#  1.Introduction

##  1.1 Project Overview

The main objective of BETTER is to implement an EO Big Data intermediate
service layer devoted to harnessing the potential of the Copernicus and
Sentinel European EO data directly from the needs of the users.

BETTER aims to go beyond the implementation of generic EO data tools and
incorporate those tools with user experience, expertise and resources to
deliver an integrated EO intermediate service layer. This layer will deliver
customized solutions denominated Data Pipelines for large volume EO and non-EO
datasets access, retrieval, processing, analysis and visualisation. The BETTER
solutions will focus in addressing the full data lifecycle needs associated
with EO Big Data to bring more downstream users to the EO market and maximise
exploitation of the current and future Copernicus data and information
services.

BETTER developments will be driven by a large number of Data Challenges to be
set forward by the users deeply involved in addressing the Key Societal
Challenges.

The World Food Programme, the European Union Satellite Centre and the Swiss
Federal Institute of Technology- Zurich working in the areas of Food Security,
Secure Societies and GeoHazards will be the challenge promoters.

During the project each promoter will introduce 9 challenges, 3 in each
project year, with an additional nine brought by the “Extending the market”
task, in a total of 36 challenges. The Data Pipelines will be deployed on top
of a mature EO data and service support ecosystem which has been under
consolidation from previous R&D activities.

The ecosystem and its further development in the scope of BETTER rely on the
experience and versatility of the consortium team responsible for service/tool
development from DEIMOS and Terradue. This is complemented by Fraunhofer
Institute’s experience in Big Data systems, which brings to the consortium
transversal knowledge extraction technologies and tools that will help bridge
the current gap between the EO and ICT sectors.  
  
<table>  
<tr>  
<th>

**General Information**

</th> </tr>  
<tr>  
<td>

Project Title

</td>  
<td>

Big-data Earth observation Technology and Tools Enhancing Research and
development

</td> </tr>  
<tr>  
<td>

Starting Date

</td>  
<td>

1st November 2017

</td> </tr>  
<tr>  
<td>

Duration in

Months

</td>  
<td>

36

</td> </tr>  
<tr>  
<td>

Call (part) Identifier

</td>  
<td>

H2020-EO-2017

</td> </tr>  
<tr>  
<td>

Topic

</td>  
<td>

EO-2-2017

EO Big Data Shift

</td> </tr>  
<tr>  
<td>

Fixed EC Keywords

</td>  
<td>

Visual techniques / Visual analytics / Intelligent data understanding, Earth
Observation /

Services and applications, Space data exploitation, Data mining and searching
techniques, Downstream industry

</td> </tr>  
<tr>  
<td>

Free Keywords

</td>  
<td>

Data Challenges, Data Pipelines

</td> </tr> </table>

This project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No 776280\.

##  1.2 Scope

This document defines the Data Management approach for the datasets used and
generated in the project.

##  1.3 Responsibilities

The table below provides information on who contributed to this document and
to which sections.

Table 3 Document Responsibilities  
  
<table>  
<tr>  
<th>

**Name**

</th>  
<th>

**Institution**

</th>  
<th>

**Responsibilities**

</th> </tr>  
<tr>  
<td>

Diego Lozano García

</td>  
<td>

DMS

</td>  
<td>

All sections

</td> </tr>  
<tr>  
<td>

Nuno Grosso

</td>  
<td>

DME

</td>  
<td>

Section 2.1 and Revision

</td> </tr>  
<tr>  
<td>

Fabrice Brito

</td>  
<td>

TDUE

</td>  
<td>

Section 2.2, 3.1, 3.2 and 3.3

</td> </tr>  
<tr>  
<td>

Pedro Gonçalves

</td>  
<td>

TDUE

</td>  
<td>

Revision

</td> </tr>  
<tr>  
<td>

Koushik Panda

</td>  
<td>

DME

</td>  
<td>

Revision

</td> </tr>  
<tr>  
<td>

Simon Scerri

</td>  
<td>

IAIS

</td>  
<td>

Revision

</td> </tr>  
<tr>  
<td>

Thomas Filippa

</td>  
<td>

DMS

</td>  
<td>

Section 2.1

</td> </tr> </table>

##  1.4 Document Structure

This document is structured as following:

  * Section One provides a project overview and then goes on to describes the scope, responsibilities and structure of this deliverable. 

  

  * Section Two will describe the datasets used/generated by the data pipelines. 

  

  * Section Three will analyse each of the aspects of the FAIR data: Findable, Accessible, Interoperable and Re-use 

  

  * Section Four will present the resources allocation and deal with data security 

  

#  Data Summary

##  Datasets used/generated by the Data pipelines

The tables below provide the main information related to the data sets
management: the Data format, Preferential Data source, Access restrictions,
area of interest, the Expected data Volume and the long-term data preservation
policy. The data sets generated by the BETTER data processing pipelines are
raster files in GeoTIFF format. Currently, the tables below provide the list
of input and output datasets used/generated by the Data pipelines defined in
the first and second cycle of the project. They will be updated in the next
version of the document in order to include the datasets of the last cycle
(no-defined yet) and modify some fields partially defined yet.  
  
