Araştırma Makalesi
BibTex RIS Kaynak Göster

Assessing the performance of multivariate data analysis for predicting solar radiation using alternative meteorological variables

Yıl 2025, Cilt: 6 Sayı: 1, 45 - 52, 30.04.2025

Mustafa Özbuldu , Yunus Emre Şekerli

https://doi.org/10.51753/flsrt.1590684

Öz

This research analyses how well the Partial Least Squares Regression models could predict the monthly average daily global solar radiation for seven stations in the Mediterranean region of Türkiye. Five model scenarios were created with the SARAH-3 satellite dataset from 2005 to 2023 and using ERA5-AG meteorological variables. These included maximum and minimum temperature configurations, dew point temperature, precipitation, wind speed, and vapor pressure. Different models were examined for their prediction success by using different criteria and assessing the models with varying performance evaluation benchmarks. Based on the results, the models were accurate, mainly when all the predictor variables were used. The highest predictive performance was observed at Burdur station with KGE=0.937, NSE=0.901, and RSR=0.322. The greater regional variations showcased the specific meteorological parameters’ relevancy. The results also support the adequacy of the ERA5-AG dataset for climate modelling and resource evaluation purposes. Unlike traditional regression approaches, this study demonstrates the efficiency of PLSR in handling high-dimensional climatic datasets for solar radiation prediction. These findings support the reanalysis of data in renewable energy and agricultural applications, particularly in data-limited regions.

Anahtar Kelimeler

ERA5-AG Mediterranean region partial least squares regression solar radiation prediction

