Description
Test data to reproduce results from publication "On adaptive smoothing for reconstructing reflectance time series for vegetation monitoring"
Contact
Contributors
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- Pieter Kempeneers
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- Raphaël d'Andrimont
How to cite
Kempeneers, Pieter; d'Andrimont, Raphaël (2023): Reflectance Time Series Reconstruction (RTSR) test data. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/51217c89-2d84-4995-a029-ec8bc494bacf
Keywords
reconstruction of time series remote sensing smoothing
Data access
Publications
- MDPI, BASEL, SWITZERLAND
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Abstract
Time series of optical remote sensing data are instrumental for monitoring vegetation dynamics, but are hampered by missing or noisy observations due to varying atmospheric conditions. Reconstruction methods have been proposed, most of which focus on time series of a single vegetation index. Under the assumption that relatively high vegetation index values can be considered as trustworthy, a successful approach is to adjust the smoothed value to the upper envelope of the time series. However, this assumption does not hold for surface reflectance in general. Clouds and cloud shadows result in, respectively, high and low values in the visible and near infrared part of the electromagnetic spectrum. A novel spectral Reflectance Time Series Reconstruction (RTSR) method is proposed. Smoothed values of surface reflectance values are adjusted to approach the trustworthy observations, using a vegetation index as a proxy for reliability. The Savitzky–Golay filter was used as the smoothing algorithm here, but different filters can be used as well. The RTSR was evaluated on 100 sites in Europe, with a focus on agriculture fields. Its potential was shown using different criteria, including smoothness and the ability to retain trustworthy observations in the original time series with RMSE values in the order of 0.01 to 0.03 in terms of surface reflectance.
Temporal coverage
| From date | To date |
|---|---|
| 2019-01-01 | 2019-12-31 |
Additional information
- Published by
- European Commission, Joint Research Centre
- Created date
- 2023-02-01
- Modified date
- 2023-04-26
- Issued date
- 2023-02-01
- Data theme(s)
- Science and technology
- Update frequency
- unknown
- Identifier
- http://data.europa.eu/89h/51217c89-2d84-4995-a029-ec8bc494bacf
- Popularity
-