文献类型：期刊文献

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

作者：Su, Yong[1,2] Zhang, Wangfei[1] Liu, Bingjie[3] Tian, Xin[2] Chen, Shuxin[2] Wang, Haiyi[2] Mao, Yingwu[1]

第一作者：Su, Yong

通信作者：Tian, X[1]

机构：[1]Southwest Forestry Univ, Coll Forestry, 300 Bailong Rd, Kunming 650224, Yunnan, Peoples R China;[2]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[3]Beijing Forestry Univ, Res Ctr Forest Management Engn, State Forestry & Grassland Adm, Beijing 100083, Peoples R China

年份：2022

卷号：14

期号：19

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20224212980872);Scopus(收录号：2-s2.0-85139961921);WOS:【SCI-EXPANDED(收录号:WOS:000867169900001)】；

基金：This research was funded by the National Natural Science Foundation of China (41871279, 32160365, 42161059 and 31860240), the Fundamental Research Funds of CAF (CAFYBB2021SY006), the National Science and Technology Major Project of China's High Resolution Earth Observation System (21-Y20B01-9001-19/22).

语种：英文

外文关键词：forest carbon fluxes; MODIS MOD_17 GPP; Biome-BGCMuSo; forest type; model incorporation

摘要：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:N-leaf (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 (R-2) between the simulated forest NPP and the measured NPP was 0.64, and the root-mean-square (RMSE) was 26.55 g/C/m(2)/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.