{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T02:46:43Z","timestamp":1761965203005,"version":"build-2065373602"},"reference-count":88,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T00:00:00Z","timestamp":1603929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFA0600102"],"award-info":[{"award-number":["2016YFA0600102"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41571340"],"award-info":[{"award-number":["41571340"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Downward surface solar radiation (Rs) plays a dominant role in determining the climate and environment on the Earth. However, the densely distributed ground observations of Rs are usually insufficient to meet the increasing demand of the climate diagnosis and analysis well, so it is essential to build a long-term accurate Rs dataset. The extremely randomized trees (ERT) algorithm was used to generate Rs using routine meteorological observations (2000\u20132015) from the Climate Data Center of the Chinese Meteorological Administration (CDC\/CMA). The estimated Rs values were validated against ground measurements at the national scale with an overall correlation coefficient value of 0.97, a mean bias of 0.04 Wm\u22122, a root-mean-square-error value of 23.12 Wm\u22122, and a mean relative error of 9.81%. It indicates that the estimated Rs from the ERT-based model is reasonably accurate. Moreover, the ERT-based model was used to generate a new daily Rs dataset at 756 CDC\/CMA stations from 1958 to 2015. The long-term variation trends of Rs at 454 stations covering 46 consecutive years (1970\u20132015) were also analyzed. The Rs in China showed a significant decline trend (\u22121.1 Wm\u22122 per decade) during 1970\u20132015. A decreasing trend (\u22122.8 Wm\u22122 per decade) in Rs during 1970\u20131992 was observed, followed by a recovery trend (0.23 Wm\u22122 per decade) during 1992\u20132015. The recovery trends at individual stations were found at 233 out of 454 stations during 1970\u20132015, which were mainly located in southern and northern China. The new Rs dataset would substantially provide basic data for the related studies in agriculture, ecology, and meteorology.<\/jats:p>","DOI":"10.3390\/s20216167","type":"journal-article","created":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T21:21:00Z","timestamp":1604006460000},"page":"6167","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A New Long-Term Downward Surface Solar Radiation Dataset over China from 1958 to 2015"],"prefix":"10.3390","volume":"20","author":[{"given":"Ning","family":"Hou","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing, Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Xiaotong","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing, Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3383-236X","authenticated-orcid":false,"given":"Weiyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing, Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Jiawen","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing, Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3673-1852","authenticated-orcid":false,"given":"Chunjie","family":"Feng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing, Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Shuyue","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing, Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8586-4243","authenticated-orcid":false,"given":"Kun","family":"Jia","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing, Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3803-8170","authenticated-orcid":false,"given":"Yunjun","family":"Yao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing, Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7620-4507","authenticated-orcid":false,"given":"Jie","family":"Cheng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing, Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5413-0247","authenticated-orcid":false,"given":"Bo","family":"Jiang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing, Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.1175\/1520-0442(1999)012<2691:ACOSRB>2.0.CO;2","article-title":"A climatology of surface radiation budget derived from satellite data","volume":"12","author":"Gupta","year":"1999","journal-title":"J. Clim."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1109\/JSTARS.2010.2048556","article-title":"Review on Estimation of Land Surface Radiation and Energy Budgets from Ground Measurement, Remote Sensing and Model Simulations","volume":"3","author":"Liang","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hinkelman, L.M., Jr. Stackhouse, P.W., Wielicki, B.A., Zhang, T., and Wilson, S.R. (2009). Surface insolation trends from satellite and ground measurements: Comparisons and challenges. J. Geophys. Res. Atmos., 114.","DOI":"10.1029\/2008JD011004"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1016\/j.rse.2009.01.012","article-title":"The CM-SAF operational scheme for the satellite based retrieval of solar surface irradiance-A LUT based eigenvector hybrid approach","volume":"113","author":"Mueller","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3107","DOI":"10.1007\/s00382-012-1569-8","article-title":"The global energy balance from a surface perspective","volume":"40","author":"Wild","year":"2013","journal-title":"Clim. Dyn."