Examining land surface phenology in the tropical moist forest eco-zone of South America

• Published in: International journal of biometeorology 2020-11, Vol.64 (11), p.1911-1922
• Main Authors: Liang, Boyi, Chen, Xiaoqiu, Lang, Weiguang, Liu, Guohua, Malhi, Yadvinder, Rifai, Sami
• Format: Article
• Language: English
• Subjects: Air temperature; Animal Physiology; Annual variations; Biological and Medical Physics; Biometeorology; Biophysics; Carbon; Carbon cycle; Climate change; Earth and Environmental Science; Environment; Environmental Health; Forests; Growing season; Interannual variations; Leaf area; Leaf area index; Meteorology; Original Paper; Phenology; Pixels; Plant Physiology; Rainfall; Reliability analysis; Reproducibility of Results; Savannahs; Seasons; Simulation; South America; Temperature; Time series; Tropical forests; Vegetation
• ISSN: 0020-7128; 1432-1254
• DOI: 10.1007/s00484-020-01978-x

Using leaf area index (LAI) data from 1981 to 2014 in the tropical moist forest eco-zone of South America, we extracted start (SOS) and end (EOS) dates of the active growing season in forest and savanna at each pixel. Then, we detected spatiotemporal characteristics of SOS and EOS in the two vegetation types. Moreover, we analyzed relationships between interannual variations of SOS/EOS and climatic factors, and simulated SOS/EOS time series based on preceding mean air temperature and accumulated rainfall. Results show that mean SOS and EOS ranged from 260 to 330 day of year (DOY) and from 150 to 260 DOY across the study region, respectively. From 1981 to 2014, SOS advancement is more extensive than SOS delay, while EOS advancement and delay are similarly extensive. For most pixels of forest and savanna in tropical moist forest eco-zone, preceding rainfall correlates predominantly negatively with SOS but positively with EOS, while the relationship between preceding temperature and phenophases is location-specific. In addition, preceding rainfall is more extensive than preceding temperature in simulating SOS, while both preceding rainfall and temperature play an important role for simulating EOS. This study highlights the reliability of using LAI data for long-term phenological analysis in the tropical moist forest eco-zone.