Simulation of forest carbon fluxes by integrating remote sensing data into biome-BGC model

The accurate quantification and monitoring of intra- and inter-annual variability of long-term forest carbon fluxes are imperative to understand the changes in the forest ecosystems in response to the changing climate. The present study aims to simulate forest carbon fluxes in two major plant functional types (PFTs) of northwest Himalayan (NWH) foothills of India by integrating time-series remote sensing (RS) data into BiomeBiogeochemical Cycle (Biome-BGC), a process-based model, and to study the spatio-temporal variability of carbon fluxes. The parameterization of the Biome-BGC model was carried out using the data from two Eddy covariance (EC) flux tower sites located in the NWH foothills of India. An analysis was carried out to identify the sensitive parameters and their calibration was performed. The calibrated Biome-BGC model with a higher coefficient of determination (R2) and%RMSE for moist deciduous (R2 = 0.80, %RMSE = 13.24) and dry deciduous (R2 = 0.79, %RMSE =13.85) PFTs was used to estimate the gross primary productivity (GPP) from 2001 to 2018. However, the range of model-simulated leaf area index (LAI) was found to be lower than the field observed LAI. Integrating satellite-based Global Land Surface Satellite (GLASS) LAI into the Biome-BGC model led to substantial improvement in GPP estimates of moist deciduous (R2 = 0.87, %RMSE = 11.12) and dry deciduous PFTs (R2 = 0.86, %RMSE = 10.38) when compared to EC tower-based GPP for the year 2018. Based upon the accuracy, GLASS LAI integrated Biome-BGC model was used to map the spatio-temporal variability of forest carbon fluxes. The study highlighted that integration of RS data into calibrated process-based model increased the accuracy of model-simulated forest carbon fluxes.

Automated summary

This study simulated forest carbon fluxes in two major plant functional types in the northwest Himalayan foothills of India using remote sensing data and Biome-BGC model. The study found that integrating remote sensing data into the calibrated process-based model increased the accuracy of model-simulated forest carbon fluxes.