{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:48:36Z","timestamp":1760150916853,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T00:00:00Z","timestamp":1643241600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002855","name":"Ministry of Science and Technology of the People's Republic of China","doi-asserted-by":"publisher","award":["2018YFC1407103"],"award-info":[{"award-number":["2018YFC1407103"]}],"id":[{"id":"10.13039\/501100002855","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41701471"],"award-info":[{"award-number":["41701471"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Surface longwave downward radiation (LWDR) plays a key role in determining the Arctic surface energy budget, especially in insolation-absent boreal winter. A reliable LWDR product is essential for understanding the intrinsic physical mechanisms of the rapid changes in the Arctic climate. The Medium-Resolution Spectral Imager (MERSI-2), a major payload of the Chinese second-generation polar-orbiting meteorological satellite, FengYun-3D (FY-3D), was designed similar to the NASA Moderate-Resolution Imaging Spectroradiometer (MODIS) in terms of the spectral bands. Although significant progress has been made in estimating clear-sky LWDR from MODIS observations using a variety of methods, few studies have focused on the retrieval of clear-sky LWDR from FY-3D MERSI-2 observations. In this study, we propose an advanced method to directly estimate the clear-sky LWDR in the Arctic from the FY-3D MERSI-2 thermal infrared (TIR) top-of-atmosphere (TOA) radiances and auxiliary information using the extremely randomized trees (ERT) machine learning algorithm. The retrieval accuracy of RMSE and bias, validated with the Baseline Surface Radiation Network (BSRN) in situ measurements, are 14.14 W\/m2 and 4.36 W\/m2, respectively, which is comparable and even better than previous studies. The scale effect in retrieval accuracy evaluation was further analyzed and showed that the validating window size could significantly influence the retrieval accuracy of the MERSI-2 clear-sky LWDR dataset. After aggregating to a spatial resolution of 9 km, the RMSE and bias of MERSI-2 retrievals can be reduced to 9.43 W\/m2 and \u22120.14 W\/m2, respectively. The retrieval accuracy of MERSI-2 clear-sky LWDR at the CERES SSF FOV spatial scale (approximately 20 km) can be further reduced to 8.64 W\/m2, which is much higher than the reported accuracy of the CERES SSF products. This study demonstrates the feasibility of producing LWDR datasets from Chinese FY-3D MERSI-2 observations using machine learning methods.<\/jats:p>","DOI":"10.3390\/rs14030606","type":"journal-article","created":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T22:01:57Z","timestamp":1643320917000},"page":"606","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Estimating the Clear-Sky Longwave Downward Radiation in the Arctic from FengYun-3D MERSI-2 Data"],"prefix":"10.3390","volume":"14","author":[{"given":"Yunfeng","family":"Cao","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Precision Forestry, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Manyao","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]},{"given":"Yuzhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1175\/2008BAMS2634.1","article-title":"Earth\u2019s global energy budget","volume":"90","author":"Trenberth","year":"2009","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1175\/JCLI-D-15-0147.1","article-title":"Dark Warming","volume":"29","author":"Burt","year":"2015","journal-title":"J. Clim."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8462","DOI":"10.1038\/s41598-017-08545-2","article-title":"Enhanced wintertime greenhouse effect reinforcing Arctic amplification and initial sea-ice melting","volume":"7","author":"Cao","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6281","DOI":"10.1175\/JCLI-D-14-00773.1","article-title":"The Impact of Arctic Winter Infrared Radiation on Early Summer Sea Ice","volume":"28","author":"Park","year":"2015","journal-title":"J. Clim."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1038\/ngeo1285","article-title":"Arctic winter warming amplified by the thermal inversion and consequent low infrared cooling to space","volume":"4","author":"Bintanja","year":"2011","journal-title":"Nat. Geosci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1038\/ngeo2071","article-title":"Arctic amplification dominated by temperature feedbacks in contemporary cliamte models","volume":"7","author":"Pithan","year":"2014","journal-title":"Nat. Geosci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1038\/nclimate1884","article-title":"Springtime atmospheric energy transport and the control of Arctic summer sea-ice extent","volume":"3","author":"Kapsch","year":"2013","journal-title":"Nat. Clim. Chang."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1080\/17538947.2019.1597189","article-title":"Remote sensing of earth\u2019s energy budget: Synthesis and review","volume":"12","author":"Liang","year":"2019","journal-title":"Int. J. Digit. Earth"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4486","DOI":"10.1002\/2013JD021427","article-title":"Evaluations of atmospheric downward longwave radiation from 44 coupled general circulation models of CMIP5","volume":"119","author":"Ma","year":"2014","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1002\/rog.20009","article-title":"Global atmospheric downward longwave radiation at the surface from ground-based observations, satellite retrievals, and reanalyses","volume":"51","author":"Wang","year":"2013","journal-title":"Rev. Geophys."