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"9161","DOI":"10.1029\/2018JD030214","article-title":"Determining Factors of Monthly to Decadal Variability in Surface Solar Radiation in China: Evidences from Current Reanalyses","volume":"124","author":"Feng","year":"2019","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3751","DOI":"10.1016\/j.energy.2010.05.024","article-title":"Radial Basis Function Network-based prediction of global solar radiation data: Application for sizing of a stand-alone photovoltaic system at Al-Madinah, Saudi Arabia","volume":"35","author":"Benghanem","year":"2010","journal-title":"Energy"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1029\/97WR03755","article-title":"Estimating the spatial distribution of snow in mountain basins using remote sensing and energy balance modeling","volume":"34","author":"Cline","year":"1998","journal-title":"Water Resour. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/0168-1923(95)02241-4","article-title":"A solar radiation model for use in biological applications in the South and Southeastern USA","volume":"78","author":"Cooter","year":"1996","journal-title":"Agric. For. Meteorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/S0168-1923(00)00108-8","article-title":"Contribution of agrometeorology to the simulation of crop production and its applications","volume":"103","author":"Hoogenboom","year":"2000","journal-title":"Agric. For. Meteorol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.agrformet.2004.05.003","article-title":"Use of empirical global radiation models for maize growth simulation","volume":"126","author":"Pohlert","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"601","DOI":"10.5194\/essd-9-601-2017","article-title":"The global energy balance archive (GEBA) version 2017: A database for worldwide measured surface energy fluxes","volume":"9","author":"Wild","year":"2017","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0038-092X(83)90039-7","article-title":"The Distribution of Global Solar-Radiation over the Land Surfaces of the Earth","volume":"31","author":"Stanhill","year":"1983","journal-title":"Solar Energy"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gilgen, H., Roesch, A., Wild, M., and Ohmura, A. (2009). Decadal changes in shortwave irradiance at the surface in the period from 1960 to 2000 estimated from Global Energy Balance Archive Data. J. Geophys. Res. Atmos., 114.","DOI":"10.1029\/2008JD011383"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1103159","article-title":"Do satellites detect trends in surface solar radiation?","volume":"308","author":"Pinker","year":"2005","journal-title":"Science"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wild, M. (2009). Global dimming and brightening: A review. J. Geophys. Res. Atmos., 114.","DOI":"10.1029\/2008JD011470"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liepert, B.G. (2002). Observed reductions of surface solar radiation at sites in the United States and worldwide from 1961 to 1990. Geophys. Res. Lett., 29.","DOI":"10.1029\/2002GL014910"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1126\/science.1103215","article-title":"From dimming to brightening: Decadal changes in solar radiation at Earth\u2019s surface","volume":"308","author":"Wild","year":"2005","journal-title":"Science"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Norris, J.R., and Wild, M. (2007). Trends in aerosol radiative effects over Europe inferred from observed cloud cover, solar \u201cdimming\u201d and solar \u201cbrightening\u201d. J. Geophys. Res. Atmos., 112.","DOI":"10.1029\/2006JD007794"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.solener.2018.07.076","article-title":"The solar radiation climate of Athens: Variations and tendencies in the period 1992-2017, the brightening era","volume":"173","author":"Kambezidis","year":"2018","journal-title":"Sol. Energy"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Long, C.N., Dutton, E.G., Augustine, J.A., Wiscombe, W., Wild, M., McFarlane, S.A., and Flynn, C.J. (2009). Significant decadal brightening of downwelling shortwave in the continental United States. J. Geophys. Res. Atmos., 114.","DOI":"10.1029\/2008JD011263"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1029\/2012JD018551","article-title":"Variability of the surface radiation budget over the United States from 1996 through 2011 from high-quality measurements","volume":"118","author":"Augustine","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ohmura, A. (2009). Observed decadal variations in surface solar radiation and their causes. J. Geophys. Res. Atmos., 114.","DOI":"10.1029\/2008JD011290"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Norris, J.R., and Wild, M. (2009). Trends in aerosol radiative effects over China and Japan inferred from observed cloud cover, solar \u201cdimming\u201d and solar \u201cbrightening\u201d. J. Geophys. Res., 114.","DOI":"10.1029\/2008JD011378"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4529","DOI":"10.1175\/JCLI-D-17-0891.1","article-title":"Homogenization and trend analysis of the 1958\u20132016 in situ surface solar radiation records in China","volume":"31","author":"Yang","year":"2018","journal-title":"J. Clim."