<table>  
<tr>  
<th>

**Input datasets included in all first cycle challenges**

</th> </tr>  
<tr>  
<td>

**Name**

</td>  
<td>

**Challenges involved**

</td>  
<td>

**Data format**

</td>  
<td>

**Preferential Data source**

</td>  
<td>

**Access restrictions**

</td>  
<td>

**Area of interest**

</td>  
<td>

**Expected data Volume**

</td>  
<td>

**Long-term data preservation**

</td> </tr>  
<tr>  
<td>

Sentinel-1 GRD acquisitions of matching orbit directions

</td>  
<td>

WFP-01-01 -

Hazards and Change detection using Sentinel-

1 SAR data

</td>  
<td>

SAFE

</td>  
<td>

Sentinel Open Data Hub

</td>  
<td>

\-

</td>  
<td>

AOI 1 NW - South Sudan

POLYGON((26.832 9.5136,

28.6843 9.5136, 28.6843

7.8009, 26.832 7.8009, 26.832

9.5136))

AOI 2 Renk - Blue Nile

POLYGON((32.0572 12.4549,

33.9087 12.4549, 33.9087

10.7344, 32.0572 10.7344,

32.0572 12.4549))

AOI 3 Niger Delta, Mali (updated after D2.2 delivered to EC) POLYGON((-5.5
17.26, -1.08

17.26, -1.08 13.5, -5.5 13.5, 5.17 17.26))

AOI 4 NE - Nigeria

POLYGON((12.9415 13.7579,

14.6731 13.7579, 14.6731

12.0093, 12.9415 12.0093,

12.9415 13.7579))

</td>  
<td>

~ 40

Sentinel1A/B / 12 days ~ 63 GB / 12 days (1.58 GB / product)

</td>  
<td>

\-

</td> </tr>  
<tr>  
<td>

SRTM 30m/90m

for Terrain Correction

</td>  
<td>

Geotiff

</td>  
<td>

NASA LTA

</td>  
<td>

\-

</td>  
<td>

\-

</td>  
<td>

\-

</td> </tr>  
<tr>  
<td>

S1 precise orbit file(s)

</td>  
<td>

SAFE

</td>  
<td>

Sentinel Open Data Hub

</td>  
<td>

\-

</td>  
<td>

\-

</td>  
<td>

\-

</td> </tr>  
<tr>  
<td>

Sentinel-1 GRD

</td>  
<td>

WFP-01-02 -

</td>  
<td>

SAFE

</td>  
<td>

Sentinel Open

</td>  
<td>

\-

</td>  
<td>

AOI 1 Niger

</td>  
<td>

~ 40

</td>  
<td>

\-

</td> </tr> </table>

This project has received funding from the European Union’s Horizon 2020
Research and

Innovation Programme under grant agreement no 776280  
  
<table>  
<tr>  
<th>

</th>  
<th>

Land cover changes and inter-annual vegetation performance

</th>  
<th>

</th>  
<th>

Data Hub

</th>  
<th>

</th>  
<th>

POLYGON((6.4788 14.5973,

7.5577 14.5973, 7.5577

13.6328, 6.4788 13.6328,

6.4788 14.5973))

AOI 2 Tajikistan

POLYGON((67.7116 37.9032,

68.791 37.9032, 68.791

36.9211, 67.7116 36.9211,

67.7116 37.9032))

AOI 3 Mali

POLYGON((-10.3668 15.3471, -

9.3518 15.3471, -9.3518

14.3406, -10.3668 14.3406, 10.3668 15.3471))

AOI 4 Afghanistan

POLYGON((67.6243 36.7228,

68.116 36.7228, 68.116

35.6923, 67.6243 35.6923,

67.6243 36.7228))

</th>  
<th>

Sentinel1A/B / 12 days ~ 63 GB / 12 days (1.58 GB / product)

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Sentinel-2 L1C

</th>  
<th>

WFP-01-02 - Land cover changes and inter-annual vegetation

performance

</th>  
<th>

SAFE

</th>  
<th>

Sentinel Open Data Hub

</th>  
<th>

\-

</th>  
<th>

~ 75

Sentinel2A/B / 10 days ~ 75 GB / 10 days (~1 GB / product)

</th>  
<th>

\-

</th> </tr>  
<tr>  
<th>

Landsat 8 L1C Reflectances

</th>  
<th>

WFP-01-02 - Land cover changes and inter-annual vegetation performance

</th>  
<th>

Geotiff

</th>  
<th>

EarthExplorer

</th>  
<th>

\-

</th>  
<th>

~ 15 Landsat

8 / 16 days ~ 15 GB / 16 days (~1 GB / product)

