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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE journal of selected topics in applied earth observations and remote sensing Ročník 16; s. 8125 - 8134
Hlavní autori: Cao, Ruyin, Ling, Xiaofang, Liu, Licong, Wang, Weiyi, Li, Luchun, Shen, Miaogen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2023.3310617