Spring and autumn phenology across the Tibetan Plateau inferred from normalized difference vegetation index and solar-induced chlorophyll fluorescence. Issue 2 (3rd April 2021)

• Record Type: Journal Article
• Authors: Meng, Fandong; Huang, Ling; Chen, Anping; Zhang, Yao; Piao, Shilong
• Is Part Of: Big earth data. Volume 5:Issue 2(2021)
• Page Start: 182
• Page End: 200
• Publication Date: 2021-04-03
• Subjects: SIF -- NDVI -- phenology -- photosynthesis -- big data -- climate change -- Tibetan plateau; Earth sciences -- Periodicals; Earth sciences -- Research -- Periodicals; Geographic information systems Periodicals; 550
• DOI: 10.1080/20964471.2021.1920661
• Languages: English
• ISSNs: 2096-4471

Abstract

ABSTRACT: Plant phenology is a key parameter for accurately modeling ecosystem dynamics. Limited by scarce ground observations and benefiting from the rapid growth of satellite-based Earth observations, satellite data have been widely used for broad-scale phenology studies. Commonly used reflectance vegetation indices represent the emergence and senescence of photosynthetic structures (leaves), but not necessarily that of photosynthetic activities. Leveraging data of the recently emerging solar-induced chlorophyll fluorescence (SIF) that is directly related to photosynthesis, and the traditional MODIS Normalized Difference Vegetation Index (NDVI), we investigated the similarities and differences on the start and end of the growing season (SOS and EOS, respectively) of the Tibetan Plateau. We found similar spatiotemporal patterns in SIF-based SOS (SOSSIF ) and NDVI-based SOS (SOSNDVI ). These spatial patterns were mainly driven by temperature in the east and by precipitation in the west. Yet the two satellite products produced different spatial patterns in EOS, likely due to their different climate dependencies. Our work demonstrates the value of big Earth data for discovering broad-scale spatiotemporal patterns, especially on regions with scarce field data. This study provides insights into extending the definition of phenology and fosters a deeper understanding of ecosystem dynamics from big data.