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| Titel |
Phenological indicators extraction from dense time-series of Landsat data |
| VerfasserIn |
Anna Borghi, Francesco Vuolo, Arianna Facchi |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250133852
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| Publikation (Nr.) |
 EGU/EGU2016-14509.pdf |
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| Zusammenfassung |
| Time series of remotely sensed vegetation indices are valuable data sets in various Earth
science fields. In particular, they have been successfully used to map vegetation phenology.
This information can be used into physically-based hydrological models to estimate crop
water requirements (e.g. Pôças et al., 2015; Consoli & Vanella, 2014; Er-Raki et al.,
2007).
Most of the phenology detection studies aimed to capture single seasonal crop growth
cycles per year. However, the phenological variability in agriculture, especially connected
with winter crops interposed to summer crops, demonstrates the necessity of deriving more
than one crop cycle per year (e.g. Patel & Oza, 2014; Li et al., 2014). Moreover,
remote sensing of phenology has been largely applied using MODIS normalized
difference vegetation index (NDVI) data with a spatial resolution of 250 m, which is
often not sufficient to resolve highly fragmented agricultural land surfaces. The
opportunities for deriving phenological indicators at high spatial resolution improved
radically in 2008 when the Landsat program opened its archive (Woodcock et al.
2008).
In this study, we present an approach to detect phenological indicators aimed to
characterize vegetation dynamics in agricultural land surfaces. The proposed algorithm was
applied to time series of bi-weekly smoothed and gap-filled Landsat Surface Reflectance
Climate Data Record (CDR) data from 2012 to 2014 for a pilot area in the Marchfeld region,
Lower Austria.
The analytic procedure can be summarized in the following steps. First, the surface
reflectance of Landsat CDR data is smoothed and gap-filled using a state-of-the art Whittaker
algorithm to create a time series of 24 images per year, regularly spaced in time (Vuolo et al.,
in preparation). NDVI and fAPAR (fraction of Absorbed Photosynthetically Active
Radiation) are then derived and used as input to calculate phenology. Using a moving window
approach, the multi-temporal time series are analysed to extract local maxima and
minima for each pixel. The resulting values are automatically screened to identify the
absolute maxima and minima for each crop cycle. Finally, the algorithm estimates the
timing of key phenological periods (i.e. green-up, maximum and senescence) for
each pixel. Accuracy assessment is carried out through the visual interpretation of
several crop growth curves and using a land cover/land use dataset to analyse the
results.
The results show that the method can successfully extract phenological indicators from
dense smoothed and gap-filled time series, both for summer and winter crops. In addition, the
comparison between phenologies extracted from each vegetation indices (NDVI and fAPAR)
shows a good agreement (R2 = 0.70).
Future effort will be dedicated to apply the proposed approach to Landsat time series for
other areas of interest. Furthermore, the method will be improved by calibrating and
validating the results for the pilot study based on ground truth data. The phenological
indicators will be then assimilated into a hydrological model to estimate crop water
requirements at basin scale. |
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