Kaynakça

  • Araújo, C. S .P., Silva, I. A. C., Ippolito, M., & Almeida, C. D. G. C. (2022). Evaluation of air temperature estimated by ERA5-Land reanalysis using surface data in Pernambuco, Brazil. Environmental Monitoring and Assessment, 194(5), 381.
  • Azad, M. A. K., Mallick, J., Islam, A. R. M. T., Ayen, K., & Hasanuzzaman, M. D. (2024). Estimation of solar radiation in data-scarce subtropical region using ensemble learning models based on a novel CART-based feature selection. Theoretical and Applied Climatology, 155, 349-369.
  • Bai, J. M., Xiao-Wei, W., Arslan, E., & Zong, X. (2024). Global solar radiation and its interactions with atmospheric substances and their effects on air temperature change in Ankara province. Climate, 12(3), 35.
  • Castro, J. R., Cuadra, S. V., Pinto, L. B., Souza, J. M. H., Santos, M. P., & Heinemann, A. B. (2018). Parametrization of models and use of estimated global solar radiation data in the irrigated rice yield simulation. Revista Brasileira de Meteorologia, 33(2), 238-246.
  • Chai, T. & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247-1250.
  • Dai, Y., Yu, S., Ma, T., Ding, J., Chen, K., Zeng, G., & Zhang, M. (2024). Improving the estimation of rice above-ground biomass based on spatio-temporal uav imagery and phenological stages. Frontiers in Plant Science, 15.
  • Esbensen, K. H. (2009). Multivariate data analysis—In practice (5th ed.). Camo Software AS.
  • Farbo, A., Trombetta, N. G., Palma, L. d., & Mondino, E. B. (2024). Estimation of intercepted solar radiation and stem water potential in a table grape vineyard covered by plastic film using sentinel-2 data: a comparison of ols-, mlr-, and ml-based methods. Plants, 13(9), 1203.
  • Fraga, H., Freitas, T. R., Moriondo, M., Molitor, D., & Santos, J. A. (2024). Determining the climatic drivers for wine production in the côa region (portugal) using a machine learning approach. Land, 13(6), 749.
  • Ghazouani, N., Bawadekji, A., El-Bary, A. A., Elewa, M. M., Becheikh, N., Alassaf, Y., & Hassan, G. (2021). Performance evaluation of temperature-based global solar radiation models—case study: Arar City, KSA. Sustainability, 14, 35.
  • Hama, A., Tanaka, K., Mochizuki, A., Tsuruoka, Y., & Kondoh, A. (2020). Improving the UAV-based yield estimation of paddy rice by using the solar radiation of geostationary satellite Himawari-8. Hydrological Research Letters, 14(1), 56-61.
  • Harmsen, E. W., Cruz, P. T., & Mecikalski, J. R. (2014). Calibration of selected pyranometers and satellite-derived solar radiation in Puerto Rico. International Journal of Renewable Energy Technology, 5(1), 43-54.
  • Irvem, A., & Ozbuldu, M. (2018). Accuracy of satellite-based solar data to estimate solar energy potential for Hatay Province, Turkey. Bitlis Eren University Journal of Science, 7(2), 361-369.
  • Ishak, A., Bray, M., Remesan, R., & Han, D. (2010). Estimating reference evapotranspiration using numerical weather modelling. Hydrological Processes, 24(24), 3490-3509.
  • JRC, (2024). Official Website of The Joint Research Centre: EU Science Hub, https://joint-research-centre.ec.europa.eu/, Last Accessed on March, 2025.
  • Karaman, Ö. A., Tanyıldızı Agir, T., & Arsel, İ. (2021). Estimation of solar radiation using modern methods. Alexandria Engineering Journal, 60, 2447-2455.
  • Kaskaoutis, D., & Polo, J. (2019). Editorial for the special issue “solar radiation, modeling, and remote sensing”. Remote Sensing, 11(10), 1198.
  • Kasuya, E. (2018). On the use of r and r squared in correlation and regression. Ecological Research, 34(1), 235-236.
  • Katsekpor, J. T., Greve, K., Yamba, E. I., & Amoah, E. G. (2024). Comparative analysis of satellite and reanalysis data with ground‐based observations in northern ghana. Meteorological Applications, 31(4), e2226.
  • Kaur, N., Snider, J. L., Paterson, A. H., Virk, G., Parkash, V., Roberts, P. M., & Li, C. (2024). Genotypic variation in functional contributors to yield for a diverse collection of field‐grown cotton. Crop Science, 64(3), 1846-1861.
  • Koester, R. P., Skoneczka, J. A., Cary, T. R., Diers, B. W., & Ainsworth, E. A. (2014). Historical gains in soybean (Glycine max Merr.) seed yield are driven by linear increases in light interception, energy conversion, and partitioning efficiencies. Journal of Experimental Botany, 65(12), 3311-3321.
