Vegetation phenology is an important parameter in models of global vegetation and land surfaces, as the accuracy of these simulations depends strongly on the description of the canopy status. Temperate forests represent one of the major types of vegetation at mid-high latitudes in the Northern Hemisphere and act as a globally important carbon sink. Thus, a better understanding of the phenological variables of temperate forests will improve the accuracy of vegetation models and estimates of regional carbon fluxes. In this work, we explored the possibility of using digital camera images to monitor phenology at species and community scales of a temperate forest in northeastern China, and used the greenness index derived from these digital images to optimize phenological model parameters. The results show that at the species scale, the onset dates of phenological phases （Korean pine, Mongolian oak） derived from the images are close to those from field observations （error 〈 3d）. At the community scale the time series data accurately reflected the observed canopy status （A^2=0.9） simulated using the phenological model, especially in autumn. The phenological model was derived from simple meteorological data and environmental factors optimized using the greenness index. These simulations provide a powerful means of analyzing environmental factors that control the phenology of temperate forests. The results indicate that digital images can be used to obtain accurate phenologicai data and provide reference data to validate remote-sensing phenological data. In addition, we propose a new method to accurately track phenological phases in land-surface models and reduce uncertainty in spatial carbon flux simulations.
准确估算陆地生态系统呼吸(Ecosystem respiration,RE)对全球陆地生态系统碳收支研究具有重要意义.模型模拟是估算陆地RE变化的一种常用手段.然而目前陆地生态系统过程模型的RE模拟尚未得到充分验证.基于耦合模式比较计划第五阶段(Coupled Model Intercomparison Project Phase 5,CMIP5)的通用陆面模型(Community land model,CLM)RE模拟结果和全球通量网(FLUXNET)66个站点的涡度相关通量观测数据(277条站点年数据)评估CLM模型对RE的模拟效果.结果表明:(1)在空间尺度上,CLM低估了高纬度站点RE,高估了低纬度站点RE,但高纬度低估量更大导致空间格局整体低估(相对误差为-3.56%).(2)在时间尺度上,CLM模型基本捕捉了RE的年际和季节变化,相关系数分别为0.60(P < 0.001)和0.63(P < 0.001);CLM低估年尺度和月尺度的RE(以C计),绝对误差分别是-182.21 g m-2 a-1、-120.16 g m-2 mon-1,相对误差分别是-17.84%、-10.60%.(3)CLM模型对不同植被功能型的RE模拟效果不同,由优及差依次为混交林、常绿针叶林、草地、农田、落叶阔叶林、常绿阔叶林.本研究在时空尺度上量化了CLM模型的生态系统呼吸模拟误差,并分析了土壤呼吸Q10和MRbase参数以及土壤碳库模拟等因素的影响,可为CLM模型的生态系统呼吸模块参数优化提供依据,进而提升其模拟精度.
