PROGRESS IN GEOGRAPHY ›› 2016, Vol. 35 ›› Issue (3): 304-319.doi: 10.18306/dlkxjz.2016.03.005

• Reviews • Previous Articles     Next Articles

Review of influencing factors of accuracy of plant phenology monitoring based on remote sensing data

Deqin FAN1(), Xuesheng ZHAO1, Wenquan ZHU2,*(), Zhoutao ZHENG2   

  1. 1. College of Geosciences and Survey Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China
    2. College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China
  • Received:2015-06-01 Accepted:2015-08-01 Online:2016-03-25 Published:2016-03-25
  • Contact: Wenquan ZHU E-mail:kinly129@163.com;zhuwq75@bnu.edu.cn
  • Supported by:
    National Natural Science Foundation of China, No.41371389, No.41171306

Abstract:

Monitoring plant phenology with remote sensing data has important scientific value for studying the response of vegetation to climate change. A comprehensive analysis on the influencing factors of accuracy of plant phenology estimation based on principles and general technical processes of remote sensing application in vegetation monitoring was carried out by taking into account the following four aspects: the specific vegetation type and its geographical conditions; remote sensing data and pre-processing; techniques used to identify plant phenometrics; and evaluation of satellite-derived plant phenometrics. Potential methods for improving the accuracy of plant phenology monitoring are thoroughly discussed. These include: building high-resolution near-surface sensor-derived phenology observation and sharing network; developing universally applicable methods for noise removal of satellite remote sensing time-series data and reconstruction of vegetation index curves; searching more stable methods to estimate plant phenology; and exploring the possibility of synthesizing ground-based observation, remote sensing monitoring, and model simulation to achieve the spatial scaling-up of phenometrics.

Key words: plant phenology, remote sensing, vegetation index, time series, accuracy, influencing factor, review