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2616","DOI":"10.1002\/2016JD026250","article-title":"An efficient hybrid method for estimating clear\u2014Sky surface downward longwave radiation from MODIS data","volume":"122","author":"Cheng","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2071","DOI":"10.1029\/2019EA000754","article-title":"New Methods for Deriving Clear-Sky Surface Longwave Downward Radiation Based on Remotely Sensed Data and Ground Measurements","volume":"6","author":"Zhou","year":"2019","journal-title":"Earth Space Sci."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.isprsjprs.2020.01.011","article-title":"All-sky longwave downward radiation from satellite measurements: General parameterizations based on LST, column water vapor and cloud top temperature","volume":"161","author":"Wang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s00704-013-0891-3","article-title":"Estimation of daytime downward longwave radiation under clear and cloudy skies conditions over a sub-humid region","volume":"115","author":"Carmona","year":"2013","journal-title":"Theor. Appl. Climatol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/j.rse.2008.12.004","article-title":"Estimation of high-spatial resolution clear-sky longwave downward and net radiation over land surfaces from MODIS data","volume":"113","author":"Wang","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1109\/LGRS.2010.2046472","article-title":"A Method for Estimating Clear-Sky Instantaneous Land-Surface Longwave Radiation With GOES Sounder and GOES-R ABI Data","volume":"7","author":"Wang","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/B978-0-12-409548-9.10373-2","article-title":"Surface Downward Longwave Radiation","volume":"5","author":"Cheng","year":"2018","journal-title":"Compr. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Feng, C., Zhang, X., Wei, Y., Zhang, W., Hou, N., Xu, J., Jia, K., Yao, Y., Xie, X., and Jiang, B. (2020). Estimating Surface Downward Longwave Radiation Using Machine Learning Methods. Atmosphere, 11.","DOI":"10.3390\/atmos11111147"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111972","DOI":"10.1016\/j.rse.2020.111972","article-title":"A framework for estimating cloudy sky surface downward longwave radiation from the derived active and passive cloud property parameters","volume":"248","author":"Yang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1109\/JSTARS.2018.2878229","article-title":"Clear-Sky Longwave Downward Radiation Estimation by Integrating MODIS Data and Ground-Based Measurements","volume":"12","author":"Zhou","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6639","DOI":"10.1109\/JSTARS.2021.3075698","article-title":"Retrieving Land Surface Temperature From Chinese FY-3D MERSI-2 Data Using an Operational Split Window Algorithm","volume":"14","author":"Tang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","first-page":"86","article-title":"Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing","volume":"78","author":"Carter","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhang, Y., Yao, Y., Xiao, Z., Shang, K., Guo, X., Yang, J., Xue, S., and Wang, J. (2021). GBRT-Based Estimation of Terrestrial Latent Heat Flux in the Haihe River Basin from Satellite and Reanalysis Datasets. Remote Sens., 13.","DOI":"10.3390\/rs13061054"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Ma, J., Liang, S., Li, X., and Li, M. (2020). An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products. Remote Sens., 12.","DOI":"10.3390\/rs12244015"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1175\/2009JAMC2246.1","article-title":"Validation of the CERES Edition 2B Surface-Only Flux Algorithms","volume":"49","author":"Kratz","year":"2010","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Luo, M., Wang, Y., Xie, Y., Zhou, L., Qiao, J., Qiu, S., and Sun, Y. (2021). Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass. Forests, 12.","DOI":"10.3390\/f12020216"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Azpiroz, I., Oses, N., Quartulli, M., Olaizola, I.G., Guidotti, D., and Marchi, S. (2021). Comparison of Climate Reanalysis and Remote-Sensing Data for Predicting Olive Phenology through Machine-Learning Methods. Remote Sens., 13.","DOI":"10.3390\/rs13061224"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1175\/JAMC-D-19-0068.1","article-title":"Validation of the CERES Edition-4A Surface-Only Flux Algorithms","volume":"59","author":"Kratz","year":"2020","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1175\/1520-0477(1998)079<2115:BSRNBW>2.0.CO;2","article-title":"Baseline Surface Radiation Network (BSRN_WCRP): New Precision Radiometry for Climate Research","volume":"79","author":"Ohmura","year":"1998","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_32","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_33","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/606\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:08:44Z","timestamp":1760134124000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/606"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,27]]},"references-count":33,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030606"],"URL":"https:\/\/doi.org\/10.3390\/rs14030606","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,1,27]]}}}