气候变化领域集成服务门户(CCPortal): Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE Asia

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DOI	10.1088/1748-9326/ab7765
	Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE Asia
	Carreiras J.M.B.; Quegan S.; Tansey K.; Page S.
发表日期	2020
ISSN	17489318
卷号	15 期号:5
英文摘要	Frequent cloud cover in the tropics significantly affects the observation of the surface by satellites. This has enormous implications for current approaches that estimate greenhouse gas (GHG) emissions from fires or map fire scars. These mainly employ data acquired in the visible to middle infrared bands to map fire scars or thermal data to estimate fire radiative power and consequently derive emissions. The analysis here instead explores the use of microwave data from the operational Sentinel-1A (S-1A) in dual-polarisation mode (VV and VH) acquired over Central Kalimantan during the 2015 fire season. Burnt areas were mapped in three consecutive periods between August and October 2015 using the random forests machine learning algorithm. In each mapping period, the omission and commission errors of the unburnt class were always below 3%, while the omission and commission errors of the burnt class were below 20% and 5% respectively. Summing the detections from the three periods gave a total burnt area of ∼1.6 million ha, but this dropped to ∼1.2 million ha if using only a pair of pre- and post-fire season S-1A images. Hence the ability of Sentinel-1 to make frequent observations significantly increases fire scar detection. Comparison with burnt area estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) burnt area product at 5 km scale showed poor agreement, with consistently much lower estimates produced by the MODIS data-on average 14%-51% of those obtained in this study. The method presented in this study offers a way to reduce the substantial errors likely to occur in optical-based estimates of GHG emissions from fires in tropical areas affected by substantial cloud cover. © 2020 The Author(s). Published by IOP Publishing Ltd.
语种	英语
scopus关键词	Decision trees; Errors; Gas emissions; Greenhouse gases; Learning algorithms; Machine learning; Radiometers; Tropics; Fire radiative power; Microwave data; Middle infrared; Moderate resolution imaging spectroradiometer; Observation frequencies; Omission and commission errors; Polarisation mode; Substantial errors; Fires; algorithm; biomass burning; cloud cover; error analysis; greenhouse gas; learning; microwave radiation; MODIS; Sentinel; Central Kalimantan; Indonesia
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/154034
作者单位	National Centre for Earth Observation, University of Sheffield, Sheffield, United Kingdom; Centre for Landscape and Climate Research, School of Geography Geology and the Environment, University of Leicester, Leicester, United Kingdom
推荐引用方式 GB/T 7714	Carreiras J.M.B.,Quegan S.,Tansey K.,et al. Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE Asia[J],2020,15(5).
APA	Carreiras J.M.B.,Quegan S.,Tansey K.,&Page S..(2020).Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE Asia.Environmental Research Letters,15(5).
MLA	Carreiras J.M.B.,et al."Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE Asia".Environmental Research Letters 15.5(2020).