  • Kong, H., Wang, J., Cai, L., Cao, J., Zhou, M., & Fan, Y. (2024). Surface solar radiation resource evaluation of Xizang region based on station observation and high-resolution satellite dataset. Remote Sensing, 16(8), 1405.
  • Kosmopoulos, P. G., Kazadzis, S., El-Askary, H. M., Taylor, M., Gkikas, A., Proestakis, E., Kontoes, C. H., & El-Khayat, M. M. (2018). Earth-observation-based estimation and forecasting of particulate matter impact on solar energy in Egypt. Remote Sensing, 10(12), 1870.
  • Kothe, S., Pfeifroth, U., Cremer, R., Trentmann, J., & Hollmann, R. (2017). A satellite-based sunshine duration climate data record for Europe and Africa. Remote Sensing, 9(5), 429.
  • Li, Q., Bessafi, M., & Li, P. (2023). Mapping prediction of surface solar radiation with linear regression models: case study over Reunion Island. Atmosphere, 14(9), 1331.
  • Liu, Y., Xiao, J., Li, X., & Li, Y. (2025). Critical soil moisture detection and water–energy limit shift attribution using satellite-based water and carbon fluxes over China. Hydrology and Earth System Sciences, 29, 1241-1258.
  • Manara, V., Stocco, E., Brunetti, M., Diolaiuti, G., Fugazza, D., Pfeifroth, U., & Maugeri, M. (2020). Comparison of surface solar irradiance from ground observations and satellite data (1990–2016) over a complex orography region (Piedmont—northwest Italy). Remote Sensing, 12(23), 3882.
  • Marin, F. R., & Carvalho, G. L. (2012). Spatio-temporal variability of sugarcane yield efficiency in the state of São Paulo, Brazil. Pesquisa Agropecuária Brasileira, 47(2), 149-156.
  • Mikelsons, K., Wang, M., Kwiatkowska, E., Jiang, L., Dessailly, D., & Gossn, J. I. (2022). Statistical evaluation of sentinel-3 OLCI ocean color data retrievals. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-19.
  • Mohammadi, K., Shamshirband, S., Anisi, M. H., Alam, K. A., & Petković, D. (2015). Support vector regression based prediction of global solar radiation on a horizontal surface. Energy Conversion and Management, 91, 433-441.
  • Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the American Society of Agricultural and Biological Engineers, 50(3), 885-900.
  • Munnoli, S., Shivashankar, K., & Golshetti, A. (2023). Influences of changes in rainfall and solar radiation on performance of kharif sorghum. International Journal of Environment and Climate Change, 13(6), 186-193.
  • Olpenda, A., Stereńczak, K., & Będkowski, K. (2018). Modeling solar radiation in the forest using remote sensing data: a review of approaches and opportunities. Remote Sensing, 10(5), 694.
  • Pelosi, A., Terribile, F., D’Urso, G., & Chirico, G. B. (2020). Comparison of ERA5-Land and UERRA MESCAN-SURFEX reanalysis data with spatially interpolated weather observations for the regional assessment of reference evapotranspiration. Water, 12(6), 1669.
  • Perdinan, Winkler, J. A., & Andresen, J. A. (2021). Evaluation of multiple approaches to estimate daily solar radiation for input to crop process models. Atmosphere, 12(1), 8.
  • Pérez, C., Rivero, M., Escalante, M., Ramirez, V., & Guilbert, D. (2023). Influence of atmospheric stability on wind turbine energy production: A case study of the coastal region of Yucatan. Energies, 16(10), 4134.
  • Pfeifroth, U., Sánchez-Lorenzo, A., Manara, V., Trentmann, J., & 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.
  • Pfeifroth, U., Drücke, J., Kothe, S., Trentmann, J., Schröder, M., & Hollmann, R. (2024). SARAH-3 – satellite-based climate data records of surface solar radiation. Earth System Science Data, 16, 5243-5265.
  • Polo, J. & Kaskaoutis, D. (2023). Editorial on new challenges in solar radiation, modeling and remote sensing. Remote Sensing, 15(10), 2633.
  • Rabault, J., Sætra, M. L., Dobler, A., Eastwood, S., & Berge, E. (2025). Data fusion of complementary data sources using Machine Learning enables higher accuracy Solar Resource Maps. Solar Energy, 290, 113337.
  • Sakowska, K., Juszczak, R., & Gianelle, D. (2016). Remote sensing of grassland biophysical parameters in the context of the Sentinel-2 satellite mission. Journal of Sensors, 2016, 1-16.
  • Sgarbossa, J., Schwerz, F., Elli, E., Tibolla, L., Schmidt, D., & Caron, B. (2018). Agroforestry systems and their effects on the dynamics of solar radiation and soybean yield. Comunicata Scientiae, 9(3), 492-502.
