{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T07:55:52Z","timestamp":1764402952140,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2019,9,5]],"date-time":"2019-09-05T00:00:00Z","timestamp":1567641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key R&amp;D Program Key Project","award":["2018YFC1506502"],"award-info":[{"award-number":["2018YFC1506502"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The thermal infrared (TIR) data from the Medium Resolution Spectral Imager II (MERSI-2) on the Chinese meteorological satellite FY-3D have high spatiotemporal resolution. Although the MERSI-2 land surface temperature (LST) products have good application prospects, there are some deviations in the TIR band radiance from MERSI-2. To accurately retrieve LSTs from MERSI-2, a method based on a cross-calibration model and split window (SW) algorithm is proposed. The method is divided into two parts: cross-calibration and LST retrieval. First, the MODTRAN program is used to simulate the radiation transfer process to obtain MERSI-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) simulation data, establish a cross-calibration model, and then calculate the actual brightness temperature (BT) of the MERSI-2 image. Second, according to the characteristics of the near-infrared (NIR) bands, the atmospheric water vapor content (WVC) is retrieved, and the atmospheric transmittance is calculated. The land surface emissivity is estimated by the NDVI-based threshold method, which ensures that both parameters (transmittance and emissivity) can be acquired simultaneously. The validation shows the following: 1) The average accuracy of our algorithm is 0.42 K when using simulation data; 2) the relative error of our algorithm is 1.37 K when compared with the MODIS LST product (MYD11A1); 3) when compared with ground-measured data, the accuracy of our algorithm is 1.23 K. Sensitivity analysis shows that the SW algorithm is not sensitive to the two main parameters (WVC and emissivity), which also proves that the estimation of LST from MERSI-2 data is feasible. In general, our algorithm exhibits good accuracy and applicability, but it still requires further improvement.<\/jats:p>","DOI":"10.3390\/rs11182083","type":"journal-article","created":{"date-parts":[[2019,9,6]],"date-time":"2019-09-06T02:59:22Z","timestamp":1567738762000},"page":"2083","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["A Split Window Algorithm for Retrieving Land Surface Temperature from FY-3D MERSI-2 Data"],"prefix":"10.3390","volume":"11","author":[{"given":"Han","family":"Wang","sequence":"first","affiliation":[{"name":"National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1288-8428","authenticated-orcid":false,"given":"Kebiao","family":"Mao","sequence":"additional","affiliation":[{"name":"School of Geography, South China Normal University, Guangzhou 510631, China"},{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth Research, Chinese Academy of Science and Beijing Normal University, Beijing 100086, China"}]},{"given":"Fengyun","family":"Mu","sequence":"additional","affiliation":[{"name":"College of Architecture and Urban Planning of Chongqing Jiaotong University, Chongqing, 400074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6163-2912","authenticated-orcid":false,"given":"Jiancheng","family":"Shi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth Research, Chinese Academy of Science and Beijing Normal University, Beijing 100086, China"}]},{"given":"Jun","family":"Yang","sequence":"additional","affiliation":[{"name":"National Satellite Meteorological Center, Beijing 100081, China"}]},{"given":"Zhaoliang","family":"Li","sequence":"additional","affiliation":[{"name":"National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]},{"given":"Zhihao","family":"Qin","sequence":"additional","affiliation":[{"name":"National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11769-018-0930-1","article-title":"Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network","volume":"28","author":"Mao","year":"2018","journal-title":"Chin. 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