</th>  
<th>

\-

</th> </tr>  
<tr>  
<td>

Copernicus (VGTProbaV) 1Km LAI time series. NRT product (RT0, RT2 and RT6)

</td>  
<td>

WFP-01-03 EO indicators

for global Early

Warning,

Seasonal Monitoring and

Climatology

Studies

</td>  
<td>

netCDF

</td>  
<td>

Copernicus Land

Monitoring

Service

</td>  
<td>

\-

</td>  
<td>

All WFP regions

</td>  
<td>

1 product

LAI: ~310MB,

</td>  
<td>

\-

</td> </tr>  
<tr>  
<td>

Copernicus (VGTProbaV) 1Km fAPAR time

</td>  
<td>

WFP-01-03 EO indicators for global Early

</td>  
<td>

netCDF

</td>  
<td>

Copernicus Land

Monitoring

Service

</td>  
<td>

\-

</td>  
<td>

All WFP regions

</td>  
<td>

1 product

FAPAR: ~380

MB;

</td>  
<td>

\-

</td> </tr> </table>  
<table>  
<tr>  
<th>

series. NRT product (RT0, RT2 and RT6)

</th>  
<th>

Warning,

Seasonal Monitoring and

Climatology

Studies

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Time series of

MODIS MOD11C2

LST 2000-present

</td>  
<td>

WFP-01-03 EO indicators

for global Early

Warning,

Seasonal Monitoring and

Climatology

Studies

</td>  
<td>

HDF

</td>  
<td>

NASA LPDAAC

</td>  
<td>

\-

</td>  
<td>

All WFP regions

</td>  
<td>

\-

</td>  
<td>

\-

</td> </tr>  
<tr>  
<td>

CHIRPS RFE 5Km resolution daily

data

</td>  
<td>

WFP-01-03 EO indicators

for global Early

Warning,

Seasonal Monitoring and

Climatology

Studies

</td>  
<td>

Geotiff

</td>  
<td>

Climate Hazards

Group InfraRed

Precipitation with

Station data

</td>  
<td>

\-

</td>  
<td>

All WFP regions

</td>  
<td>

1.77 GB / 1 month (CHIRPS: ~58 MB per product)

</td>  
<td>

\-

</td> </tr>  
<tr>  
<td>

Sentinel-2 L1C

</td>  
<td>

SATCEN-0101 - Thematic

Indexes for

Land Use

Indentification

</td>  
<td>

SAFE

</td>  
<td>

Sentinel Open Data Hub

</td>  
<td>

\-

</td>  
<td>

AOI 1 Columbia - Cauca-Narino

2°44'38.51"N 78°19'27.30"W, 2°

5'57.77"N 77°14'51.12"W,

0°55'38.70"N 78° 1'49.27"W,

1°34'56.24"N 79° 0'39.68"W

AOI 2 Albania border with

Greece

</td>  
<td>

~ 220

Sentinel2A/B L2A / 10 days

~ 220 GB / 10 days ( ~1 GB / product)

</td>  
<td>

\-

</td> </tr> </table>  
<table>  
<tr>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

41° 0'57.81"N 20°44'47.45"E,

40°51'0.67"N 21°17'1.40"E,

39°34'1.65"N 20°22'47.57"E,

39°52'46.66"N 19°53'54.20"E

AOI 3 Afghanistan - Helmand - Nad-e-Ali

73°22'24.90"W

64°44'54.92"E,32° 0'17.86"N 65°

0'7.32"E, 30°25'32.61"N

63°57'57.16"E, 31°31'55.09"N

63°55'9.38"E

AOI 4 Serbia

45°56'55.10"N 18°42'39.05"E,

45°57'44.72"N 18°59'43.44"E,

45° 7'2.00"N 19°40'15.66"E,

44°48'47.50"N 18°55'57.11"E

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Sentinel-1 A/B 1 SLC StripMap

</td>  
<td>

SATCEN-0102 - Change Detection based on SAR single look complex (SLC) data

</td>  
<td>

SAFE

</td>  
<td>

Sentinel Open Data Hub

</td>  
<td>

\-

</td>  
<td>

AOI 1 - 15°18'50.37"N,

8°22'52.84"E, 15°28'49.28"N,

10° 2'15.33"E, 12°17'43.44"N,

10°38'54.40"E, 12° 9'54.62"N, 9°

2'22.33"E

AOI 2 - 23° 8'15.91"N,

12°55'57.41"E, 23°27'11.02"N,

14°28'12.27"E, 19° 6'4.65"N,

15°24'34.73"E, 18°52'42.78"N,

13°50'31.39"E

AOI 3 20° 6'22.43"N,

5°20'58.79"E, 20°13'57.72"N, 6°

3'52.23"E, 18°30'49.60"N,

6°25'48.99"E, 18°23'13.00"N,

5°42'55.45"E

</td>  
<td>

~ 380

Sentinel1

A/B / YEAR

~ 3 TB / YEAR ( ~8 GB / product)

