Forest Carbon Flux Simulation Using Multi-Source Data and Incorporation of Remotely Sensed Model with Process-Based Model

Abstract

Forest carbon flux is critical to climate change, and the accurate modeling of forest carbon flux is an extremely challenging task. The remote sensing model (the MODIS MOD_17 gross primary productivity (GPP) model (MOD_17)) has strong practicability and is widely used around the world. The ecological process (the Biome-BioGeochemical Cycles Multilayer Soil Module model (Biome-BGCMuSo)) model can describe most of the vegetation’s environmental and physiological processes on fine time scales. Nevertheless, complex parameters and calibrations pose challenges to the application and development of models. In this study, we optimized all the input parameters of the MOD_17 model for the calibration of the Biome-BGCMuSo model to obtain GPP with continuous spatiality. To determine the contribution of input parameters to the GPP of different forest types, an Extended Fourier Amplitude Sensitivity Test (EFAST) was performed on the Biome-BGCMuSo model firstly. Then, we selected the sample points of each forest type and its different ecological gradients (30 for each type), using the GPP simulation value of the optimized MOD_17 model corresponding to the time and space scale to calibrate the Biome-BGCMuSo model, to drive the calibrated Biome-BGCMuSo, and we simulated the different forest types’ net primary productivity (NPP). According to dendrochronological measurements, the NPP simulation results were verified on the whole regional scale. The results showed that the GPP values of different forest types were highly sensitive to C:Nleaf (C:N of leaf), SLA1 (canopy average specific leaf area in phenological phase 1), and FLNR (fraction of leaf N in Rubisco). The coefficient of determination (R2) between the simulated forest NPP and the measured NPP was 0.64, and the root-mean-square (RMSE) was 26.55 g/C/m2/year. Our study aims to reduce uncertainty in forest carbon fluxes simulated by the Biome-BGCMuSo model, providing feedback for understanding forest ecosystem carbon cycling, vegetation productivity, and climate change.

Spatiotemporal dynamic of subtropical forest carbon storage and its resistance and resilience to drought in China

“…Second, the uncertainty of the input variables may be due to errors or inadequacies in the collection and statistics of input data ( Jung et al., 2007 ; Eastaugh et al., 2011 ). Although the forest abundance data used in this simulation are from fine-scale classification products with a resolution of 30 m, resampling to 1 km does not avoid pixel mixing and thus errors ( Su et al., 2022 ). Third, the uncertainty of the model parameters may be due to their different effects of the model parameters on the output results under different conditions ( Tatarinov and Cienciala, 2006 ; Kang, 2016 ; Raj et al., 2018 ).…”

Use of Remote Sensing and Biogeochemical Modelling to Simulate the Impact of Climatic and Anthropogenic Factors on Forest Carbon Fluxes

“…Biogeochemical models, on the other hand, can simulate all main vegetation processes, and specifically forest photosynthesis and respiration. The two techniques are therefore intrinsically suited to be combined for estimating forest C fluxes, and in particular gross primary and net ecosystem production (GPP and NEP, respectively) [2][3][4].…”

Use of Remote Sensing and Biogeochemical Modeling to Simulate the Impact of Climatic and Anthropogenic Factors on Forest Carbon Fluxes

“…Biogeochemical models, on the other hand, can simulate all main vegetation processes, and specifically forest photosynthesis and respiration. The two techniques are therefore intrinsically suited to be combined for estimating forest C fluxes and, in particular, gross primary and net ecosystem production (GPP and NEP, respectively) [3][4][5][6].…”