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, Y.W., Trentmann, J., Pfeifroth, U., Yuan, W.P., and Wild, M. (2019). Improvement of air pollution in china inferred from changes between satellite-based and measured surface solar radiation. Remote Sens., 11.","DOI":"10.3390\/rs11242910"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5901","DOI":"10.1175\/JCLI-D-18-0666.1","article-title":"Causes of dimming and brightening in china inferred from homogenized daily clear-sky and all-sky in situ surface solar radiation records (1958\u20132016)","volume":"32","author":"Yang","year":"2019","journal-title":"J. Clim."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1038\/s41561-019-0528-y","article-title":"Changes in atmospheric shortwave absorption as important driver of dimming and brightening","volume":"13","author":"Schwarz","year":"2020","journal-title":"Nat. Geosci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Che, H.Z., Shi, G.Y., Zhang, X.Y., Arimoto, R., Zhao, J.Q., Xu, L., Wang, B., and Chen, Z.H. (2005). Analysis of 40 years of solar radiation data from China, 1961-2000. Geophys. Res. Lett., 32.","DOI":"10.1029\/2004GL022322"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Qian, Y., Kaiser, D.P., Leung, L.R., and Xu, M. (2006). More frequent cloud-free sky and less surface solar radiation in China from 1955 to 2000. Geophys. Res. Lett., 33.","DOI":"10.1029\/2005GL024586"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.5194\/angeo-28-1121-2010","article-title":"A closer looking at dimming and brightening in China during 1961\u20132005","volume":"28","author":"Xia","year":"2010","journal-title":"Ann. Geophys."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1175\/JCLI-D-10-05030.1","article-title":"Urbanization effect on the diurnal temperature range: Different roles under solar dimming and brightening*","volume":"25","author":"Wang","year":"2012","journal-title":"J. Clim."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, K.C. (2014). Measurement biases explain discrepancies between the observed and simulated decadal variability of surface incident solar radiation. Sci. Rep. UK, 4.","DOI":"10.1038\/srep06144"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3622","DOI":"10.1002\/2014JD022560","article-title":"Sunshine duration variability and trends in Italy from homogenized instrumental time series (1936\u20132013)","volume":"120","author":"Manara","year":"2015","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4112","DOI":"10.1175\/JCLI-D-12-00482.1","article-title":"Measurement Methods Affect the Observed Global Dimming and Brightening","volume":"26","author":"Wang","year":"2013","journal-title":"J. Clim."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"9581","DOI":"10.5194\/acp-12-9581-2012","article-title":"Atmospheric impacts on climatic variability of surface incident solar radiation","volume":"12","author":"Wang","year":"2012","journal-title":"Atmos. Chem. Phys."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.rse.2015.05.015","article-title":"Analysis of surface incident shortwave radiation from four satellite products","volume":"165","author":"Zhang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.1016\/j.rser.2015.08.021","article-title":"Comparison of regression and artificial neural network models for estimation of global solar radiations","volume":"52","author":"Kumar","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/S0168-1923(99)00090-8","article-title":"Estimation of mean monthly solar global radiation as a function of temperature","volume":"100","author":"Meza","year":"2000","journal-title":"Agric. Forest Meteorol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.1007\/s11430-012-4542-9","article-title":"Development of a 50-year daily surface solar radiation dataset over China","volume":"56","author":"Tang","year":"2013","journal-title":"Sci. China Earth Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"13292","DOI":"10.1002\/2013JD020527","article-title":"Reconstruction of daily photosynthetically active radiation and its trends over China","volume":"118","author":"Tang","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.agrformet.2006.02.001","article-title":"Improving estimation of hourly, daily, and monthly solar radiation by importing global data sets","volume":"137","author":"Yang","year":"2006","journal-title":"Agric. For. Meteorol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.solener.2010.01.006","article-title":"Quality control and estimation of global solar radiation in China","volume":"84","author":"Tang","year":"2010","journal-title":"Sol. Energy"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.1016\/j.solener.2015.10.053","article-title":"A novel clustering approach for short-term solar radiation forecasting","volume":"122","author":"Ghayekhloo","year":"2015","journal-title":"Sol. Energy"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.energy.2014.04.116","article-title":"On modeling global solar irradiation using air temperature for Alagoas State, Northeastern Brazil","volume":"71","author":"Tiba","year":"2014","journal-title":"Energy"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5043","DOI":"10.1016\/j.energy.2010.08.014","article-title":"Simple correlation for estimating the global solar radiation on horizontal surfaces in India","volume":"35","author":"Katiyar","year":"2010","journal-title":"Energy"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4975","DOI":"10.1002\/2015JD023097","article-title":"An