Greenness Index from Phenocams Performs Well in Linking Climatic Factors and Monitoring Grass Phenology in a Temperate Prairie Ecosystem

利用物候相机绿度指数分析温带草原生态系统的植被物候及其与气象因子关系（英文）

Near-surface remote sensing(e.g.,digital cameras)has played an important role in capturing plant phenological metrics at either a focal or landscape scale.Exploring the relationship of the digital image-based greenness index(e.g.,Gcc,green chromatic coordinate)with that derived from satellites is critical for land surface process research.Moreover,our understanding of how well Gcc time series associate with environmental variables at field stations in North American prairies remains limited.This paper investigated the response of grass Gcc to daily environmental factors in 2018,such as soil moisture(temperature),air temperature,and solar radiation.Thereafter,using a derivative-based phenology extraction method,we evaluated the correspondence between key phenological events(mainly including start,end and length of growing season,and date with maximum greenness value)derived from Gcc,MODIS and VIIRS NDVI(EVI)for the period 2015–2018.The results showed that daily Gcc was in good agreement with ground-level environmental variables.Additionally,multivariate regression analysis identified that the grass growth in the study area was mainly affected by soil temperature and solar radiation,but not by air temperature.High frequency Gcc time series can respond immediately to precipitation events.In the same year,the phenological metrics retrieved from digital cameras and multiple satellites are similar,with spring phenology having a larger relative difference.There are distinct divergences between changing rates in the greenup and senescence stages.Gcc also shows a close relationship with growing degree days(GDD)derived from air temperature.This study evaluated the performance of a digital camera for monitoring vegetation phenological metrics and related climatic factors.This research will enable multiscale modeling of plant phenology and grassland resource management of temperate prairie ecosystems.

近地面数码相机在区域和景观尺度上的植被物候参数分析中发挥着重要作用。评估数码照片提取的绿度指数(Gcc,相对绿度指数)与卫星绿度指数的相关性有助于推进陆面过程的研究工作。另外,在北美大草原地区,Gcc时间序列数据对环境因子的响应仍然不清晰。首先,本文在生态观测站点尺度,评估了日尺度的相对绿度指数与日值气象因子的响应关系,如土壤湿度、土壤温度、气温和太阳辐射等。其次,基于站点Gcc数据、MODIS和VIIRS卫星植被指数数据,利用曲线求导方法获得植被的关键物候参数,包括生长季开始点、结束点、最大生长点等,并比较了不同数据源物候参数的差异。结果表明:日尺度Gcc数据能够很好的地反映地面环境因子的变化情况;多元线性回归分析表明研究区草原生长主要受土壤温度和太阳辐射的影响,受气温影响较小;高频的Gcc时间序列数据可以及时响应降水的变化;在相同年份,数码相机和卫星数据提取的物候参数具有一致性,但是在春季物候上差异较显著,生长速率和衰落速率的提取结果并不一致;Gcc提取的生长季长度与根据气温计算的生长度(GDD)长度相关性较好。本文评估了基于数码相机的植被物候监测及其与气象因子的关系,研究有助于多尺度植被物候建模与温带大草原区域的草原资源管理。