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-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.