efficient physically based parameterization to derive surface solar irradiance based on satellite atmospheric products","volume":"120","author":"Qin","year":"2015","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.infrared.2014.12.006","article-title":"Prediction of the solar radiation on the Earth using support vector regression technique","volume":"68","author":"Piri","year":"2015","journal-title":"Infrared Phys. Technol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.renene.2018.05.069","article-title":"A support vector machine approach to estimate global solar radiation with the influence of fog and haze","volume":"128","author":"Yao","year":"2018","journal-title":"Renew. Energy"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3467","DOI":"10.1002\/joc.6408","article-title":"Trends in downward surface shortwave radiation from multi-source data over China during 1984\u20132015","volume":"40","author":"Zhou","year":"2019","journal-title":"Int. J. Climatol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.solener.2016.06.017","article-title":"Bias induced by the AOD representation time scale in long-term solar radiation calculations. Part 2: Impact on long-term solar irradiance predictions","volume":"135","author":"Gueymard","year":"2016","journal-title":"Sol. Energy"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.solener.2020.04.082","article-title":"Bias in modeled solar radiation by non-resolved intra-daily AOD variability","volume":"205","year":"2020","journal-title":"Sol. Energy"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.enconman.2018.02.087","article-title":"Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China","volume":"164","author":"Fan","year":"2018","journal-title":"Energy Convers. Manag."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1002\/joc.4762","article-title":"Prediction of solar radiation in China using different adaptive neuro-fuzzy methods and M5 model tree","volume":"37","author":"Wang","year":"2017","journal-title":"Int. J. Climatol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.solener.2018.11.008","article-title":"Estimation of surface downward shortwave radiation over China from AVHRR data based on four machine learning methods","volume":"177","author":"Wei","year":"2019","journal-title":"Sol. Energy"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Hou, N., Zhang, X.T., Zhang, W.Y., Wei, Y., Jia, K., Yao, Y.J., Jiang, B., and Cheng, J. (2020). Estimation of surface downward shortwave radiation over china from himawari-8 ahi data based on random forest. Remote Sens., 12.","DOI":"10.3390\/rs12010181"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Yang, L., Zhang, X.T., Liang, S.L., Yao, Y.J., Jia, K., and Jia, A.L. (2018). Estimating surface downward shortwave radiation over china based on the gradient boosting decision tree method. Remote Sens., 10.","DOI":"10.3390\/rs10020185"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.rse.2012.04.026","article-title":"Consistent retrieval methods to estimate land surface shortwave and longwave radiative flux components under clear-sky conditions","volume":"124","author":"Wang","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.solener.2019.11.028","article-title":"A short-term solar radiation forecasting system for the Iberian Peninsula. Part 1: Models description and performance assessment","volume":"195","year":"2020","journal-title":"Sol. Energy"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1016\/j.solener.2019.03.079","article-title":"Machine learning regressors for solar radiation estimation from satellite data","volume":"183","year":"2019","journal-title":"Sol. Energy"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"111320","DOI":"10.1016\/j.rse.2019.111320","article-title":"Estimating hourly land surface downward shortwave and photosynthetically active radiation from DSCOVR\/EPIC observations","volume":"232","author":"Hao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.enconman.2017.02.006","article-title":"A novel hybrid model for hourly global solar radiation prediction using random forests technique and firefly algorithm","volume":"138","author":"Ibrahim","year":"2017","journal-title":"Energy Convers. Manag."},{"key":"ref_63","unstructured":"Wehenkel, L., Ernst, D., and Geurts, P. (2006, January 29\u201330). Ensembles of extremely randomized trees and some generic applications. Proceedings of the Robust Methods for Power System State Estimation and Load Forecasting, Versailles, France."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.energy.2008.09.006","article-title":"Quality control of global solar radiation using sunshine duration hours","volume":"34","author":"Moradi","year":"2009","journal-title":"Energy"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3273","DOI":"10.5194\/acp-20-3273-2020","article-title":"Improved 1\u2009km resolution PM2.5 estimates across China using enhanced space\u2013time extremely randomized trees","volume":"20","author":"Wei","year":"2020","journal-title":"Atmos. Chem. Phys."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1016\/j.enconman.2007.09.021","article-title":"Solar radiation modelling using ANNs for different climates in China","volume":"49","author":"Lam","year":"2008","journal-title":"Energy Convers. Manag."