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

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 NDVI_RS 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 NDVI_Cam 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 NDVI_Cam, NDVI_RS 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.