气候变化领域集成服务门户(CCPortal): Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms

CCPortal

DOI	10.3390/rs16050750
	Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms
	Suwanlee, Savittri Ratanopad; Pinasu, Dusadee; Som-ard, Jaturong; Borgogno-Mondino, Enrico; Sarvia, Filippo
发表日期	2024
EISSN	2072-4292
起始页码	16
结束页码	5
卷号	16 期号:5
英文摘要	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 (R-2) 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.
英文关键词	sugarcane; aboveground biomass; carbon stock; remote sensing; earth observation; time series; Sentinel-1; Sentinel-2; machine learning; model transferability
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001183440100001
来源期刊	REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/301833
作者单位	Mahasarakham University; National Science & Technology Development Agency - Thailand; National Metal & Materials Technology Center (MTEC); University of Turin
推荐引用方式 GB/T 7714	Suwanlee, Savittri Ratanopad,Pinasu, Dusadee,Som-ard, Jaturong,et al. Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms[J],2024,16(5).
APA	Suwanlee, Savittri Ratanopad,Pinasu, Dusadee,Som-ard, Jaturong,Borgogno-Mondino, Enrico,&Sarvia, Filippo.(2024).Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms.REMOTE SENSING,16(5).
MLA	Suwanlee, Savittri Ratanopad,et al."Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms".REMOTE SENSING 16.5(2024).