{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T05:24:34Z","timestamp":1761110674276,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,25]],"date-time":"2023-06-25T00:00:00Z","timestamp":1687651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006132","name":"State Key Laboratory of Earth Surface Processes and Resource Ecology","doi-asserted-by":"publisher","award":["2022-TS-01","4227502","2023JJ30484"],"award-info":[{"award-number":["2022-TS-01","4227502","2023JJ30484"]}],"id":[{"id":"10.13039\/501100006132","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China","award":["2022-TS-01","4227502","2023JJ30484"],"award-info":[{"award-number":["2022-TS-01","4227502","2023JJ30484"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["2022-TS-01","4227502","2023JJ30484"],"award-info":[{"award-number":["2022-TS-01","4227502","2023JJ30484"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Fire plays a prominent role in the construction and destruction of ecosystems, and the accurate estimation of the burned area (BA) after a fire occurrence is of great significance to protect ecosystems and save people\u2019s lives and property. This study evaluated the performances of three publicly available BA satellite products (GFED4, MCD64CMQ, and FireCCI5.1) in detecting Chinese forest fire burning from 2001 to 2016 across different time scales (yearly, monthly, and seasonally) and spatial scales (regional and provincial). The reference data were derived from the monthly China Forestry Statistical Yearbook (CFSY), and they were mainly used to evaluate the detection ability of each of the three BA products in the three major forest fire areas of China consisting of the Northeast (NE), Southwest (SW), and Southeast (SE) regions. The main results are as follows: (1) A significant declining BA trend was demonstrated in the whole study area and in the NE and SE subregions. Specifically, the slopes for the whole area ranged from \u22123821.1 ha\/year for MCD64CMQ to \u221233,218 ha\/year for the CFSY, the slopes for the NE region ranged from \u22123821.1 ha\/year for MCD64CMQ to \u221233,218 ha\/year for the CFSY, and the slopes for the SE region ranged from \u2212594.24 ha\/year for GFED4 to \u22123162.1 ha\/year for the CFSY. The BA in China was mainly dominated by forest fires in the NE region, especially in 2003 and 2006 when this region accounted for 90% and 87% of occurrences, respectively. (2) Compared with the CFSY, GFED4 had the best performance at the yearly scale with an RMSE of 23.9 \u00d7 104 ha\/year and CC of 0.83. Similarly, at the monthly scale, GFED4 also had the best performance for the three regions, with the lowest RMSE ranging from 0.33 \u00d7 104 to 5.4 \u00d7 104 ha\/month\u2014far lower than that of FireCC5.1 which ranged from 1.16 \u00d7 104 to 8.56 \u00d7 104 ha\/month (except for the SE region where it was slightly worse than MCD64CMQ). At the seasonal scale, GFFD4 had the best performance in spring and winter. It was also noted that the fewer BAs in summer made the differences among the products insignificant. (3) Spatially, GFED4 had the best performance in RMSEs for all the provinces of the three regions, in CCs for the provinces of the SW and SE regions, and in MEs for the provinces of the SE region. (4) All three products had stronger detection abilities for severe and disaster fires than for common fires. Additionally, GFED4 had a more consistent number of months with the CFSY than the other products in the NE region. Moreover, the conclusion that GFED4 had the best performance in the China region was also proved using other validated BA datasets. These results will help us to understand the BA detection abilities of the satellite products in China and promote the further development of multi-source satellite fire data fusion.<\/jats:p>","DOI":"10.3390\/rs15133260","type":"journal-article","created":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T03:14:56Z","timestamp":1687749296000},"page":"3260","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Evaluating the Abilities of Satellite-Derived Burned Area Products to Detect Forest Burning in China"],"prefix":"10.3390","volume":"15","author":[{"given":"Xueyan","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0188-0479","authenticated-orcid":false,"given":"Zhenhua","family":"Di","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province, School of Mathematics and Computational Science, Huaihua University, Huaihua 418008, China"}]},{"given":"Jianguo","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province, School of Mathematics and Computational Science, Huaihua University, Huaihua 418008, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2015.01.001","article-title":"Global land cover mapping using Earth observation satellite data: Recent progresses and challenges","volume":"103","author":"Yifang","year":"2015","journal-title":"ISPRS J. 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