Younes, Nicolas 1, Joyce, Karen1, Maier, Stefan1,2
1Centre for Tropical Environmental and Sustainability Science and School of Science and Engineering, James Cook University, Townsville/Cairns, Australia, 2Maitec, P.O. Box U19, Charles Darwin University, Darwin, Australia
Satellite-derived phenology is frequently used to illustrate changes in plant phenology and the effects of climate forcing. However, each study uses a different method to detect phenology. Plant phenology refers to the relationship between the life cycle of plants and weather and climate events. Phenology is often studied in the field, but recently studies have transitioned towards using satellite images to monitor phenology at the plot, country, and continental scales. The problem with this approach is that there is an ever-increasing variety of earth observation satellites collecting data with different spatial, spectral, and temporal characteristics. In this paper we ask if studies that detect phenology using different sensors over the same site produce comparable results. We hypothesize that apparent phenology changes with: 1) areal extent; 2) site location; 3) frequency of observation; 4) spatial resolution; 5) temporal coverage; and 6) the number of cloud contaminated observations. We used Landsat and Sentinel 2 imagery over the Darwin Harbor (Northern Territory, Australia) as case study, and found that apparent phenology does change with the sensor, site, and cloud contamination. Importantly, the apparent phenology is comparable between Landsat and Sentinel 2 sensors, but it is not comparable to phenology derived from MODIS. This is due to differences in the spatial resolution of the sensors. Cloud contamination also significantly changes the apparent phenology of vegetation. In this paper we expose the complexity of modelling phenology with remote sensing and help guide future phenology investigations.
Nicolas is an Environmental Engineer from Colombia, and has just finished his PhD at James Cook University. Recently, he focused on plant phenology, and is currently looking al Live Fuel Moisture Content for bushfire prediction and prevention.