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2006","DOI":"10.1016\/j.enconman.2007.01.004","article-title":"Climate classification and passive solar design implications in China","volume":"48","author":"Lau","year":"2007","journal-title":"Energy Convers Manag."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2379","DOI":"10.1016\/j.atmosenv.2011.02.028","article-title":"Factors affecting the surface radiation trends over China between 1960 and 2000","volume":"45","author":"Wang","year":"2011","journal-title":"Atmos. Env."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.renene.2017.12.052","article-title":"Innovative trend analysis of solar radiation in China during 1962-2015","volume":"119","author":"Zhou","year":"2018","journal-title":"Renew. Energy"},{"key":"ref_70","first-page":"162","article-title":"Investigation and reduction of discretization variance in decision tree induction","volume":"1810","author":"Geurts","year":"2000","journal-title":"Lect. Notes Artif. Int."},{"key":"ref_71","first-page":"413","article-title":"Discretization of continuous attributes for supervised learning. variance evaluation and variance reduction","volume":"1","author":"Wehenkel","year":"2012","journal-title":"Proc. Seventh Int. Fuzzy Syst. Assoc. World Congr."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.csbj.2018.10.007","article-title":"iGHBP: Computational identification of growth hormone binding proteins from sequences using extremely randomised tree","volume":"16","author":"Basith","year":"2018","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.3389\/fimmu.2018.01695","article-title":"iBCE-EL: A new ensemble learning framework for improved linear b-cell epitope prediction","volume":"9","author":"Manavalan","year":"2018","journal-title":"Front. Immunol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1016\/j.csbj.2019.06.024","article-title":"AtbPpred: A robust sequence-based prediction of anti-tubercular peptides using extremely randomized trees","volume":"17","author":"Manavalan","year":"2019","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"245","DOI":"10.2307\/1907187","article-title":"Non\u2013parametric tests against trend","volume":"13","author":"Mann","year":"1945","journal-title":"Econometrica"},{"key":"ref_78","unstructured":"Kendall, M.G. (1975). Rank Correlation Methods, Charles Griffin."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"3141","DOI":"10.1016\/j.renene.2011.03.019","article-title":"Global solar radiation estimation with sunshine duration in Tibet, China","volume":"36","author":"Li","year":"2011","journal-title":"Renew. Energy"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/S0168-1923(00)00093-9","article-title":"Measuring and modelling photosynthetically active radiation in Tibet Plateau during April-October","volume":"102","author":"Zhang","year":"2000","journal-title":"Agric. Forest Meteorol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"4395","DOI":"10.1002\/joc.5676","article-title":"Estimated spatiotemporal variability of total, direct and diffuse solar radiation across China during 1958\u20132016","volume":"38","author":"Feng","year":"2018","journal-title":"Int. J. Climatol."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1007\/s00704-014-1208-x","article-title":"Spatial variability of the trends in climatic variables across China during 1961\u20132010","volume":"120","author":"Yang","year":"2015","journal-title":"Theor. Appl. Climatol."},{"key":"ref_83","first-page":"942","article-title":"Climate changes of china\u2019s mainland over the past half century","volume":"63","author":"Ren","year":"2005","journal-title":"Acta Meteorol. Sin."},{"key":"ref_84","first-page":"203","article-title":"Characteristic Analysis of Sunshine Duration Change in China during the Last 50 Years","volume":"18","author":"Li","year":"2013","journal-title":"Clim. Environ. Res."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Wang, H., and Pinker, R.T. (2009). Shortwave radiative fluxes from MODIS: Model development and implementation. J. Geophys. Res. Atmos., 114.","DOI":"10.1029\/2008JD010442"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Qin, W., Wang, L., Lin, A., Zhang, M., and Bilal, M. (2018). Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network. Remote Sens., 10.","DOI":"10.3390\/rs10071022"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"3431","DOI":"10.1002\/jgrd.50353","article-title":"Evaluation of satellite and reanalysis products of downward surface solar radiation over East Asia: Spatial and seasonal variations","volume":"118","author":"Jia","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.5194\/acp-13-2347-2013","article-title":"Intercomparison of shortwave radiative transfer schemes in global aerosol modeling: Results from the AeroCom Radiative Transfer Experiment","volume":"13","author":"Randles","year":"2013","journal-title":"Atmos. Chem. Phys."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6167\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:26:30Z","timestamp":1760178390000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6167"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,29]]},"references-count":88,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["s20216167"],"URL":"https:\/\/doi.org\/10.3390\/s20216167","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,10,29]]}}}