science,  21(3): 211-217.
Chen, M.; Zhuang, Q. and He, Y. (2014). An efficient method of estimating downward solar radiation based on the MODIS observations for the use of land surface modeling, Journal of Remote Sensing,  6(8): 7136-7157.
Espinar, B.; Ramírez, L.; Drews, A.; Beyer, H.G.; Zarzalejo, L.F; Polo, J. and Martín, L. (2009). Analysis of different comparison parameters applied to solar radiation data from satellite and German radiometric stations, Journal of Solar Energy,  83(1): 118-125.
Estévez, J.; Gavilán, P. and Giráldez, J.V. (2011). Guidelines on validation procedures for meteorological data from automatic weather stations, Journal of hydrology,  402(1-2): 144-154.
Jahani, B. and Mohammadi, B. (2019). A comparison between the application of empirical and ANN methods for estimation of daily global solar radiation in Iran, Journal of Theoretical and Applied Climatology,  137(1-2): 1257-1269.
Journée, M. and Bertrand, C. (2010). Improving the spatio-temporal distribution of surface solar radiation data by merging ground and satellite measurements, Journal of Remote Sensing of Environment,  114(11):  2692-2704.
Karlsson, K.G.; Anttila, K.; Trentmann, J.; Stengel, M.; Meirink, J.F.; Devasthale, A.; Hanschmann, T.; Kothe, S.; Jaaskelainen, E.; Sedlar, J. and Benas, N. (2017). CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data, Journal of Atmospheric Chemistry and Physics,  17(9): 5809-5828.
Kothe, S.; Pfeifroth, U.; Cremer, R.; Trentmann, J. and Hollmann, R. (2017). A satellite-based sunshine duration climate data record for Europe and Africa, Journal of Remote Sensing,  9(5): 429-443.
Laiti, L.; Andreis, D.; Zottele, F.; Giovannini, L.; Panziera, L.; Toller, G. and Zardi, D. (2014). A solar atlas for the Trentino region in the Alps: quality control of surface radiation data, Journal of Energy Procedia,  59: 336-343.
Lotfinejad, M.; Hafezi, R.; Khanali, M.; Hosseini, S.; Mehrpooya, M. and Shamshirband, S. (2018). A comparative assessment of predicting daily solar radiation using bat neural network (BNN), generalized regression neural network (GRNN), and neuro-fuzzy (NF) system: A case study. Journal of Energies,  11(5): 1188.
Mojarrad, F.; Fathnia, A. and Rajaee, S. (2015). The Estimation of incoming solar radiation in Kermanshah province, Journal of Arid Regions Geographic Studies,  5(19): 55-69.
Mokhtari, A.; Noory, H. and Vazifedoust, M. (2018). Performance of Different Surface Incoming Solar Radiation Models and Their Impacts on Reference Evapotranspiration, Journal of Water resources management,  32(9): 3053-3070.
Moradi, I. (2009). Quality control of global solar radiation using sunshine duration hours, Journal of Energy,  34(1): 1-6.
Mousavi, S.M.; Mostafavi, E.S.; Jaafari, A.; Jaafari, A. and Hosseinpour, F. (2015). Using measured daily meteorological parameters to predict daily solar radiation, Journal of Measurement,  76: 148-155.
Mueller, R.W.; Matsoukas, C.; Gratzki, A.; Behr, H.D. and Hollmann, R. (2009). The CM-SAF operational scheme for the satellite-based retrieval of solar surface irradiance—A LUT based eigenvector hybrid approach, Journal of Remote Sensing of Environment,  113(5): 1012-1024.
Pfeifroth, U.; Sanchez‐Lorenzo, A.; Manara, V.; Trentmann, J. and Hollmann, R. (2018). Trends and variability of surface solar radiation in Europe based on surface‐and satellite‐based data records, Journal of Geophysical Research: Atmospheres,  123(3): 1735-1754.
Polo, J.; Wilbert, S.; Ruiz-Arias, J.A.; Meyer, R.; Gueymard, C.; Suri, M.; Martin, L.; Mieslinger, T.; Blanc, P.; Grant, I. and Boland, J. (2016). Preliminary survey on site-adaptation techniques for satellite-derived and reanalysis solar radiation datasets, Journal of Solar Energy,  132: 25-37.
Posselt, R.; Mueller, R.W.; Stöckli, R. and Trentmann, J. (2012). Remote sensing of solar surface radiation for climate monitoring—The CM-SAF retrieval in international comparison, Journal of Remote Sensing of Environment,  118: 186-198.