  • Shamshirband, S., Mohammadi, K., Tong, C. W., Zamani, M., Motamedi, S., & Ch, S. (2016). A hybrid SVM-FFA method for prediction of monthly mean global solar radiation. Theoretical and Applied Climatology, 125(1-2), 53-65.
  • Simanjuntak, C., Gaiser, T., Ahrends, H. E., & Srivastava, A. K. (2022). Spatial and temporal patterns of agrometeorological indicators in maize producing provinces of South Africa. Scientific Reports, 12(1), 12072.
  • Singh, J., Knapp, H. V., Arnold, J. G., & Demissie, M. (2005). Hydrologic modeling of the Iroquois River watershed using HSPF and SWAT. Journal of the American Water Resources Association, 41(2), 361-375.
  • Smit, E., & Van Tol, J. (2022). Impacts of soil information on process-based hydrological modelling in the Upper Goukou Catchment, South Africa. Water, 14(3), 407.
  • Soci, C., Hersbach, H., Simmons, A. J., Poli, P., Bell, B., Berrisford, P., & Thépaut, J. (2024). The era5 global reanalysis from 1940 to 2022. Quarterly Journal of the Royal Meteorological Society, 150(764), 4014-4048.
  • Soria, J. J., Poma, O., Sumire, D. A., Rojas, J. H. F., & Chipa, S. M. R. (2022). Multiple linear regression model of environmental variables, predictors of global solar radiation in the area of East Lima, Peru. IOP Conference Series: Earth and Environmental Science, 1006(1), 012009.
  • Teke, A., Yildirim, H. B., & Celik, O. (2015). Evaluation and performance comparison of different models for the estimation of solar radiation. Renewable and Sustainable Energy Reviews, 50, 1097-1107.
  • Thomas, C., Nyamsi, W. W., Arola, A., Pfeifroth, U., Trentmann, J., Dorling, S., & Alexandr, A. (2023). Smart approaches for evaluating photosynthetically active radiation at various stations based on MSG Prime satellite imagery. Atmosphere, 14(8), 1259.
  • Towner, J., Cloke, H. L., Zsoter, E., Flamig, Z., Hoch, J. M., Bazo, J., ... & Stephens, E. M. (2019). Assessing the performance of global hydrological models for capturing peak river flows in the Amazon basin. Hydrology and Earth System Sciences, 23(7), 3057-3080.
  • Vizzo, J. I., Cabrerizo, M. J., Helbling, E. W., & Villafañe, V. E. (2021). Extreme and gradual rainfall effects on winter and summer estuarine phytoplankton communities from Patagonia (Argentina). Marine Environmental Research, 163, 105235.
  • Wangeci, A., Adén, D., Nikolajsen, T., Greve, M. H., & Knadel, M. (2024). Combining laser-induced breakdown spectroscopy and visible near-infrared spectroscopy for predicting soil organic carbon and texture: a Danish national-scale study. Sensors, 24(14), 4464.
  • Wang, D., Laza, M. R. C., Cassman, K. G., Huang, J., Nie, L., Ling, X., Centeno, G. S., Cui, K., Wang, F., Li, Y., & Peng, S. (2016). Temperature explains the yield difference of double-season rice between tropical and subtropical environments. Field Crops Research, 198, 303-311.
  • Wang, Y., Wang, L., Chen, H., Xiang, J., Zhang, Y., Shi, Q., Zhu, D., & Zhang, Y. (2019). Sowing dates have different effects on spikelet formation among different photoperiod‐sensitive rice genotypes. Agronomy Journal, 111(5), 2263-2275.
  • Wang, Y., Zhou, T., & Zhou, W. (2021). Solar radiation distribution method for a photovoltaic greenhouse based on the maximization of annual economic benefits. Research Square.
  • Wang, X., Zhou, J., Ma, J., Luo, P., Fu, X., Feng, X., Zhang, X., Jia, Z., Wang, X., & Huang, X. (2024). Evaluation and comparison of reanalysis data for runoff simulation in the data-scarce watersheds of alpine regions. Remote Sensing, 16(5), 751.
  • Wu, X., Liu, H., Hartmann, H., Ciais, P., Kimball, J. S., Schwalm, C. R., Camarero, J. J., Chen, A., Gentine, P., Yang, Y., Zhang, S., Li, X., Xu, C., Zhang, W., Li, Z., & Chen, D. (2022). Timing and order of extreme drought and wetness determine bioclimatic sensitivity of tree growth. Earth’s Future, 10(7), e2021EF002530.
  • Zhou, Q., Flores, A., Glenn, N., Walters, R., & Han, B. (2017). A machine learning approach to estimation of downward solar radiation from satellite-derived data products: an application over a semi-arid ecosystem in the U.S. PLoS One, 12(8), e0180239.
  • Zhou, Q., & Ismaeel, A. (2020). Seasonal cropland trends and their nexus with agrometeorological parameters in the Indus River plain. Remote Sensing, 13(1), 41.

