{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T09:08:19Z","timestamp":1768554499095,"version":"3.49.0"},"reference-count":46,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,3,9]],"date-time":"2018-03-09T00:00:00Z","timestamp":1520553600000},"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>An appropriate model to correct thermal radiation anisotropy is important for the wide applications of land surface temperature (LST). This paper evaluated the performance of three published directional thermal radiation models\u2014the Roujean\u2013Lagouarde (RL) model, the Bidirectional Reflectance Distribution Function (BRDF) model, and the Vinnikov model\u2014at canopy and pixel scale using simulation, airborne, and satellite data. The results at canopy scale showed that (1) the three models could describe directional anisotropy well and the Vinnikov model performed the best, especially for erectophile canopy or low leaf area index (LAI); (2) the three models reached the highest fitting accuracy when the LAI varied from 1 to 2; and (3) the capabilities of the three models were all restricted by the hotspot effect, plant height, plant spacing, and three-dimensional structure. The analysis at pixel scale indicated a consistent result that the three models presented a stable effect both on verification and validation, but the Vinnikov model had the best ability in the erectophile canopy (savannas and grassland) and low LAI (barren or sparsely vegetated) areas. Therefore, the Vinnikov model was calibrated for different land cover types to instruct the angular correction of LST. Validation with the Surface Radiation Budget Network (SURFRAD)-measured LST demonstrated that the root mean square (RMSE) of the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product could be decreased by 0.89 K after angular correction. In addition, the corrected LST showed better spatial uniformity and higher angular correlation.<\/jats:p>","DOI":"10.3390\/rs10030420","type":"journal-article","created":{"date-parts":[[2018,3,9]],"date-time":"2018-03-09T12:17:41Z","timestamp":1520597861000},"page":"420","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Evaluation of Three Parametric Models for Estimating Directional Thermal Radiation from Simulation, Airborne, and Satellite Data"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0320-6579","authenticated-orcid":false,"given":"Xiangyang","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environment Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"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 (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Zhao-Liang","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environment Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture\/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"933","DOI":"10.3390\/s8020933","article-title":"Generalized split-window algorithm for estimate of land surface temperature from Chinese geostationary FengYun meteorological satellite (FY-2C) data","volume":"8","author":"Tang","year":"2008","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2012.12.008","article-title":"Satellite-derived land surface temperature: Current status and perspectives","volume":"131","author":"Li","year":"2013","journal-title":"Remote Sens. 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