All models of satellite-derived phenology are wrong, but some are useful: a case study from northern Australia

Younes, Nicolas, Joyce, Karen E., and Maier, Stefan M. (2021) All models of satellite-derived phenology are wrong, but some are useful: a case study from northern Australia. International Journal of Applied Earth Observation and Geoinformation, 97 (102285).

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Abstract

Satellite-derived phenology (or apparent phenology) is frequently used to illustrate changes in plant phenology (i.e. true 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. Mangrove forests are one example where different methods have been used to examine their apparent phenology. In general, plant phenology, including mangroves, is described using few individual plants, but continental-scale descriptions of phenological events are scarce or inexistent. Few attempts have been made to describe the phenology of mangroves using satellite imagery, and each study presents a different method. 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. Intuitively, one would assume that these hypotheses hold true, yet few studies have investigated this. For example, one would expect that clouds change the observed phenology of vegetation, that the number of species captured at spatial resolution will impact the apparent phenology, or that mangroves in different places display different phenologies, but how are these changes represented in the apparent phenology? We use the Enhanced Vegetation Index (EVI) to examine the changes in the start of season and peak growing season dates, as well as the shape and amplitude of the apparent phenology in each hypothesis. We use Landsat and Sentinel 2 imagery over the mangrove forests in Darwin Harbour (Northern Territory, Australia) as a 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.

Item ID: 65676
Item Type: Article (Research - C1)
ISSN: 1872-826X
Keywords: Landsat, Sentinel 2, Phenology, Time series analysis, Generalized additive models, Apparent phenology, Mangroves
Copyright Information: ©2020 The Author(s). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Funders: NIESGI Cia. Ltda., James Cook University (JCU), Wet Tropics Management Authority (WTMA), National Environment Science Program (NESP) Tropical Water Quality Hub, Centre for Tropical Water and Aquatic Ecosystem Research (TropWATER)
Projects and Grants: JCU Postgraduate Research Fellowship, WTMA Student Research Grant, NESP Research Grant, TropWater Student Research Grant
Date Deposited: 20 Jan 2021 01:53
FoR Codes: 41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410404 Environmental management @ 50%
41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410402 Environmental assessment and monitoring @ 50%
SEO Codes: 96 ENVIRONMENT > 9605 Ecosystem Assessment and Management > 960503 Ecosystem Assessment and Management of Coastal and Estuarine Environments @ 33%
96 ENVIRONMENT > 9609 Land and Water Management > 960902 Coastal and Estuarine Land Management @ 33%
96 ENVIRONMENT > 9603 Climate and Climate Change > 960305 Ecosystem Adaptation to Climate Change @ 34%
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