</td>  
<td>

\-

</td> </tr> </table>  
<table>  
<tr>  
<th>

Sentinel-2 L2A

</th>  
<th>

SATCEN-01-

03 - Illicit Crop

Monitoring with Optical data

</th>  
<th>

SAFE

</th>  
<th>

Sentinel Open Data Hub

</th>  
<th>

\-

</th>  
<th>

AOI 1 Columbia - Cauca-Narino

2°44'38.51"N 78°19'27.30"W, 2°

5'57.77"N 77°14'51.12"W,

0°55'38.70"N 78° 1'49.27"W,

1°34'56.24"N 79° 0'39.68"W

AOI 2 Albania border with

Greece

41° 0'57.81"N 20°44'47.45"E,

40°51'0.67"N 21°17'1.40"E,

39°34'1.65"N 20°22'47.57"E,

39°52'46.66"N 19°53'54.20"E

AOI 3 Afghanistan - Helmand - Nad-e-Ali

73°22'24.90"W

64°44'54.92"E,32° 0'17.86"N 65°

0'7.32"E, 30°25'32.61"N

63°57'57.16"E, 31°31'55.09"N

63°55'9.38"E

</th>  
<th>

~ 220

Sentinel2A/B L2A / 10 days

~ 220 GB / 10 days ( ~1 GB / product)

</th>  
<th>

\-

</th> </tr>  
<tr>  
<td>

Envisat ASAR dataset, observation period 2002-2010

</td>  
<td>

ETHZ-01-01 Global catalogue of co-seismic deformation

</td>  
<td>

N1

</td>  
<td>

EOLI

</td>  
<td>

Depending on processing level ESA might need to approve the pre-procesing of
image

</td>  
<td>

Coordinates of the Earthquake epicenters with magnitude higher than 5 (10 km
box around them)

</td>  
<td>

~ 4 ENVISAT

ASAR / event ~ 2.3 GB / days ( ~ 600 MB / product)

</td>  
<td>

\-

</td> </tr>  
<tr>  
<td>

SRTM-1 (30m)

</td>  
<td>

ETHZ-01-01 Global catalogue of co-seismic deformation

</td>  
<td>

Geotiff

</td>  
<td>

NASA LTA

</td>  
<td>

\-

</td>  
<td>

\-

</td> </tr>  
<tr>  
<td>

DLR systematic

</td>  
<td>

ETHZ-01-02 -

</td>  
<td>

SAFE

</td>  
<td>

DLR

</td>  
<td>

\-

</td>  
<td>

~ InSAR

</td>  
<td>

\-

</td> </tr>  
<tr>  
<td>

Sentinel-1 interferograms

</td>  
<td>

Exploitation of differential SAR

interferograms

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

Browse MedRes output / event ~ 200 MB / event ( ~ 33 MB / product)-

</td>  
<td>

</td> </tr>  
<tr>  
<td>

ShakeMap from USGS

</td>  
<td>

ETHZ-01-02 -

Exploitation of

differential SAR

interferograms

</td>  
<td>

Geotiff

</td>  
<td>

USGS

</td>  
<td>

\-

</td>  
<td>

\-

</td> </tr>  
<tr>  
<td>

Sentinel 1 product for generating interferograms

</td>  
<td>

ETHZ-01-03 Automated detection of changes due to earthquakes

</td>  
<td>

SAFE

</td>  
<td>

Sentinel Open Data Hub

</td>  
<td>

\-

</td>  
<td>

~ 4 Sentinel1

A/B / event ~ 22.8 GB / event ( ~5.7 GB / product)-

</td>  
<td>

\-

</td> </tr>  
<tr>  
<td>

USGS Earthquake catalogue

</td>  
<td>

ETHZ-01-0 Automated detection of changes due to earthquakes

</td>  
<td>

Geotiff

</td>  
<td>

USGS

</td>  
<td>

\-

</td>  
<td>

\-

</td> </tr> </table>  
<table>  
<tr>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

**Output datasets included in all first cycle challenges**

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

**Name**

</td>  
<td>

**Challenges involved**

</td>  
<td>

**Data format**

</td>  
<td>

**Preferential Data source**

</td>  
<td>

**Access restrictions**

</td>  
<td>

**Area of interest**

</td>  
<td>

**Expected data Volume**

</td>  
<td>

**Long-term data preservation**

</td> </tr>  
<tr>  
<td>

Sentinel-1 SLC image pair, repeat pass 6 or

</td>  
<td>

WFP-01-01 -

Hazards and

Change

</td>  
<td>

Geotiff

</td>  
<td>

From Data

Pipeline to be developed in

</td>  
<td>

\-

</td>  
<td>

AOI 1 NW - South Sudan

POLYGON((26.832 9.5136,

28.6843 9.5136, 28.6843

</td>  
<td>

Sentinel-1

Backscatter

Time Series ~

</td>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

12 days

</th>  
<th>

detection using Sentinel1 SAR data

</th>  
<th>

</th>  
<th>

BETTER

</th>  
<th>

</th>  
<th>

7.8009, 26.832 7.8009, 26.832

9.5136))

