South Sudan: NDVI at Subnational Level

This dataset is part of the South Sudan Data Grid
  • 200+ Downloads
  • This dataset updates: Every two weeks
This dataset is part of the data series [?]: WFP - NDVI at Subnational Level
Additional information
Time Period of the Dataset [?]
July 01, 2002-February 10, 2025 ... More
Modified [?]
13 February 2025
Dataset Added on HDX [?]
13 December 2022 Less
Expected Update Frequency
Every two weeks
Location
Source
National Aeronautics and Space Administration (NASA) & WFP
Methodology

The Normalized Difference Vegetation Index (NDVI) is obtained from combining the raw MODIS AQUA & TERRA 16 day composite products (MYD13C1 & MOD13C1) into 8 day synthetic product. This synthetic product is then filtered and gap-filled using a Whittaker filter with V-curve optimization to remove atmospheric noise and fill missing data. The filtered 8-day data is then interpolated and averaged to dekads.

The filtered 10-day NDVI is averaged by subnational administrative units defined by WFP datasets, and the subsequent indicators (averages and anomalies) are calculated directly from the averaged data.

The anomalies (in %) are calculated in reference to the long term average of the timeseries, which is calculated over the reference period 2002-07-01 to 2018-07-01.

To provide valid dates, the dekads are mapped to their start date. Therefore, the 1st dekad corresponds to the 1st of each month, the 2nd dekad to the 11th and the 3rd dekad to the 21st.

For further information on the Whittaker filter:

  • Paul H. C. Eilers Analytical Chemistry 2003 75 (14), 3631-3636 DOI: 10.1021/ac034173t
  • P. H. C. Eilers, V. Pesendorfer and R. Bonifacio, "Automatic smoothing of remote sensing data," 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), 2017, pp. 1-3, doi: 10.1109/Multi-Temp.2017.8076705.
Caveats / Comments

Due to the nature of the Whittaker filter, the update of the timeseries (i.e. the addition of a new dekad) has an effect of preceding dekads which diminishes going back in time. Therefore slight variations can be expected for a certain number of dekads for every update.

File Format
Visibility
Public
Export metadata for this dataset: JSON | CSV
[{"value": 2, "date": "2024-09-02"}, {"value": 6, "date": "2024-09-09"}, {"value": 2, "date": "2024-09-16"}, {"value": 3, "date": "2024-09-23"}, {"value": 2, "date": "2024-09-30"}, {"value": 2, "date": "2024-10-07"}, {"value": 1, "date": "2024-10-14"}, {"value": 2, "date": "2024-10-21"}, {"value": 1, "date": "2024-10-28"}, {"date": "2024-11-04", "value": 0}, {"date": "2024-11-11", "value": 0}, {"value": 2, "date": "2024-11-18"}, {"value": 1, "date": "2024-11-25"}, {"value": 2, "date": "2024-12-02"}, {"value": 2, "date": "2024-12-09"}, {"date": "2024-12-16", "value": 0}, {"date": "2024-12-23", "value": 0}, {"value": 2, "date": "2024-12-30"}, {"value": 6, "date": "2025-01-06"}, {"value": 3, "date": "2025-01-13"}, {"value": 3, "date": "2025-01-20"}, {"value": 1, "date": "2025-01-27"}, {"value": 2, "date": "2025-02-03"}, {"value": 1, "date": "2025-02-10"}, {"value": 2, "date": "2025-02-17"}]

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Data and Resources [2]