Phenology is the study of the timing of recurring biological life-cycle events, the causes of their timing with regard to abiotic and biotic factors, and the interrelation among phases of the same or different species at population and/or community level (Leith 1974). Many biological life-cycle events are highly sensitive to climatic changes, particularly temperature. Thus, abiotic factors considered in phenology studies typically include climatic variables (e.g. temperature, rain,…), but also habitat variables (e.g. elevation). Phenology studies make use of long-term records and typically investigate the variation within and between seasons of the timing of the biological events.
   Initially the term phenology was used to refer to the timing of the first occurrence of the studied biological event. This use agrees with the etymology of the term (derived from the Greek: φαίνω (phainō) = "to bring to light, make to appear" + λόγος (logos) = "study, discourse, reasoning"). For example, the first emergence of flowers and leaves, the first colouring and fall of leaves in deciduous trees, the initial appearance of migratory birds, or the first laying and hatching of eggs by oviparous animals. Currently, in the ecological literature it is used more broadly to refer to the time frame for any cyclical biological event, which often includes the dates of both the first and last appearance.


   As described above the main use of phenological records is to investigate the timing of recurring biological life-cycle events and the factors affecting this timing. In addition, because the timing of these biological events is highly sensitive to temperature and other climatic factors, historical phenological records can be used as a proxy for climatic variables when instrumental measurements are not available. Therefore, historical phenological records are highly valuable in the study of global warming and climate change.

Phenological measurements and research

   Historically many ancient civilizations (e.g. Egyptian, Mesopotamian, Roman, pre-Columbian, Chinese, and Japanese civilizations) have a knowledge of the timing of cyclical natural events, particularly those related to agriculture, acquired through observation. In fact, ancient civilizations often used their phenological knowledge to plan agricultural cycles.
   Formal recording of the time of cyclical biological events and their associated factors, as well as the use of measurement instruments, facilitated the transition to modern phenological research. A growing interest in the ecological impacts of climate change and technological advancements have driven a renewed scientific and public interest in phenology. This has been reflected, for example, in an approximately 10-fold increase in the number of peer-reviewed journal articles on plant phenology since 1980 (Tang et al. 2016; Figure 1).

Figure 1. Trends of papers and citations of vegetation phenology each year between year 1970 and 2014 (Figure from Tang et al. 2016). The statistics are from an ISI Web of Knowledge search conducted on 26 October 2015 using the following terms: (Title = Phenolog*) AND (Topic = vegetation oR Topic = plant).

 The main technical developments allowing this renewed interest in phenology have been advancements in remote sensing and near-surface observation equipment and methodology. These technical advancements have allowed an unprecedented availability and integration of data at multiple spatial and temporal scales (e.g. Zhang et al. 2003, Richardson et al. 2009). Finally, a multi-disciplinary approach to the way in which these data are investigated, integrating climate science, ecology, and evolutionary biology, is being crucial in the development of a deeper understanding of phenology and the advancement of phenological research (Tang et al. 2016).

Phenology and Remote Sensing

   Remote sensing (RS) imagery can be used to monitor and study the phenology of whole ecosystems and vegetation stands. In this case, the most successful approach has been to use a vegetation index (e.g. NDVI) and/or cover (e.g. fraction cover) data product derived from the RS data to characterised cycles of greening and browning (Figure 2). The data contained in a RS image pixel is an aggregate of what is perceived by the sensor in the corresponding area, rather than individual targets (i.e. particular species of shrubs or trees). Therefore, RS phenology data products only provide an approximation to the true biological cycle stages occurring in the field. Nevertheless, the temporal dynamics of the derived vegetation index or cover fraction closely tracks the typical green vegetation growth stages (i.e. emergence, growth/vigor, maturity, and senescence/harvest). Moreover, RS phenology data products can provide automated data streams acquired at large-scales (up to global). Consequently, RS imagery has opened a new field of phenological research that is complementary to traditional phenological methods.

Figure 2. NDVI temporal profile for a typical patch of coniferous forest over a period of six years (from Wikipedia This temporal profile depicts the growing season every year as well as changes in this profile from year to year due to climatic and other constraints. Data and graph are based on the MODIS sensor standard public vegetation index product. [We should replace this graph with our own when I get around creating my own. I would like to do one for the Tutorial; unfortunately, this is one of the data products that it is not currently available (from UTS)].

   The vegetation index (or cover fraction) temporal curves can be analysed to obtain useful information about the vegetation phenological cycles. Phenological cycles are defined as a period of greening and browning measured via a vegetation index (or cover fraction) that may occur at any time of the year, extend across the end of a year, skip one or multiple years, and/or occur more than once a year. Multiple phenological cycles within a year can occur in the form of double cropping in agricultural areas or be caused by a-seasonal rain events in water-limited environments. Derived phenological curve parameters can include point metrics (i.e. the location and values of minimum/valley and maximum/peak points), and cycle metrics (i.e. start, end, length and amplitude of cycles). The integrated vegetation index under the curved between the start and end of a cycle can also be computed as a proxy for productivity.
   RS phenology data products have been successfully used in multiple applications. For example by studying temporal changes in EVI, the Amazon rainforest have been shown to exhibit growth spurts during the dry season, which is contrary to the traditional view of growth only occurring during the wet season (Huete et al. 2006). Similarly, using a RS phenology approach vegetation productivity in boreal forest has been shown to be positively related to temperature and length of the growing season (Myneni et al. 1997).


  • Huete, A. R., K. Didan, Y. E. Shimabukuro, P. Ratana, S. R. Saleska, L. R. Hutyra, W. Yang, R. R. Nemani, and R. Myneni (2006). “Amazon rainforests green-up with sunlight in dry season”. Geophysical Research Letters, 33.
  • Leith H. (1974). “Phenology and Seasonality modelling”. Ecological Studies series. Series volume 8.
  • Myneni, R.B., Keeling, C.D., Tucker, C.J., and Asrar, G. (1997). “increased Plant Growth in the Northern High Latitudes from 1981 to 1991”. Nature 386(6626).
  • Richardson, A.D., Braswell, B.H., Hollinger, D.Y., Jenkins, J.P., and Ollinger, S.V. (2009). “Near- surface remote sensing of spatial and temporal variation in canopy phenology”. Ecological Applications, 19.
  • Tang, J., Korner, C., Muraoka, H., Piao, S., Shen, M., Thackeray, S.J., and Yang, X. (2016). “Emerging opportunities and challenges in phenology: A review”. Ecosphere, 7(8).
  • Zhang, Y.Y., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F>, Gao, F., Reed, B.C., and Huete, A. (2003). “Monitoring vegetation  phenology  using  MoDIS”. Remote Sensing of Environment 84.