AOI 2 Renk - Blue Nile

POLYGON((32.0572 12.4549,

33.9087 12.4549, 33.9087

10.7344, 32.0572 10.7344,

32.0572 12.4549))

AOI 3 Niger Delta, Mali (updated after D2.2 delivered to EC) POLYGON((-5.5
17.26, -1.08

17.26, -1.08 13.5, -5.5 13.5, 5.17 17.26))

AOI 4 NE - Nigeria

POLYGON((12.9415 13.7579,

14.6731 13.7579, 14.6731

12.0093, 12.9415 12.0093,

12.9415 13.7579))

</th>  
<th>

210 GB /12 days (~5.27 GB / product) TOTAL SPACE FORESEEN:

~18.5 TB

Sentinel-1

Coherence Time

Series ~ 135 GB

/12 days (~3 GB

/ product)

TOTAL SPACE

FORESEEN ~12

TB

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Sentinel-2 L1C derived NDVI, NDWI, MNDWI,

NDBI Indices

</td>  
<td>

WFP-01-02 - Land cover changes and inter-annual vegetation performance

</td>  
<td>

Geotiff

</td>  
<td>

From Data

Pipeline to be developed in

BETTER

</td>  
<td>

\-

</td>  
<td>

AOI 1 Niger

POLYGON((6.4788 14.5973,

7.5577 14.5973, 7.5577

13.6328, 6.4788 13.6328,

6.4788 14.5973))

AOI 2 Tajikistan

POLYGON((67.7116 37.9032,

68.791 37.9032, 68.791

36.9211, 67.7116 36.9211,

67.7116 37.9032))

AOI 3 Mali

POLYGON((-10.3668 15.3471, -

9.3518 15.3471, -9.3518

14.3406, -10.3668 14.3406, -

</td>  
<td>

~ 44 GB /10 days (~600 MB / product)

TOTAL SPACE

FORESEEN ~4.7

TB

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Sentinel 2 derived

NDVI, NDWI,

MNDWI, NDBI smoothed indices

</td>  
<td>

WFP-01-02 - Land cover changes and inter-annual vegetation performance

</td>  
<td>

Geotiff

</td>  
<td>

From Data

Pipeline to be developed in

BETTER

</td>  
<td>

\-

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Landsat-8 L1C derived NDVI,

</td>  
<td>

WFP-01-02 - Land cover

</td>  
<td>

Geotiff

</td>  
<td>

From Data

Pipeline to be

</td>  
<td>

\-

</td>  
<td>

~ 55 GB / 16 days (~3.66 GB /

</td>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

NDWI, MNDWI,

NDBI Indices

</th>  
<th>

changes and inter-annual vegetation performance

</th>  
<th>

</th>  
<th>

developed in BETTER

</th>  
<th>

</th>  
<th>

10.3668 15.3471))

AOI 4 Afghanistan

POLYGON((67.6243 36.7228,

68.116 36.7228, 68.116

35.6923, 67.6243 35.6923,

67.6243 36.7228))

</th>  
<th>

product)

TOTAL SPACE

FORESEEN ~3.7

TB

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Landsat-8 derived

NDVI, NDWI,

MNDWI, NDBI smoothed indices

</th>  
<th>

WFP-01-02 - Land cover changes and inter-annual vegetation performance

</th>  
<th>

Geotiff

</th>  
<th>

From Data

Pipeline to be developed in

BETTER

</th>  
<th>

\-

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Copernicus (VGTProbaV) 1Km LAI time series smoothed and

gap-filled

</td>  
<td>

WFP-01-03 - EO indicators for global Early

Warning,

Seasonal Monitoring and

Climatology

Studies

</td>  
<td>

Geotiff

</td>  
<td>

From Data

Pipeline to be developed in

BETTER

</td>  
<td>

\-

</td>  
<td>

All WFP regions

</td>  
<td>

~268 MB / 1 month (~ 67 MB / product)

TOTAL SPACE

FORESEEN ~9.5

GB

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Copernicus (VGTProbaV) 1Km fAPAR time series

smoothed and

gap-filled

</td>  
<td>

WFP-01-03 - EO indicators for global Early

Warning,

Seasonal Monitoring and

Climatology

Studies

</td>  
<td>

Geotiff

</td>  
<td>

From Data

Pipeline to be developed in

BETTER

</td>  
<td>

\-

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Time series of

Copernicus (VGT-

</td>  
<td>

WFP-01-03 -

EO indicators

</td>  
<td>

Geotiff

</td>  
<td>

From Data

Pipeline to be

</td>  
<td>

\-

</td>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

ProbaV) LAI temporally aggregated

</th>  
<th>

for global Early

Warning,

Seasonal Monitoring and

Climatology

Studies

</th>  
<th>

</th>  
<th>

developed in BETTER

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Time series of

Copernicus (VGTProbaV) fAPAR temporally aggregated

</th>  
<th>

WFP-01-03 - EO indicators for global Early

Warning,

Seasonal Monitoring and

Climatology

Studies

</th>  
<th>

Geotiff

</th>  
<th>

From Data

Pipeline to be developed in

BETTER

</th>  
<th>

\-

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Long term averages of Copernicus (VGT-ProbaV) fAPAR (2 indicators * 2
aggregation functions (max and avg) * 9 aggregation time windows = 36 LTA

sets to be generated)

