Multisatellite Analyses of Spatiotemporal Variability in Photosynthetic Activity Over the Tibetan Plateau. Issue 12 (12th December 2019)

• Record Type: Journal Article
• Authors: Wang, Xiaoyi; Wang, Tao; Liu, Dan; Zhang, Taotao; Xu, Jinfeng; Cui, Guishan; Lv, Guanting; Huang, Huabing
• Is Part Of: Journal of geophysical research. Volume 124:Issue 12(2019)
• Page Start: 3778
• Page End: 3797
• Publication Date: 2019-12-12
• Subjects: phenological timing -- trend -- interannual variability -- vegetation productivity -- Tibetan Plateau; Geobiology -- Periodicals; Biogeochemistry -- Periodicals; Biotic communities -- Periodicals; Geophysics -- Periodicals; 577.14
• DOI: 10.1029/2019JG005249
• Languages: English
• ISSNs: 2169-8953

Abstract

Abstract: Ecosystems on the Tibetan Plateau are particularly sensitive to climate change. Significant advances have been made toward understanding the effects of climate change on the vegetation productivity of the plateau by using satellite observations, but a comprehensive study including various satellite measurements has yet to be presented. Here, we analyze the spatiotemporal variability in Tibetan vegetation productivity using a variety of proxies, including the Normalized Difference Vegetation Index, Enhanced Vegetation Index, Near‐Infrared Reflectance of vegetation, and Solar‐Induced chlorophyll Fluorescence. First, in terms of productivity responses to seasonal climate variation, Near‐Infrared Reflectance of vegetation‐ and Solar‐Induced chlorophyll Fluorescence‐based growing season length had better correlation with eddy covariance site measurements and was ~50 days shorter than those of the other proxies. Second, all proxies, except the Normalized Difference Vegetation Index derived from Système Probatoire d'Observation de la Terre, displayed a dipole‐like trend pattern, similar to the observed north‐south dipole pattern of precipitation change. This result is explained by the finding that moisture is a dominant driver controlling the interannual variation in productivity. Third, we analyzed the long‐term responses of vegetation productivity to the precipitation regime and found four distinct productivity regimes, desert steppe, alpine steppe, meadow, and shrub/forests, indicating that Tibetan productivity varies with precipitation in a nonlinear way. The result offers insights into how Tibetan vegetation will respond to future climate change. Our results provide a multisatellite perspective on the impacts of climate change on spatiotemporal variation in productivity and suggest an urgent need to improve Tibetan productivity simulation. Key Points: A multi‐satellite evaluation of spatiotemporal variability in Tibetan productivity NIRv and SIF‐based spring (autumn) dates were ~30 days later (~20 days earlier) than those of the other proxies Tibetan productivity varies with precipitation nonlinear with three breakpoints