Chin J Plant Ecol ›› 2021, Vol. 45 ›› Issue (5): 487-495.DOI: 10.17521/cjpe.2020.0076

Special Issue: 遥感生态学 青藏高原植物生态学:遥感生态学

• Research Articles • Previous Articles     Next Articles

Estimation on seasonal dynamics of alpine grassland aboveground biomass using phenology camera-derived NDVI

CHEN Zhe1, WANG Hao2,*(), WANG Jin-Zhou1, SHI Hui-Jin1, LIU Hui-Ying3, HE Jin-Sheng1,2   

  1. 1Institute of Ecology, College of Urban and Environmental Sciences, Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China
    2State Key Laboratory of Grassland Agro-ecosystems, Lanzhou University, Lanzhou 730000, China
    3School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China
  • Received:2020-03-19 Accepted:2020-05-28 Online:2021-05-20 Published:2020-06-12
  • Contact: WANG Hao
  • Supported by:
    the National Natural Science Foundation of China(31630009);the National Natural Science Foundation of China(31901168);the National Natural Science Foundation of China(31901145)


Aims Accurate assessment of plant aboveground biomass is important for optimizing grassland resource management and for understanding the balance of carbon, water and energy fluxes in grassland ecosystems. This study constructed the optimal empirical models by near-surface remote sensing normalized difference vegetation index (NDVI) data, and then estimated plant aboveground biomass in an alpine grassland on the Qingzang Plateau.
Methods Using the dataset of both the field-measured aboveground biomass and the NDVIRS observed by plant canopy spectrometer (RapidSCAN), we constructed the empirical models for estimating aboveground biomass in different phases of the growing season across 2018 and 2019. Using the NDVICam time series observed by phenology camera and the estimated models, we simulated seasonal dynamics of aboveground biomass in 2018.
Important findings (1) The seasonal dynamics of NDVICam, NDVIRS and aboveground biomass exhibited a similar unimodal pattern; however, the timing of peak NDVI (August) preceded that of peak aboveground biomass (July). (2) The best model for estimating aboveground biomass is the power function in May, July and September, and the quadratic equation in June and August. The estimation accuracy ranged from 0.29 to 0.77. (3) The estimation of aboveground biomass based on the models in different phases of growing season (R2 = 0.91) showed a higher accuracy compared to that based on the model at a single time (September)(R2 = 0.49). Our results suggest that the near-surface remote sensing is an effective approach for estimating alpine grassland aboveground biomass, and further investigation on the seasonal growth of plants will help accurately evaluate grassland resources.

Key words: phenology camera, near-surface remote sensing, normalized difference vegetation index (NDVI), aboveground biomass, alpine grassland, Qingzang Plateau