</th>  
<th>

WFP-01-03 - EO indicators for global Early

Warning,

Seasonal Monitoring and

Climatology

Studies

</th>  
<th>

Geotiff

</th>  
<th>

From Data

Pipeline to be developed in

BETTER

</th>  
<th>

\-

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Time series of

Copernicus (VGT-

</th>  
<th>

WFP-01-03 -

EO indicators

</th>  
<th>

Geotiff

</th>  
<th>

From Data

Pipeline to be

</th>  
<th>

\-

</th>  
<th>

</th> </tr> </table>  
<table>  
<tr>  
<th>

ProbaV) fAPAR anomalies at a

variety of temporal aggregations

</th>  
<th>

for global Early

Warning,

Seasonal Monitoring and

Climatology

Studies

</th>  
<th>

</th>  
<th>

developed in BETTER

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Long term averages of Copernicus (VGT-ProbaV) LAI (2 indicators * 2
aggregation functions (max and avg) * 9 aggregation time windows = 36 LTA

sets to be generated)

</th>  
<th>

WFP-01-03 - EO indicators for global Early

Warning,

Seasonal Monitoring and

Climatology

Studies

</th>  
<th>

Geotiff

</th>  
<th>

From Data

Pipeline to be developed in

BETTER

</th>  
<th>

\-

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Time series of

Copernicus (VGTProbaV) LAI anomalies at a

variety of temporal aggregations

</th>  
<th>

WFP-01-03 - EO indicators for global Early

Warning,

Seasonal Monitoring and

Climatology

Studies

</th>  
<th>

Geotiff

</th>  
<th>

From Data

Pipeline to be developed in

BETTER

</th>  
<th>

\-

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Time series of

MODIS MOD11C2

LST temporally

</th>  
<th>

WFP-01-03 - EO indicators for global Early

</th>  
<th>

Geotiff

</th>  
<th>

From Data

Pipeline to be developed in

</th>  
<th>

\-

</th>  
<th>

~ 408 MB / 1 month for 3 years

</th>  
<th>

</th> </tr> </table>  
<table>  
<tr>  
<th>

aggregated values

(N = 1 (no aggregation), 3, 6,

9, 12, 15, 18, 27,

36)

</th>  
<th>

Warning,

Seasonal Monitoring and

Climatology

Studies

</th>  
<th>

</th>  
<th>

BETTER

</th>  
<th>

</th>  
<th>

</th>  
<th>

WFP-01-03-01 aggregations (~ 34 MB/ product) ~ 9 MB / 1 month for 3 years

WFP-01-03-02 aggregations (~

1 MB / product)

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Long term averages of MODIS MOD11C2 LST (2 indicators * 2 aggregation
functions (max and avg) * 9 aggregation time windows = 36 LTA

sets to be generated)

</th>  
<th>

WFP-01-03 - EO indicators for global Early

Warning,

Seasonal Monitoring and

Climatology

Studies

</th>  
<th>

Geotiff

</th>  
<th>

From Data

Pipeline to be developed in

BETTER

</th>  
<th>

\-

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Time series of

MODIS MOD11C2

LST 2000-present anomalies

</th>  
<th>

WFP-01-03 - EO indicators for global Early

Warning,

Seasonal Monitoring and

Climatology

Studies

</th>  
<th>

Geotiff

</th>  
<th>

From Data

Pipeline to be developed in

BETTER

</th>  
<th>

\-

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Time series of aggregated CHIRPS RFE 1981present (N = 10,

</th>  
<th>

WFP-01-03 - EO indicators for global Early Warning,

</th>  
<th>

Geotiff

</th>  
<th>

From Data

Pipeline to be developed in BETTER

</th>  
<th>

\-

</th>  
<th>

~ 12 MB / 1 month (~2 MB / product)

TOTAL SPACE

</th>  
<th>

</th> </tr> </table>  
<table>  
<tr>  
<th>

30, 60, 90, 120,

150, 180, 270,

365 days)

</th>  
<th>

Seasonal Monitoring and

Climatology

Studies

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

FORESEEN ~432 MB

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Long term averages of CHIRPS RFE 1981present (2 indicators * 2 aggregation
functions (max and avg) * 9 aggregation time windows = 36 LTA

sets to be generated)

</th>  
<th>

WFP-01-03 - EO indicators for global Early

Warning,

Seasonal Monitoring and

Climatology

Studies

</th>  
<th>

Geotiff

</th>  
<th>

From Data

Pipeline to be developed in

BETTER

</th>  
<th>

\-

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Time series of anomalies of aggregated CHIRPS RFE 1981present and current
anomalies

