气候变化领域集成服务门户(CCPortal): The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in Southwestern China's evergreen coniferous forests

CCPortal

DOI	10.1080/15481603.2024.2345438
	The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in Southwestern China's evergreen coniferous forests
	Zhang, Yiru; He, Binbin; Chen, Rui; Zhang, Hongguo; Fan, Chunquan; Yin, Jianpeng; Li, Yanxi
发表日期	2024
ISSN	1548-1603
EISSN	1943-7226
起始页码	61
结束页码	1
卷号	61 期号:1
英文摘要	Accurate assessments of forest biomass carbon are invaluable for managing forest resources, evaluating effects on ecological protection, and achieving goals related to climate change and sustainable development. Currently, the integration of optical and synthetic aperture radar (SAR) data has been extensively utilized in estimating forest aboveground biomass carbon (AGC), while it is limited by using single-phase remote sensing images. Time-series data, which capture the interannual dynamic growth and seasonal variations of photosynthetic phenology in forests, can sufficiently describe forest growth characteristics. However, there remains a gap in research focusing on utilizing satellite-based time-series data for AGC estimation, especially for SAR sensors. This study investigated the potential of satellite-based optical and SAR time-series data for estimating AGC. Here, we undertook nine quantitative experiments of AGC estimation from Landsat 8 and Sentinel-1 and tested several regression algorithms (including multiple linear regression (MLR), random forests (RF), artificial neural network (ANN), and extreme gradient boosting (XGBoost)) to explore the contributions of spatiotemporal features to AGC estimation. The results suggested that the XGBoost algorithm was suitable for AGC estimation with explanatory solid power and stable performance. The temporal features representing forest growth trends and periodic change characteristics (such as coefficients of continuous wavelet transform) were more valuable for AGC estimation than spatial features for both sensor types, accounting for around 40% similar to 50% of the variance compared to 17% similar to 25%. The combination of optical and SAR time-series data produced the best performance (R-2 = 0.814, RMSE = 18.789 Mg C/ha, rRMSE = 26.235%), compared with when utilizing optical or SAR time-series data alone (optical: R-2 of 0.657 and rRMSE of 35.317%; SAR: R-2 of 0.672 and rRMSE of 34.701%). Feature importance analysis also verified that temporal features of optical vegetation indices, SWIR 1/2 bands, and SAR backscatter from VV polarization were the most critical variables for AGC estimation. Furthermore, incorporating temporal features into the modeling is illustrated to be effective in reducing saturation effects within high-biomass forests. This study demonstrated the superiority of time-series data for forest carbon estimation. While the applicability of this methodology has only been investigated in evergreen coniferous forests, it may provide a viable approach needed to make full use of increasingly better and free satellite time-series data to estimate forest AGC with high accuracy, supporting policy making of forest management and sustainable development.
英文关键词	Aboveground biomass; forest carbon storage; remote sensing; time series; machine learning
语种	英语
WOS研究方向	Physical Geography ; Remote Sensing
WOS类目	Geography, Physical ; Remote Sensing
WOS记录号	WOS:001208743900001
来源期刊	GISCIENCE & REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/306437
作者单位	University of Electronic Science & Technology of China
推荐引用方式 GB/T 7714	Zhang, Yiru,He, Binbin,Chen, Rui,et al. The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in Southwestern China's evergreen coniferous forests[J],2024,61(1).
APA	Zhang, Yiru.,He, Binbin.,Chen, Rui.,Zhang, Hongguo.,Fan, Chunquan.,...&Li, Yanxi.(2024).The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in Southwestern China's evergreen coniferous forests.GISCIENCE & REMOTE SENSING,61(1).
MLA	Zhang, Yiru,et al."The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in Southwestern China's evergreen coniferous forests".GISCIENCE & REMOTE SENSING 61.1(2024).