Assessing the performance of multivariate data analysis for predicting solar radiation using alternative meteorological variables

Yıl 2025, Cilt: 6 Sayı: 1, 45 - 52, 30.04.2025

Mustafa Özbuldu , Yunus Emre Şekerli

https://doi.org/10.51753/flsrt.1590684

Öz

This research analyses how well the Partial Least Squares Regression models could predict the monthly average daily global solar radiation for seven stations in the Mediterranean region of Türkiye. Five model scenarios were created with the SARAH-3 satellite dataset from 2005 to 2023 and using ERA5-AG meteorological variables. These included maximum and minimum temperature configurations, dew point temperature, precipitation, wind speed, and vapor pressure. Different models were examined for their prediction success by using different criteria and assessing the models with varying performance evaluation benchmarks. Based on the results, the models were accurate, mainly when all the predictor variables were used. The highest predictive performance was observed at Burdur station with KGE=0.937, NSE=0.901, and RSR=0.322. The greater regional variations showcased the specific meteorological parameters’ relevancy. The results also support the adequacy of the ERA5-AG dataset for climate modelling and resource evaluation purposes. Unlike traditional regression approaches, this study demonstrates the efficiency of PLSR in handling high-dimensional climatic datasets for solar radiation prediction. These findings support the reanalysis of data in renewable energy and agricultural applications, particularly in data-limited regions.

Anahtar Kelimeler

ERA5-AG Mediterranean region partial least squares regression solar radiation prediction

