PROGRESS IN GEOGRAPHY ›› 2009, Vol. 28 ›› Issue (1): 33-40.doi: 10.11820/dlkxjz.2009.01.005

• Original Articles • Previous Articles     Next Articles

Progress in Remote Sensing Phenological Research

CHEN Xiaoqiu, WANG Linhai   

  1. College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
  • Received:2008-06-01 Revised:2008-12-01 Online:2009-01-24 Published:2009-01-24

Abstract:

Plant phenological phenomena are the most salient and sensitive bio-indicators of the environmental change at seasonal and interannual scales. Timings of plant phenological phenomena can indicate the rapid response of terrestrial ecosystems to climate change. Since the remote sensed phenology observation is characterized by multi-temporal, broad coverage, spatial continuality, and relatively long time series, recently, it has been an important means for detecting responses and feedbacks of vegetation dynamics to global climate change. On the basis of introducing remote sensing data sets and processing methods for monitoring plant phenology, we systematically reviewed important progresses in remote sensing phenology during the last five years worldwide focusing on identification of the phenological growing season, plant phenology and climate change, plant phenology and net primary production, plant phenology and land cover, and plant phenology and crop yield estimate, and so on. Then, we pointed out some existing problems in the current research, and tried to propose some main research aspects in the near future as follows: (1) developing a kind of more general technique for identifying the phenological growing season using remote sensing data; (2) unifying surface observed and satellite derived spatial information by carrying out plant community phenology observations and selecting appropriate scale transition methods; (3) analyzing quantitatively response mechanisms of plant phenology to human activities; (4) implementing amalgamation of remote sensing data with different spatial resolutions using suitable mathematical methods and models; and (5) estimating possible responses of plant phenology to future climate change by dynamic simulations.

Key words: climate change, crop yield estimate, growing season, land cover, net primary production, remote sensing phenology