气候变化领域集成服务门户(CCPortal): Remotely Sensed Vegetation Green-Up Onset Date on the Tibetan Plateau: Simulations and Future Predictions

CCPortal

DOI	10.1109/JSTARS.2023.3310617
	Remotely Sensed Vegetation Green-Up Onset Date on the Tibetan Plateau: Simulations and Future Predictions
	Cao, Ruyin; Ling, Xiaofang; Liu, Licong; Wang, Weiyi; Li, Luchun; Shen, Miaogen
发表日期	2023
ISSN	1939-1404
EISSN	2151-1535
起始页码	8125
结束页码	8134
卷号	16
英文摘要	Vegetation green-up onset date (VGD) is a key indicator of ecosystem structure and processes. As the largest and highest alpine ecoregion, the Tibetan plateau (TP) has experienced markable climate warming during the past decades and showed substantial changes in VGD. However, the existing process-based phenology models still cannot simulate interannual variations in satellite-derived VGD. In this study, we developed a data-driven VGD model for the TP based on the Long short-term memory neural network (called VGD-LSTM). VGD-LSTM considers the complicated nonlinear relationship between VGD and multiple climatic and environmental drivers, including the time series of temperature (daytime, daily minimum, and daily mean) and precipitation, as well as nonsequential variables (elevation and geolocation). Compared with the benchmark process-based VGD model for the TP (i.e., the hierarchical model), VGD-LSTM greatly improved the simulation of interannual VGD variations. We calculated the correlation coefficients (R) between satellite-derived VGDs and VGD simulations during 2000-2018; the percentages of pixels with R values above 0.5 increased from 15% for the hierarchical model to 41% for VGD-LSTM. The advanced trend in the satellite-derived VGD on the entire TP during 2000-2018 (-0.37 day/year) was captured well by VGD-LSTM (-0.33 day/year) but was underestimated by the hierarchical model (-0.08 day/year). According to VGD-LSTM simulations, VGDs on the TP are projected to advance by 8-10 days by 2100 relative to 2015-2020 under high shared socioeconomic pathway scenarios. This study suggests the potential of artificial intelligence in phenology modeling for which the physiological processes are difficult to be fully represented.
关键词	Precipitation Vegetation mapping Data models Biological system modeling Temperature sensors Ecosystems Springs Alpine ecosystem land surface phenology phenological model Qinghai-Tibet plateau start of vegetation growing season
英文关键词	TIME-SERIES DATA; SPRING PHENOLOGY; TEMPERATURE SENSITIVITY; CLIMATE-CHANGE; IMPACTS; DATASET; QUALITY; MODEL
WOS研究方向	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001068910800004
来源期刊	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/283633
作者单位	University of Electronic Science & Technology of China; Beijing Normal University
推荐引用方式 GB/T 7714	Cao, Ruyin,Ling, Xiaofang,Liu, Licong,et al. Remotely Sensed Vegetation Green-Up Onset Date on the Tibetan Plateau: Simulations and Future Predictions[J],2023,16.
APA	Cao, Ruyin,Ling, Xiaofang,Liu, Licong,Wang, Weiyi,Li, Luchun,&Shen, Miaogen.(2023).Remotely Sensed Vegetation Green-Up Onset Date on the Tibetan Plateau: Simulations and Future Predictions.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,16.
MLA	Cao, Ruyin,et al."Remotely Sensed Vegetation Green-Up Onset Date on the Tibetan Plateau: Simulations and Future Predictions".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 16(2023).