</th>  
<th>

WFP-01-03 - EO indicators for global Early

Warning,

Seasonal Monitoring and

Climatology

Studies

</th>  
<th>

Geotiff

</th>  
<th>

From Data

Pipeline to be developed in

BETTER

</th>  
<th>

\-

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Sentinel-2 A/B

L1C NDVI and

NDWI indices

</td>  
<td>

SATCEN-0101 - Thematic

Indexes for

Land Use

Identification

</td>  
<td>

DIM

</td>  
<td>

From Data

Pipeline to be developed in

BETTER

</td>  
<td>

\-

</td>  
<td>

AOI 1 Columbia - Cauca-Narino

2°44'38.51"N 78°19'27.30"W, 2°

5'57.77"N 77°14'51.12"W,

0°55'38.70"N 78° 1'49.27"W,

1°34'56.24"N 79° 0'39.68"W

</td>  
<td>

~ 6.8 GB / 12 days (~400 MB / product)

TOTAL SPACE

FORESEEN ~620

</td>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

AOI 2 Albania border with

Greece

41° 0'57.81"N 20°44'47.45"E,

40°51'0.67"N 21°17'1.40"E,

39°34'1.65"N 20°22'47.57"E,

39°52'46.66"N 19°53'54.20"E

AOI 3 Afghanistan - Helmand - Nad-e-Ali

73°22'24.90"W

64°44'54.92"E,32° 0'17.86"N 65°

0'7.32"E, 30°25'32.61"N

63°57'57.16"E, 31°31'55.09"N

63°55'9.38"E

AOI 4 Serbia

45°56'55.10"N 18°42'39.05"E,

45°57'44.72"N 18°59'43.44"E,

45° 7'2.00"N 19°40'15.66"E,

44°48'47.50"N 18°55'57.11"E

</th>  
<th>

GB

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Co-registered stack of Sentinel2 A/B L1C

</th>  
<th>

SATCEN-0101 - Thematic

Indexes for

Land Use

Identification

</th>  
<th>

DIM

</th>  
<th>

From Data

Pipeline to be developed in

BETTER

</th>  
<th>

\-

</th>  
<th>

~3.7 TB / YEAR (~10 GB / product)

TOTAL SPACE

FORESEEN ~11.1

TB

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Multi-temporal stack of Sentinel1 A/B 1 SLC

StripMap

</td>  
<td>

SATCEN-0102 - Change Detection based on SAR single look complex (SLC) data

</td>  
<td>

DIM

</td>  
<td>

From Data

Pipeline to be developed in

BETTER

</td>  
<td>

\-

</td>  
<td>

AOI 1 - 15°18'50.37"N,

8°22'52.84"E, 15°28'49.28"N,

10° 2'15.33"E, 12°17'43.44"N,

10°38'54.40"E, 12° 9'54.62"N, 9°

2'22.33"E

AOI 2 - 23° 8'15.91"N,

12°55'57.41"E, 23°27'11.02"N,

14°28'12.27"E, 19° 6'4.65"N,

15°24'34.73"E, 18°52'42.78"N,

13°50'31.39"E

AOI 3 20° 6'22.43"N,

5°20'58.79"E, 20°13'57.72"N, 6°

3'52.23"E, 18°30'49.60"N,

</td>  
<td>

~9.3 TB / YEAR (~ 25 GB / product)

TOTAL SPACE

FORESEEN ~27.9

TB

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Multi-temporal stack of Coherence products derived from Multitemporal stack of

</td>  
<td>

SATCEN-0102 - Change Detection based on SAR single look complex (SLC)

</td>  
<td>

DIM

</td>  
<td>

From Data

Pipeline to be developed in

BETTER

</td>  
<td>

\-

</td>  
<td>

~3.7 TB / YEAR (~10 GB / product)

TOTAL SPACE

FORESEEN ~11.1

TB

</td>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

Sentinel-1 A/B 1 SLC StripMap

</th>  
<th>

data

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

6°25'48.99"E, 18°23'13.00"N,

5°42'55.45"E

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Sentinel-2 A/B L1C DVI Maps

</td>  
<td>

SATCEN-01-

03 - Illicit Crop

Monitoring with Optical data

</td>  
<td>

Geotiff

DIM

</td>  
<td>

From Data

Pipeline to be developed in

BETTER

</td>  
<td>

\-

</td>  
<td>

AOI 1 Columbia - Cauca-Narino

2°44'38.51"N 78°19'27.30"W, 2°

5'57.77"N 77°14'51.12"W,

0°55'38.70"N 78° 1'49.27"W,

1°34'56.24"N 79° 0'39.68"W

AOI 2 Albania border with

Greece

41° 0'57.81"N 20°44'47.45"E,

40°51'0.67"N 21°17'1.40"E,

39°34'1.65"N 20°22'47.57"E,

39°52'46.66"N 19°53'54.20"E

AOI 3 Afghanistan - Helmand - Nad-e-Ali

73°22'24.90"W

64°44'54.92"E,32° 0'17.86"N 65°

0'7.32"E, 30°25'32.61"N

63°57'57.16"E, 31°31'55.09"N

63°55'9.38"E

</td>  
<td>

~1 TB / 10 days (~5 GB / product) TOTAL SPACE

FORESEEN

~109.5 TB--

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Co-registered stack of Sentinel2 A/B L1C