Kaynakça

  • Araújo, C. S .P., Silva, I. A. C., Ippolito, M., & Almeida, C. D. G. C. (2022). Evaluation of air temperature estimated by ERA5-Land reanalysis using surface data in Pernambuco, Brazil. Environmental Monitoring and Assessment, 194(5), 381.
  • Azad, M. A. K., Mallick, J., Islam, A. R. M. T., Ayen, K., & Hasanuzzaman, M. D. (2024). Estimation of solar radiation in data-scarce subtropical region using ensemble learning models based on a novel CART-based feature selection. Theoretical and Applied Climatology, 155, 349-369.
  • Bai, J. M., Xiao-Wei, W., Arslan, E., & Zong, X. (2024). Global solar radiation and its interactions with atmospheric substances and their effects on air temperature change in Ankara province. Climate, 12(3), 35.
  • Castro, J. R., Cuadra, S. V., Pinto, L. B., Souza, J. M. H., Santos, M. P., & Heinemann, A. B. (2018). Parametrization of models and use of estimated global solar radiation data in the irrigated rice yield simulation. Revista Brasileira de Meteorologia, 33(2), 238-246.
  • Chai, T. & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247-1250.
  • Dai, Y., Yu, S., Ma, T., Ding, J., Chen, K., Zeng, G., & Zhang, M. (2024). Improving the estimation of rice above-ground biomass based on spatio-temporal uav imagery and phenological stages. Frontiers in Plant Science, 15.
  • Esbensen, K. H. (2009). Multivariate data analysis—In practice (5th ed.). Camo Software AS.
  • Farbo, A., Trombetta, N. G., Palma, L. d., & Mondino, E. B. (2024). Estimation of intercepted solar radiation and stem water potential in a table grape vineyard covered by plastic film using sentinel-2 data: a comparison of ols-, mlr-, and ml-based methods. Plants, 13(9), 1203.
  • Fraga, H., Freitas, T. R., Moriondo, M., Molitor, D., & Santos, J. A. (2024). Determining the climatic drivers for wine production in the côa region (portugal) using a machine learning approach. Land, 13(6), 749.
  • Ghazouani, N., Bawadekji, A., El-Bary, A. A., Elewa, M. M., Becheikh, N., Alassaf, Y., & Hassan, G. (2021). Performance evaluation of temperature-based global solar radiation models—case study: Arar City, KSA. Sustainability, 14, 35.
  • Hama, A., Tanaka, K., Mochizuki, A., Tsuruoka, Y., & Kondoh, A. (2020). Improving the UAV-based yield estimation of paddy rice by using the solar radiation of geostationary satellite Himawari-8. Hydrological Research Letters, 14(1), 56-61.
  • Harmsen, E. W., Cruz, P. T., & Mecikalski, J. R. (2014). Calibration of selected pyranometers and satellite-derived solar radiation in Puerto Rico. International Journal of Renewable Energy Technology, 5(1), 43-54.
  • Irvem, A., & Ozbuldu, M. (2018). Accuracy of satellite-based solar data to estimate solar energy potential for Hatay Province, Turkey. Bitlis Eren University Journal of Science, 7(2), 361-369.
  • Ishak, A., Bray, M., Remesan, R., & Han, D. (2010). Estimating reference evapotranspiration using numerical weather modelling. Hydrological Processes, 24(24), 3490-3509.
  • JRC, (2024). Official Website of The Joint Research Centre: EU Science Hub, https://joint-research-centre.ec.europa.eu/, Last Accessed on March, 2025.
  • Karaman, Ö. A., Tanyıldızı Agir, T., & Arsel, İ. (2021). Estimation of solar radiation using modern methods. Alexandria Engineering Journal, 60, 2447-2455.
  • Kaskaoutis, D., & Polo, J. (2019). Editorial for the special issue “solar radiation, modeling, and remote sensing”. Remote Sensing, 11(10), 1198.
  • Kasuya, E. (2018). On the use of r and r squared in correlation and regression. Ecological Research, 34(1), 235-236.
  • Katsekpor, J. T., Greve, K., Yamba, E. I., & Amoah, E. G. (2024). Comparative analysis of satellite and reanalysis data with ground‐based observations in northern ghana. Meteorological Applications, 31(4), e2226.
  • Kaur, N., Snider, J. L., Paterson, A. H., Virk, G., Parkash, V., Roberts, P. M., & Li, C. (2024). Genotypic variation in functional contributors to yield for a diverse collection of field‐grown cotton. Crop Science, 64(3), 1846-1861.
  • Koester, R. P., Skoneczka, J. A., Cary, T. R., Diers, B. W., & Ainsworth, E. A. (2014). Historical gains in soybean (Glycine max Merr.) seed yield are driven by linear increases in light interception, energy conversion, and partitioning efficiencies. Journal of Experimental Botany, 65(12), 3311-3321.
  • Kong, H., Wang, J., Cai, L., Cao, J., Zhou, M., & Fan, Y. (2024). Surface solar radiation resource evaluation of Xizang region based on station observation and high-resolution satellite dataset. Remote Sensing, 16(8), 1405.
