{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T06:51:12Z","timestamp":1764053472275,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2017,2,16]],"date-time":"2017-02-16T00:00:00Z","timestamp":1487203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land surface temperature (LST) is a key variable in the study of the energy exchange between the land surface and the atmosphere. Among the different methods proposed to estimate LST, the quadratic split-window (SW) method has achieved considerable popularity. This method works well when the emissivities are high in both channels. Unfortunately, it performs poorly for low land surface emissivities (LSEs). To solve this problem, assuming that the LSE is known, the constant in the quadratic SW method was calculated by maintaining the other coefficients the same as those obtained for the black body condition. This procedure permits transfer of the emissivity effect to the constant. The result demonstrated that the constant was influenced by both atmospheric water vapour content (W) and atmospheric temperature (T0) in the bottom layer. To parameterize the constant, an exponential approximation between W and T0 was used. A LST retrieval algorithm was proposed. The error for the proposed algorithm was RMSE = 0.70 K. Sensitivity analysis results showed that under the consideration of NE\u0394T = 0.2 K, 20% uncertainty in W and 1% uncertainties in the channel mean emissivity and the channel emissivity difference, the RMSE was 1.29 K. Compared with AST 08 product, the proposed algorithm underestimated LST by about 0.8 K for both study areas when ASTER L1B data was used as a proxy of Gaofen-5 (GF-5) satellite data. The GF-5 satellite is scheduled to be launched in 2017.<\/jats:p>","DOI":"10.3390\/rs9020161","type":"journal-article","created":{"date-parts":[[2017,2,16]],"date-time":"2017-02-16T12:55:34Z","timestamp":1487249734000},"page":"161","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Algorithm Development for Land Surface Temperature Retrieval: Application to Chinese Gaofen-5 Data"],"prefix":"10.3390","volume":"9","author":[{"given":"Yuanyuan","family":"Chen","sequence":"first","affiliation":[{"name":"Key Laboratory of Agri-informatics, Ministry of Agriculture\/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"},{"name":"ICube (UMR7357), UdS, CNRS, 300 Bld S\u00e9bastien Brant, CS10413, Illkirch 67412, France"}]},{"given":"Si-Bo","family":"Duan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agri-informatics, Ministry of Agriculture\/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2882-308X","authenticated-orcid":false,"given":"Huazhong","family":"Ren","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"}]},{"given":"Jelila","family":"Labed","sequence":"additional","affiliation":[{"name":"ICube (UMR7357), UdS, CNRS, 300 Bld S\u00e9bastien Brant, CS10413, Illkirch 67412, France"}]},{"given":"Zhao-Liang","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agri-informatics, Ministry of Agriculture\/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"},{"name":"ICube (UMR7357), UdS, CNRS, 300 Bld S\u00e9bastien Brant, CS10413, Illkirch 67412, France"}]}],"member":"1968","published-online":{"date-parts":[[2017,2,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3801","DOI":"10.3390\/s90503801","article-title":"A review of current methodologies for regional evapotranspiration estimation from remotely sensed data","volume":"9","author":"Li","year":"2009","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4025","DOI":"10.3390\/rs6054025","article-title":"A three-dimensional index for characterizing crop water stress","volume":"6","author":"Torrion","year":"2014","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1080\/01431169508954489","article-title":"Evaluation of a combined modelling-remote sensing method for estimating net radiation in a wetland: A case study in the Nebraska Sand Hills, U.S.A","volume":"16","author":"Goodin","year":"1995","journal-title":"Int. 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