{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T12:31:43Z","timestamp":1764333103129,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T00:00:00Z","timestamp":1639699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["grant number 2652018077"],"award-info":[{"award-number":["grant number 2652018077"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate cropland burned area estimation is crucial for air quality modeling and cropland management. However, current global burned area products have been primarily derived from coarse spatial resolution images which cannot fulfill the spatial requirement for fire monitoring at local levels. In addition, there is an overall lack of accurate cropland straw burning identification approaches at high temporal and spatial resolution. In this study, we propose a novel algorithm to capture burned area in croplands using dense Landsat time series image stacks. Cropland burning shows a short-term seasonal variation and a long-term dynamic trend, so a multi-harmonic model is applied to characterize fire dynamics in cropland areas. By assessing a time series of the Burned Area Index (BAI), our algorithm detects all potential burned areas in croplands. A land cover mask is used on the primary burned area map to remove false detections, and the spatial information with a moving window based on a majority vote is employed to further reduce salt-and-pepper noise and improve the mapping accuracy. Compared with the accuracy of 67.3% of MODIS products and that of 68.5% of Global Annual Burned Area Map (GABAM) products, a superior overall accuracy of 92.9% was obtained by our algorithm using Landsat time series and multi-harmonic model. Our approach represents a flexible and robust way of detecting straw burning in complex agriculture landscapes. In future studies, the effectiveness of combining different spectral indices and satellite images can be further investigated.<\/jats:p>","DOI":"10.3390\/rs13245131","type":"journal-article","created":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T02:40:32Z","timestamp":1639968032000},"page":"5131","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Mapping Cropland Burned Area in Northeastern China by Integrating Landsat Time Series and Multi-Harmonic Model"],"prefix":"10.3390","volume":"13","author":[{"given":"Jinxiu","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6546-1639","authenticated-orcid":false,"given":"Du","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7932-1824","authenticated-orcid":false,"given":"Eduardo Eiji","family":"Maeda","sequence":"additional","affiliation":[{"name":"School of Biological Sciences, Faculty of Science, University of Hong Kong, Hong Kong SAR, China"},{"name":"Department of Geosciences and Geography, University of Helsinki, P.O. Box 68, 00014 Helsinki, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5996-9268","authenticated-orcid":false,"given":"Petri K. E.","family":"Pellikka","sequence":"additional","affiliation":[{"name":"Department of Geosciences and Geography, University of Helsinki, P.O. Box 68, 00014 Helsinki, Finland"},{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3899-8860","authenticated-orcid":false,"given":"Janne","family":"Heiskanen","sequence":"additional","affiliation":[{"name":"Department of Geosciences and Geography, University of Helsinki, P.O. Box 68, 00014 Helsinki, Finland"},{"name":"Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1080\/16742834.2020.1742574","article-title":"Spatial and temporal variability of open biomass burning in Northeast China from 2003 to 2017","volume":"13","author":"Wang","year":"2020","journal-title":"Atmos. Ocean. Sci. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xie, H., Du, L., Liu, S., Chen, L., Gao, S., Liu, S., Pan, H., and Tong, X. (2016). Dynamic monitoring of agricultural fires in China from 2010 to 2014 using MODIS and GlobeLand30 data. ISPRS Int. J. 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