Statistical Models for Global Phenological Phenomena

• Activity Number: 195
• Type: Contributed
• Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Maggie Johnson*+ and Petrutza Caragea
• Companies: Iowa State University and Iowa State University
• Keywords: Spatio-temporal models ; Massive spatial data ; Phenology ; Climate Change Impact

Abstract

Understanding the effects of phenological events, due to both natural and man-made causes, is critical for research in global climate modeling and agriculture, among many others. During the last two decades, remote sensing data on satellite derived biophysical variables (such as chlorophyll content) have become widely available through the launch of satellites such as MERIS (MEdium Resolution Imaging Spectrometer). Weekly MTCI aggregates from 2003 to 2007 were used to model phenological changes in southern India. Three modeling techniques -- a classical time series model with seasonality represented by using Fourier terms, a method integrating the decomposition of time series into season, trend, and white noise components with methods for detecting significant changes (BFAST), and a hierarchical model incorporating spatially distributed covariates such as land use and elevation -- were used to extract the phenological variables of onset of greenness, peak of greenness, and end of senescence using an iterative search. The advantages and shortcomings of the three methods are compared and discussed in terms of phenological variable extraction and efficacy across spatial locations.