  • Kosmopoulos, P. G., Kazadzis, S., El-Askary, H. M., Taylor, M., Gkikas, A., Proestakis, E., Kontoes, C. H., & El-Khayat, M. M. (2018). Earth-observation-based estimation and forecasting of particulate matter impact on solar energy in Egypt. Remote Sensing, 10(12), 1870.
  • Kothe, S., Pfeifroth, U., Cremer, R., Trentmann, J., & Hollmann, R. (2017). A satellite-based sunshine duration climate data record for Europe and Africa. Remote Sensing, 9(5), 429.
  • Li, Q., Bessafi, M., & Li, P. (2023). Mapping prediction of surface solar radiation with linear regression models: case study over Reunion Island. Atmosphere, 14(9), 1331.
  • Liu, Y., Xiao, J., Li, X., & Li, Y. (2025). Critical soil moisture detection and water–energy limit shift attribution using satellite-based water and carbon fluxes over China. Hydrology and Earth System Sciences, 29, 1241-1258.
  • Manara, V., Stocco, E., Brunetti, M., Diolaiuti, G., Fugazza, D., Pfeifroth, U., & Maugeri, M. (2020). Comparison of surface solar irradiance from ground observations and satellite data (1990–2016) over a complex orography region (Piedmont—northwest Italy). Remote Sensing, 12(23), 3882.
  • Marin, F. R., & Carvalho, G. L. (2012). Spatio-temporal variability of sugarcane yield efficiency in the state of São Paulo, Brazil. Pesquisa Agropecuária Brasileira, 47(2), 149-156.
  • Mikelsons, K., Wang, M., Kwiatkowska, E., Jiang, L., Dessailly, D., & Gossn, J. I. (2022). Statistical evaluation of sentinel-3 OLCI ocean color data retrievals. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-19.
  • Mohammadi, K., Shamshirband, S., Anisi, M. H., Alam, K. A., & Petković, D. (2015). Support vector regression based prediction of global solar radiation on a horizontal surface. Energy Conversion and Management, 91, 433-441.
  • Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the American Society of Agricultural and Biological Engineers, 50(3), 885-900.
  • Munnoli, S., Shivashankar, K., & Golshetti, A. (2023). Influences of changes in rainfall and solar radiation on performance of kharif sorghum. International Journal of Environment and Climate Change, 13(6), 186-193.
  • Olpenda, A., Stereńczak, K., & Będkowski, K. (2018). Modeling solar radiation in the forest using remote sensing data: a review of approaches and opportunities. Remote Sensing, 10(5), 694.
  • Pelosi, A., Terribile, F., D’Urso, G., & Chirico, G. B. (2020). Comparison of ERA5-Land and UERRA MESCAN-SURFEX reanalysis data with spatially interpolated weather observations for the regional assessment of reference evapotranspiration. Water, 12(6), 1669.
  • Perdinan, Winkler, J. A., & Andresen, J. A. (2021). Evaluation of multiple approaches to estimate daily solar radiation for input to crop process models. Atmosphere, 12(1), 8.
  • Pérez, C., Rivero, M., Escalante, M., Ramirez, V., & Guilbert, D. (2023). Influence of atmospheric stability on wind turbine energy production: A case study of the coastal region of Yucatan. Energies, 16(10), 4134.
  • Pfeifroth, U., Sánchez-Lorenzo, A., Manara, V., Trentmann, J., & 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.
  • Pfeifroth, U., Drücke, J., Kothe, S., Trentmann, J., Schröder, M., & Hollmann, R. (2024). SARAH-3 – satellite-based climate data records of surface solar radiation. Earth System Science Data, 16, 5243-5265.
  • Polo, J. & Kaskaoutis, D. (2023). Editorial on new challenges in solar radiation, modeling and remote sensing. Remote Sensing, 15(10), 2633.
  • Rabault, J., Sætra, M. L., Dobler, A., Eastwood, S., & Berge, E. (2025). Data fusion of complementary data sources using Machine Learning enables higher accuracy Solar Resource Maps. Solar Energy, 290, 113337.
  • Sakowska, K., Juszczak, R., & Gianelle, D. (2016). Remote sensing of grassland biophysical parameters in the context of the Sentinel-2 satellite mission. Journal of Sensors, 2016, 1-16.
  • Sgarbossa, J., Schwerz, F., Elli, E., Tibolla, L., Schmidt, D., & Caron, B. (2018). Agroforestry systems and their effects on the dynamics of solar radiation and soybean yield. Comunicata Scientiae, 9(3), 492-502.
  • Shamshirband, S., Mohammadi, K., Tong, C. W., Zamani, M., Motamedi, S., & Ch, S. (2016). A hybrid SVM-FFA method for prediction of monthly mean global solar radiation. Theoretical and Applied Climatology, 125(1-2), 53-65.
  • Simanjuntak, C., Gaiser, T., Ahrends, H. E., & Srivastava, A. K. (2022). Spatial and temporal patterns of agrometeorological indicators in maize producing provinces of South Africa. Scientific Reports, 12(1), 12072.
