Phenology - MODIS, derived from MOD13C1 EVI, Australia coverage

Link to the data

Data licence and Access rights

Alternate title

The Australian phenology product from MOD13C1(16-days,5600 m) - covering Australia

Abstract or Summary

The Australian Phenology Product is a continental data set that allows the quantitative analysis of Australia’s phenology derived from MODIS Enhanced Vegetation Index (EVI) data using an algorithm designed to accommodate Australian conditions. The product can be used to characterize phenological cycles of greening and browning and quantify the cycles’ inter and intra annual variability from 2000 to 2015 across Australia.

Phenological cycles are defined as a period of EVI-measured greening and browning that may occur at any time of the year, extend across the end of a year, skip a year (not occur for one or multiple years) 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.

Based on per-pixel greenness trajectories measured by MODIS EVI, phenological cycle curves were modelled and their key properties in the form of phenological curve metrics were derived including: the first and second minimum point, peak, start and end of cycle; length of cycle, and; the amplitude of the cycle. Integrated EVI under the curve between the start and end of the cycle time of each cycle is calculated as a proxy of productivity.

Spatial and Temporal extents

Point of contact

Credit

The development of the Australian Phenology Product was funded by the AusCover Facility of the Australian Terrestrial Ecosystem Research Network (TERN) and supported by ARC-DP1115479 grant entitled "Integrating remote sensing, landscape flux measurements, and phenology to understand the impacts of climate change on Australian landscapes" (Huete, CI). Calculations were preformed on the University of Technology, Sydney eResearch high performance computing facility.

Keywords

There are three main thesauri that AusCover recommends:

1. Global Change Master Directory (http://gcmd.nasa.gov)
2. Climate and Forecast (CF) convention standard names (http://cfconventions.org/standard-names.html).
3. Fields of Research codes (http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E?opendocument).

Data quality

The quality of the phenological parameters could be limited by the spatial-temporal resolution of the input data, sub pixel clouds, and by the ‘local’ applicability of the gap filling, smoothing, and curve fitting methodology within the phenology algorithm that is applied at national scale.

The dataset covers all of Australia. The input dataset is the complete 2000-2012 MOD13C1 16-day record. Individual pixel observations with less than a “good data” MOD13C1 QA flags are gap filled (interpolated).

Less than “good data” MOD13C1 QA flags occur more frequently during the monsoon season at tropical latitudes near the coast and during the southern hemisphere winter at higher latitudes. The west coast of Tasmania and high elevation areas in South Eastern Australia also have more frequent cloud cover causing data gaps. The gap filling scheme used by the phenology algorithm overcomes the issue of missing observation in most cases except for areas with persistent cloud cover.

Less than “good data” MOD13C1 QA flags occur with very high frequency over highly reflecting surfaces (salt crusts) in the continent’s interior. The gap filling method and the subsequently derived phenological metrics should not be considered reliable.

Seasons in Australia do only in certain regions ‘fit’ within a calendar year. The implication for data completeness is that part of a season that occurred in the beginning of 2000 or in late 2012 may not be captured by the derived phenological metrics as the season’s complete trajectory is not covered by the input data time series.

Validation status

Validation of seasonal/phenologic parameters is accomplished across a wide range of Australian landscapes through comparisons with finer resolution (half-hourly/ daily) OzFlux eddy covariance tower measures of gross primary productivity (GPP) and evapotranspiration (ET). Tower measurements of actual photosynthetic activity (GPP) primarily relate to onset, duration, magnitude, and growing season length. Tower measures of ET potentially provide more accurate measures of vegetation green-up and duration in the arid and semiarid landscapes. At regional levels, we plan validation activities focused on the use of phenocam networks that capture the seasonal dynamics and species-level phenology of overstory and understory plant functional types. This is being prototyped in NSW and thenceforth to be expanded to TERN supersites, and transects. We are exploring the availability, prevalence, and cost-effectiveness of using CSIRO's Wireless Sensor Network technology for 'light' sensors. The combined use of a PAR sensor (400-700nm) and total radiation sensor (0.4 to 3.5um) enables the calculation of Visible/ Near-infrared based broadband vegetation indices yielding in situ measures of seasonal vegetation profiles.

Related products

References

Algorithm summary

R software, Savitzsky-Golay filtering and double-logistics curve modelling is used to generate the entire set of seasonal metrics from MOD13C1 EVI data cubes. The product can be updated annually.

Product version history

Metadata history