Remotely Sensed Vegetation Green-Up Onset Date on the Tibetan Plateau: Simulations and Future Predictions

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-ba...

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Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing Jg. 16; S. 8125 - 8134
Hauptverfasser:	Cao, Ruyin, Ling, Xiaofang, Liu, Licong, Wang, Weiyi, Li, Luchun, Shen, Miaogen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Alpine ecosystem Annual variations Artificial intelligence Biological system modeling Climate change Coefficients Correlation coefficient Correlation coefficients Data models Ecosystem structure Ecosystems Global warming land surface phenology Long short-term memory Neural networks phenological model Phenology Precipitation Qinghai–Tibet plateau Remote sensing Satellites Simulation Springs start of vegetation growing season Temperature sensors Vegetation Vegetation mapping Tibetan Plateau
ISSN:	1939-1404, 2151-1535
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Abstract	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.
AbstractList	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.
Author	Wang, Weiyi Li, Luchun Liu, Licong Shen, Miaogen Cao, Ruyin Ling, Xiaofang
Author_xml	– sequence: 1 givenname: Ruyin orcidid: 0000-0002-8122-9696 surname: Cao fullname: Cao, Ruyin email: cao.ruyin@uestc.edu.cn organization: School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, China – sequence: 2 givenname: Xiaofang orcidid: 0000-0002-4524-8865 surname: Ling fullname: Ling, Xiaofang email: lingxiaofang@std.uestc.edu.cn organization: School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, China – sequence: 3 givenname: Licong surname: Liu fullname: Liu, Licong email: liulicong@mail.bnu.edu.cn organization: State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China – sequence: 4 givenname: Weiyi surname: Wang fullname: Wang, Weiyi email: 202221070117@std.uestc.edu.cn organization: School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, China – sequence: 5 givenname: Luchun surname: Li fullname: Li, Luchun email: 202121070118@std.uestc.edu.cn organization: School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, China – sequence: 6 givenname: Miaogen orcidid: 0000-0001-5742-8807 surname: Shen fullname: Shen, Miaogen email: shenmiaogen@bnu.edu.cn organization: State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
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SubjectTerms	Alpine ecosystem Annual variations Artificial intelligence Biological system modeling Climate change Coefficients Correlation coefficient Correlation coefficients Data models Ecosystem structure Ecosystems Global warming land surface phenology Long short-term memory Neural networks phenological model Phenology Precipitation Qinghai–Tibet plateau Remote sensing Satellites Simulation Springs start of vegetation growing season Temperature sensors Vegetation Vegetation mapping
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Title	Remotely Sensed Vegetation Green-Up Onset Date on the Tibetan Plateau: Simulations and Future Predictions
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