【摘要】 利用遥感开展植被物候的研究涉及到物候提取方法这一基本问题,但当前不同方法的优劣尚无定论,亟待开展不同方法的综合评价。本研究利用2009年的叶面积指数数据,基于18种植被物候遥感提取组合,联立23个地面站点的物候观测数据,利用偏差等5个定量指标综合评价不同遥感提取组合对植被生长季开始时期(SOS)、结束时期(EOS)和长度(LOS)的提取精度。结果表明:基于SG-Sa 0.1(SG滤波法+阈值为0.1的季节性振幅法)组合能够较好地提取地面23个站点的总体植被物候;针对单个物候期,SOS、EOS和LOS的最优方法分别为SG-Sa 0.1、SG-Sa 0.3和DL-Sa 0.1,但提取结果与地面观测物候日期的最小偏差均超过5 d,显示了利用时间分辨率为8 d的遥感数据提取植被物候可以达到的精度水平; SOS、EOS和LOS各自最优与对应的最差方法提取的物候期差值分别可达到-51.55、19.06和86.33 d,说明选取适宜遥感提取方法的重要性。此外,遥感提取物候和地面观测物候不能完全匹配,采用多重评价方法,特别是偏差一致性方法可以有效选取出最优的遥感提取组合。更多还原

【Abstract】 Analyzing vegetation phenology is the key step for the accuracy evaluation of extraction methods using remote sensing. There is no consensus about the advantages and disadvantages of different methods. It is necessary to comprehensively evaluate the accuracy of various extraction methods. In this study,18 combining methods for vegetation phenology remote sensing extraction and observation data from 23 ground phenology stations were used to evaluate the accuracy in extracting three key vegetation phenophases: Start Of the growing Season( SOS),End Of the growing Season( EOS) and Length Of the growing Season( LOS). The results showed that SG-Sa 0.1( the combining Savitzky-Golay filter and Seasonal Amplitude method with a threshold of 0.1) had the optimal recognition effect. For single phenophase,the optimal combination for SOS,EOS and LOS was SG-Sa 0.1,SG-Sa 0.3 and DL-Sa 0.1,respectively. The minimum deviation between the optimal extraction results and the ground-based observation phenology data were more than five days,indicating the accuracy of extraction phenology using remote sensing data with 8-day temporal resolution. The deviation highlighted the importance of choosing a suitable extraction combination. In addition,remote sensing phenology and ground-based phenology cannot completely match. Therefore,the multiple evaluation indices used in this study,especially the deviation consistency can effectively select the optimal vegetation phenology remote sensing extraction combination for various phenophases.更多还原