| Abstract |
The vegetation green-up date (GUD) of the Tibetan Plateau (TP) is highly sensitive to climate change. Accurate estimation of GUD is essential for understanding the dynamics and stability of terrestrial ecosystems and their interactions with climate. The GUD is usually determined from a time-series of vegetation indices (VIs). The adoption of different VIs and GUD extraction methods can lead to different GUDs. However, our knowledge of the uncertainty in these GUDs on TP is still limited. In this study, we evaluated the performance of different VIs and GUD extraction methods on TP from 2003 to 2020. The GUDs were determined from six Moderate Resolution Imaging Spectroradiometer (MODIS) derived VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference infrared index (NDII), phenology index (PI), normalized difference phenology index (NDPI), and normalized difference greenness index (NDGI). Four extraction methods (β max , CCR max , G20, and RC max ) were applied individually to each VI to determine GUD. The GUDs obtained from all VIs showed similar patterns of early green-up in the eastern and late green-up in the western plateau, and similar trend of GUD advancement in the eastern and postponement in the western plateau. The accuracy of the derived GUDs was evaluated by comparison with ground-observed GUDs from 19 agrometeorological stations. Our results show that two snow-free VIs, NDGI and NDPI, had better performance in GUD extraction than the snow-calibrated conventional VIs, NDVI and EVI. Among all the VIs, NDGI gave the highest GUD accuracy when combined with the four extraction methods. Based on NDGI, the GUD extracted by the CCR max method was found to have the highest consistency (r = 0.62, p < 0.01, RMSE = 11 days, bias = −3.84 days) with ground observations. The NDGI also showed the highest accuracy for preseason snow-covered site-years (r = 0.71, p < 0.01, RMSE = 10.69 days, bias = −4.05 days), indicating its optimal resistance to snow cover ...
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