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摘要： 研究植被物候及其与气候之间的关系对于理解全球生态环境变化意义重大。近地面数字相机凭借其监测频率高、数据质量好等优势已成为一种有效的监测植被物候的遥感平台。以北美地区瓦瑞(Vaira Ranch)牧场为例,对研究区植被的春季生长情况进行监测,利用近地表数字相机获取的影像计算绿度相对亮度(Greenness Chromaticity Coordinates,Gcc)并构成时间序列,模拟植被春季物候,将所得植被物候信息分别与地面同步实测的总初级生产力(Gross Primary Production,GPP)以及气象数据进行对比分析。结果表明:研究区植被春季生长季开始于第20 d,结束于第145 d,Gcc与GPP的总体相关性为0.88,二者提取的7项物候指标平均相对差异为0.05;降雨、土壤湿度、空气温度、土壤温度、太阳辐射通量对植被生长存在影响:空气温度、土壤温度、太阳辐射通量三者整体对于Gcc变化的解释力为91.3%,其中,气温和土温的单因子解释力分别为30.9%和49.0%,此外,由于水分缺乏,降水成为制约研究区植被生长的重要因素。 关键词： 草地物候;  数字相机;  Gcc;  时间序列;  气象因子     Abstract: Studying vegetation phenology and the relationship between climate and phenology is of vital importance in understanding the change of global ecological environment.Near-surface digital camera has become an effective detection method in vegetation phenology due to its frequent and accurate data\|acquirement capability.Taking Vaira Ranch in North America for example,this study used digital camera based time series of Greenness Chromaticity Coordinates (Gcc) to monitor the growing condition in spring and to model its phonology.The phonological metrics were extracted from Gcc,after which GPP and meteorological data were used to compare and analyze with the extracted metrics.The results showed that vegetation within research area grew from the 20th to 145 th day of year,and the correlation index between Gcc and GPP was 0.88,the relative mean deviation of phonological features was 0.05.Meanwhile,precipitation,soil moisture,soil temperature and solar radiation are factors that can affect the vegetation growth.In details,the combination of atmosphere temperature,soil temperature and solar radiation can explain 91.3% of Gcc change,among which,atmosphere and soil temperature can explain the most of phonology with 30.9% and 49.0% individually.Furthermore,as the lacking of water content,precipitation behaves as an essential factor for inhibiting vegetation growth in our research area.

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