This AVHRR_NDVI_readme.txt file was generated on 01.05.2024 by Sonia Dupuis
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GENERAL INFORMATION
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1. Title of dataset:40-year AVHRR top-of-atmosphere NDVI dataset

2. Contributor information: 

Contributors: Sonia Dupuis (UniBe), Pauline Rivoire (Université de Lausanne), Mira Barben (UniBe), Stefan Wunderle (UniBe)
Contact Information:
      Name: Sonia Dupuis
      Role/Function: Project Manager
      Institution: University of Bern
      Email: sonia.dupuis@unibe.ch
      
3. Date of data collection: 
1981-01-01 - 2022-31-12

4. Geographic location of data collection:
The AVHRR local area coverage (LAC) data covers Europe and North Africa.

5. Keywords describing the subject of your dataset:
Vegetation indices (NDVI), AVHRR, Earth observation, satellite imagery

6. Information about funding sources that supported the collection of the data:
Dr. Alfred Bretscher Fund (University of Bern)

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SHARING/ACCESS INFORMATION
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1. Licenses/restrictions placed on the data:CC-BY-SA

2. Links to publications that cite or use the data: 

- Rivoire, P., Dupuis, S., Guisan, A., and Vittoz, P.: Predicting Forest Damage in Europe: A Subseasonal-to-Seasonal Forecasting Approach for Hydro-meteorological Drivers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3242, https://doi.org/10.5194/egusphere-egu24-3242, 2024

- The two main publications are under review 

3. Recommended citation for this dataset: 
Dupuis, S., Rivoire, P., Barben, M.,  and Wunderle, S.: 40-year AVHRR top-of-atmosphere NDVI dataset, BORIS [data set], https://doi.org/10.48620/400, 2024

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DATA & FILE OVERVIEW
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1. AVHRR LAC VEGETATION INDICES 10-DAY
      File List: This dataset contains on average 36 NDVI tiles per year per satellite. 
      Filename: S_NDVI_avhrr_10d_composite_XXXXX_YYYY-YY-YY__v.2.0.nc, where XXXXX represents the satellite name and YYYY-YY-YY the time stamp.
      Short description: AVHRR LAC 10-DAY NDVI: Vegetation layer generated every 10 days for each satellite. 16 different satellites carrying the AVHRR have been in orbit between 1981 and 2022.
      Date of creation: October 2023

2. Relationship between files: Each file covers a specific 10-Day period and was recorded by a different satellite.

3. Are there multiple versions of the dataset?no

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METHODOLOGICAL INFORMATION
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1. Description of methods used for collection/generation of data: 
The AVHRR LAC Archive stored at the remote sensing group of the University of Bern (https://www.geography.unibe.ch/research/remote_sensing_group/) has been pre-processed according to the steps described here: https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://www.meteoswiss.admin.ch/dam/jcr:c17954a8-56cd-440f-bbbe-b340c429ba85/Final-report-fractional-snow-cover-time-series.pdf&ved=2ahUKEwid2tnxi_yFAxX1wAIHHWhoA7QQFnoECBMQAQ&usg=AOvVaw3f6YpvrsY1k-Q2BpT_iPS1 (GCOS Switzerland Project
Fractional snow cover time series (1981 – 2021) – a novel dataset from space to support climate studies in Switzerland, Helga Weber, 2022) and here : 10.1109/IGARSS47720.2021.9553397.
The NDVI composites are derived by computing the median NDVI value across 10 consecutive days (following method described in Asam, S.; Eisfelder, C.; Hirner, A.; Reiners, P.; Holzwarth, S.; Bachmann, M. AVHRR NDVI Compositing Method Comparison and Generation of Multi-Decadal Time Series—A TIMELINE Thematic Processor. Remote Sens. 2023, 15, 1631. https://doi.org/10.3390/rs15061631). A cloud mask is applied to filter out pixels contaminated with clouds. Pixels with a 30% probability of containing clouds (NWC-SAF/PPS probabilistic cloud mask (PPS2021-alpha)) are masked out.


2. Instrument- or software-specific information needed to interpret the data: The files are stored in the NetCDF format, therefore any tool able to interpret this format is fine.


3. Describe any quality-assurance procedures performed on the data:
The dataset has been validated against MODIS NDVI 16-Day composites for 2000-2020.



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DATA-SPECIFIC INFORMATION
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Each NDVI file (xarray dataset) contains 6 variables (data arrays):

- spatial_ref () : stores the spatial information
- ndvi (y, x): NDVI layer without spectral response function correction. Values are in the range (-1, 1)
- ndvi_srf_corr (y, x): NDVI layer with spectral response function.  Values are in the range (-1, 1)
- valid_pixels (y, x): number of valid pixels during a 10-day period (composite period)
- cloudy_pixels (y, x): number of pixels with cloud probability between 1% and 30 % (if above 30%, the pixel is not valid)
- Images_number (): total number of satellite images available during a 10-day period (composite period)


y: latitude
x: longitude
the layers are spatially encoded and referenced with geographic coordinates.

and two attributes:
- Satellite : name of the satellite
- number of images