  • Singh, J., Knapp, H. V., Arnold, J. G., & Demissie, M. (2005). Hydrologic modeling of the Iroquois River watershed using HSPF and SWAT. Journal of the American Water Resources Association, 41(2), 361-375.
  • Smit, E., & Van Tol, J. (2022). Impacts of soil information on process-based hydrological modelling in the Upper Goukou Catchment, South Africa. Water, 14(3), 407.
  • Soci, C., Hersbach, H., Simmons, A. J., Poli, P., Bell, B., Berrisford, P., & Thépaut, J. (2024). The era5 global reanalysis from 1940 to 2022. Quarterly Journal of the Royal Meteorological Society, 150(764), 4014-4048.
  • Soria, J. J., Poma, O., Sumire, D. A., Rojas, J. H. F., & Chipa, S. M. R. (2022). Multiple linear regression model of environmental variables, predictors of global solar radiation in the area of East Lima, Peru. IOP Conference Series: Earth and Environmental Science, 1006(1), 012009.
  • Teke, A., Yildirim, H. B., & Celik, O. (2015). Evaluation and performance comparison of different models for the estimation of solar radiation. Renewable and Sustainable Energy Reviews, 50, 1097-1107.
  • Thomas, C., Nyamsi, W. W., Arola, A., Pfeifroth, U., Trentmann, J., Dorling, S., & Alexandr, A. (2023). Smart approaches for evaluating photosynthetically active radiation at various stations based on MSG Prime satellite imagery. Atmosphere, 14(8), 1259.
  • Towner, J., Cloke, H. L., Zsoter, E., Flamig, Z., Hoch, J. M., Bazo, J., ... & Stephens, E. M. (2019). Assessing the performance of global hydrological models for capturing peak river flows in the Amazon basin. Hydrology and Earth System Sciences, 23(7), 3057-3080.
  • Vizzo, J. I., Cabrerizo, M. J., Helbling, E. W., & Villafañe, V. E. (2021). Extreme and gradual rainfall effects on winter and summer estuarine phytoplankton communities from Patagonia (Argentina). Marine Environmental Research, 163, 105235.
  • Wangeci, A., Adén, D., Nikolajsen, T., Greve, M. H., & Knadel, M. (2024). Combining laser-induced breakdown spectroscopy and visible near-infrared spectroscopy for predicting soil organic carbon and texture: a Danish national-scale study. Sensors, 24(14), 4464.
  • Wang, D., Laza, M. R. C., Cassman, K. G., Huang, J., Nie, L., Ling, X., Centeno, G. S., Cui, K., Wang, F., Li, Y., & Peng, S. (2016). Temperature explains the yield difference of double-season rice between tropical and subtropical environments. Field Crops Research, 198, 303-311.
  • Wang, Y., Wang, L., Chen, H., Xiang, J., Zhang, Y., Shi, Q., Zhu, D., & Zhang, Y. (2019). Sowing dates have different effects on spikelet formation among different photoperiod‐sensitive rice genotypes. Agronomy Journal, 111(5), 2263-2275.
  • Wang, Y., Zhou, T., & Zhou, W. (2021). Solar radiation distribution method for a photovoltaic greenhouse based on the maximization of annual economic benefits. Research Square.
  • Wang, X., Zhou, J., Ma, J., Luo, P., Fu, X., Feng, X., Zhang, X., Jia, Z., Wang, X., & Huang, X. (2024). Evaluation and comparison of reanalysis data for runoff simulation in the data-scarce watersheds of alpine regions. Remote Sensing, 16(5), 751.
  • Wu, X., Liu, H., Hartmann, H., Ciais, P., Kimball, J. S., Schwalm, C. R., Camarero, J. J., Chen, A., Gentine, P., Yang, Y., Zhang, S., Li, X., Xu, C., Zhang, W., Li, Z., & Chen, D. (2022). Timing and order of extreme drought and wetness determine bioclimatic sensitivity of tree growth. Earth’s Future, 10(7), e2021EF002530.
  • Zhou, Q., Flores, A., Glenn, N., Walters, R., & Han, B. (2017). A machine learning approach to estimation of downward solar radiation from satellite-derived data products: an application over a semi-arid ecosystem in the U.S. PLoS One, 12(8), e0180239.
  • Zhou, Q., & Ismaeel, A. (2020). Seasonal cropland trends and their nexus with agrometeorological parameters in the Indus River plain. Remote Sensing, 13(1), 41.
Toplam 60 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İstatistik (Diğer)
Bölüm Araştırma Makaleleri
Yazarlar

Mustafa Özbuldu 0000-0002-5359-8750

Yunus Emre Şekerli 0000-0002-7954-8268

Yayımlanma Tarihi 30 Nisan 2025
Gönderilme Tarihi 24 Kasım 2024
Kabul Tarihi 19 Nisan 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 6 Sayı: 1

Kaynak Göster

APA Özbuldu, M., & Şekerli, Y. E. (2025). Assessing the performance of multivariate data analysis for predicting solar radiation using alternative meteorological variables. Frontiers in Life Sciences and Related Technologies, 6(1), 45-52. https://doi.org/10.51753/flsrt.1590684
 Kapak Resmi İndir

Creative Commons License

Frontiers in Life Sciences and Related Technologies is licensed under a Creative Commons Attribution 4.0 International License.