{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T09:54:48Z","timestamp":1726221288353},"reference-count":81,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T00:00:00Z","timestamp":1708560000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Mahasarakham University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Accurately mapping crop aboveground biomass (AGB) in a timely manner is crucial for promoting sustainable agricultural practices and effective climate change mitigation actions. To address this challenge, the integration of satellite-based Earth Observation (EO) data with advanced machine learning algorithms offers promising prospects to monitor land and crop phenology over time. However, achieving accurate AGB maps in small crop fields and complex landscapes is still an ongoing challenge. In this study, the AGB was estimated for small sugarcane fields (<1 ha) located in the Kumphawapi district of Udon Thani province, Thailand. Specifically, in order to explore, estimate, and map sugarcane AGB and carbon stock for the 2018 and 2021 years, ground measurements and time series of Sentinel-1 (S1) and Sentinel-2 (S2) data were used and random forest regression (RFR) and support vector regression (SVR) applied. Subsequently, optimized predictive models used to generate large-scale maps were adapted. The RFR models demonstrated high efficiency and consistency when compared to the SVR models for the two years considered. Specifically, the resulting AGB maps displayed noteworthy accuracy, with the coefficient of determination (R2) as 0.85 and 0.86 with a root mean square error (RMSE) of 8.84 and 9.61 t\/ha for the years 2018 and 2021, respectively. In addition, mapping sugarcane AGB and carbon stock across a large scale showed high spatial variability within fields for both base years. These results exhibited a high potential for effectively depicting the spatial distribution of AGB densities. Finally, it was shown how these highly accurate maps can support, as valuable tools, sustainable agricultural practices, government policy, and decision-making processes.<\/jats:p>","DOI":"10.3390\/rs16050750","type":"journal-article","created":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T16:28:47Z","timestamp":1708619327000},"page":"750","source":"Crossref","is-referenced-by-count":5,"title":["Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-5354-5918","authenticated-orcid":false,"given":"Savittri Ratanopad","family":"Suwanlee","sequence":"first","affiliation":[{"name":"Department of Geography, Faculty of Humanities and Social Sciences, Mahasarakham University, Maha Sarakham 44150, Thailand"}]},{"given":"Dusadee","family":"Pinasu","sequence":"additional","affiliation":[{"name":"Technology and Informatics Institute for Sustainability, National Metal and Materials Technology Center, National Science and Technology Development Agency, Thailand Science Park, Pathum Thani 12120, Thailand"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5868-1860","authenticated-orcid":false,"given":"Jaturong","family":"Som-ard","sequence":"additional","affiliation":[{"name":"Department of Geography, Faculty of Humanities and Social Sciences, Mahasarakham University, Maha Sarakham 44150, Thailand"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4570-8013","authenticated-orcid":false,"given":"Enrico","family":"Borgogno-Mondino","sequence":"additional","affiliation":[{"name":"Department of Agricultural, Forest and Food Sciences, University of Turin, 10095 Torino, Italy"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4556-446X","authenticated-orcid":false,"given":"Filippo","family":"Sarvia","sequence":"additional","affiliation":[{"name":"Department of Agricultural, Forest and Food Sciences, University of Turin, 10095 Torino, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s13021-021-00192-5","article-title":"Biomass, carbon stock and sequestration potential of Oxytenanthera abyssinica forests in Lower Beles River Basin, Northwestern Ethiopia","volume":"16","author":"Abebe","year":"2021","journal-title":"Carbon Balance Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11177","DOI":"10.1007\/s11356-017-8687-0","article-title":"Agroforestry: A sustainable environmental practice for carbon sequestration under the climate change scenarios\u2014A review","volume":"24","author":"Abbas","year":"2017","journal-title":"Environ. 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