Rahimikhoob, A.; Saberi, P.; Behbahani, S. M.; Nazarifar, M. H. (2011). Estimation of Global Solar Radiation Using NOAA Satellite Images and Statistical Equations in Southeast of Tehran, Journal of Water and Soil Science,  15(56): 79-89.
Riihelä, A.; Carlund, T.; Trentmann, J.; Müller, R. and Lindfors, A. (2015). Validation of CM SAF surface solar radiation datasets over Finland and Sweden, Journal of Remote Sensing,  7(6): 6663-6682.
Sabziparvar, A. A. (2008). A simple formula for estimating global solar radiation in central arid deserts of Iran, Journal of Renewable Energy,  33(5): 1002-1010.
Sanchez-Lorenzo, A.; Wild, M. and Trentmann, J. (2013). Validation and stability assessment of the monthly mean CM SAF surface solar radiation dataset over Europe against a homogenized surface dataset (1983–2005), Journal of Remote Sensing of Environment,  134: 355-366.
Sedaqat Masabi, B.; Aghashariatmadari, Z.; Hejabi, S. and Ghorbani, K. (2019). Evaluation of the Efficiency of Solar Radiation Estimation Models Using Satellite Imagery, Iranian Journal of Soil and Water Research,  50(8): 1963-1973.
Smith, G.L.; Priestley, K.J.; Loeb, N.G.; Wielicki, B.A.; Charlock, T.P.; Minnis, P.; Doelling, D.R. and Rutan, D.A. (2011). Clouds and Earth Radiant Energy System (CERES), a review: Past, present and future, Journal of Advances in Space Research,  48(2): 254-263.
Tazik, E.; Rezaei, A.; Abkar, A.; Alavipanah, S.; Jahantab, Z.; Rahmati, A. (2015). Estimation of the instantaneous short wavelength solar radiation using satellite images of MODIS (Case study: Central regions of Iran), Journal of RS and GIS for Natural Resources,  6(1): 17-30.
Thomas, C.; Wey, E.; Blanc, P. and Wald, L. (2016). Validation of three satellite-derived databases of surface solar radiation using measurements performed at 42 stations in Brazil, Journal of Advances in Science and Research,  13: 81-86.
Urraca, R.; Gracia-Amillo, A.M.; Koubli, E.; Huld, T.; Trentmann, J.; Riihelä, A.; Lindfors, A.V.; Palmer, D.; Gottschalg, R. and Antonanzas-Torres, F. (2017). Extensive validation of CM SAF surface radiation products over Europe, Journal of Remote Sensing of Environment,  199: 171-186.
Urraca, R.; Huld, T.; Gracia-Amillo, A.; Martinez-de-Pison, F.J.; Kaspar, F. and Sanz-Garcia, A. (2018). Evaluation of global horizontal irradiance estimates from ERA5 and COSMO-REA6 reanalyses using ground and satellite-based data, Journal of Solar Energy,  164: 339-354.
Wang, L.; Kisi, O.; Zounemat-Kermani, M.; Salazar, G.A.; Zhu, Z. and Gong, W. (2016). Solar radiation prediction using different techniques: model evaluation and comparison, Journal of Renewable and Sustainable Energy Reviews,  61, PP. 384-397.
Wang, Y.; Trentmann, J.; Yuan, W. and Wild, M. (2018). Validation of CM SAF CLARA-A2 and SARAH-E Surface Solar Radiation Datasets over China, Journal of Remote Sensing,  10(12): 1-18.
Yan, H.; Huang, J.; Minnis, P.; Wang, T. and Bi, J. (2011). Comparison of CERES surface radiation fluxes with surface observations over Loess Plateau, Journal of Remote sensing of environment, 115(6): 1489-1500.
Yang, L.; Zhang, X.; Liang, S.; Yao, Y.; Jia, K. and Jia, A. (2018). Estimating surface downward shortwave radiation over china based on the gradient boosting decision tree method, Journal of Remote Sensing, 10(2): 185-207.
Younes, S.; Claywell, R. and Muneer, T. (2005). Quality control of solar radiation data: Present status and proposed new approaches, Journal of Energy, 30(9): 1533-1549.
Žák, M.; Mikšovský, J. and Pišoft, P. (2015). CMSAF radiation data: New possibilities for climatological aPPlications in the Czech Republic, Journal of Remote Sensing, 7(11): 14445-14457.
Zhang, T.; Stackhouse Jr, P.W.; Cox, S.J.; Mikovitz, J.C. and Long, C.N. (2019). Clear-sky shortwave downward flux at the Earth's surface: Ground-based data vs. satellite-based data, Journal of Quantitative Spectroscopy and Radiative Transfer,  224, PP. 247-260.