{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:54:52Z","timestamp":1773708892796,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871244"],"award-info":[{"award-number":["41871244"]}],"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>This work proposes a new method for estimating downwelling surface longwave radiation (DSLR) under cloudy-sky conditions based on a parameterization method and a genetic algorithm\u2013artificial neural network (GA-ANN) algorithm. The new method establishes a GA-ANN model based on simulated data, and then combines MODIS satellite data and ERA5 reanalysis data to estimate the DSLR. According to the validation results of the field sites, the bias and RMSE are \u20139.18 and 34.88 W\/m2, respectively. Compared with the existing research, the new method can achieve reasonable accuracy. Parameter analysis using independently simulated data shows that the near-surface air temperature (Ta) and cloud base height (CBH) have an important influence on DSLR estimation under cloudy-sky conditions. With an increase in CBH, DSLR gradually decreases; however, with an increase in Ta, DSLR shows a trend of gradual increase. When estimating DSLR under cloudy-sky conditions, under the influence of clouds, except for cirrus, the change in DSLRs with CBH and Ta is greater than 20 W\/m2.<\/jats:p>","DOI":"10.3390\/rs14112716","type":"journal-article","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T00:10:33Z","timestamp":1654560633000},"page":"2716","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Estimation of Downwelling Surface Longwave Radiation with the Combination of Parameterization and Artificial Neural Network from Remotely Sensed Data for Cloudy Sky Conditions"],"prefix":"10.3390","volume":"14","author":[{"given":"Yun","family":"Jiang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1918-5346","authenticated-orcid":false,"given":"Bo-Hui","family":"Tang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanhong","family":"Zhao","sequence":"additional","affiliation":[{"name":"Faculty of Earth Science and Mapping Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,6]]},"reference":[{"key":"ref_1","first-page":"541","article-title":"Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation","volume":"96","author":"Deardoff","year":"1987","journal-title":"J. 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