</td>  
<td>

SATCEN-01-

03 - Illicit Crop

Monitoring with Optical data

</td>  
<td>

Geotiff

DIM

</td>  
<td>

From Data

Pipeline to be developed in

BETTER

</td>  
<td>

\-

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Color composites of co-registered stack of Sentinel2 A/B L1C

</td>  
<td>

SATCEN-01-

03 - Illicit Crop

Monitoring with Optical data

</td>  
<td>

Geotiff

DIM

</td>  
<td>

From Data

Pipeline to be developed in

BETTER

</td>  
<td>

\-

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Co-seismic deformation maps from Envisat ASAR dataset

</td>  
<td>

ETHZ-01-01 Global catalogue of co-seismic deformation

</td>  
<td>

Geotiff

</td>  
<td>

From Data

Pipeline to be developed in

BETTER

</td>  
<td>

\-

</td>  
<td>

Coordinates of the Earthquake epicenters with magnitude higher than 5 (10 km
box around them)

</td>  
<td>

~ 200 MB / event (~ 100 MB/ output set)

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Map of areas with SAR coherence decrease after earthquakes M>5 derived from
Envisat ASAR

</td>  
<td>

ETHZ-01-01 Global catalogue of co-seismic deformation

</td>  
<td>

Geotiff

</td>  
<td>

From Data

Pipeline to be developed in

BETTER

</td>  
<td>

\-

</td>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

dataset

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Composite map including areas with SAR coherence decrease larger than a
defined threshold & coseismic deformation larger than a defined threshold
after earthquakes with M>5 + USGS shakemap derived from Envisat ASAR dataset

</th>  
<th>

ETHZ-01-01 Global catalogue of co-seismic deformation

</th>  
<th>

Geotiff

</th>  
<th>

From Data

Pipeline to be developed in

BETTER

</th>  
<th>

\-

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Filtered DInSAR interferograms derived from DLR systematic Sentinel-1
interferograms

</th>  
<th>

ETHZ-01-02 -

Exploitation of

differential SAR

interferograms

</th>  
<th>

Geotiff

</th>  
<th>

From Data

Pipeline to be developed in

BETTER

</th>  
<th>

\-

</th>  
<th>

~ 200 MB / event (~ 33 MB / product)

TOTAL SPACE

FORESEEN ~ 57

GB

( current 292 events )

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Co-seismic deformation maps derived from Sentinel-1

</th>  
<th>

ETHZ-01-03 Automated detection of changes due to earthquakes

</th>  
<th>

Geotiff

</th>  
<th>

From Data

Pipeline to be developed in

BETTER

</th>  
<th>

\-

</th>  
<th>

~ 12 GB / event (~ 6 GB/ output set)

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Coherence changes map

(areas with SAR coherence decrease after earthquakes M>5) derived from
Sentinel-1

</td>  
<td>

ETHZ-01-03 Automated detection of changes due to earthquakes

</td>  
<td>

Geotiff

</td>  
<td>

From Data

Pipeline to be developed in

BETTER

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Composite map including areas with SAR coherence decrease larger than a
defined threshold & coseismic deformation larger than a defined threshold
after earthquakes with M>5 + USGS shakemap derived from Sentinel-1

</td>  
<td>

ETHZ-01-03 Automated detection of changes due to earthquakes

</td>  
<td>

Geotiff

</td>  
<td>

From Data

Pipeline to be developed in

BETTER

</td>  
<td>

\-

</td>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

**Input Datasets included in all second cycle challenges**

</th> </tr>  
<tr>  
<td>

**Name**

</td>  
<td>

**Challenges involved**

</td>  
<td>

**Data format**

</td>  
<td>

**Preferential Data source**

</td>  
<td>

**Access restrictions**

</td>  
<td>

**Area of interest**

</td>  
<td>

**Expected data**

**Volume**

</td>  
<td>

**Long-term data preservation**

</td> </tr>  
<tr>  
<td>

_LST MODIS_

_MOD11C2 - 5.6Km_ _resolution 8 day_ _data_

</td>  
<td>

WFP-02-01 - MODIS TERRA

Land Surface

Temperature

(LST) -

Aggregations and Anomalies

</td>  
<td>

GEOTIFF

</td>  
<td>

</td>  
<td>

\-

</td>  
<td>

Global

**For validation:**

Years 2015, 2016 and 2017 **For production:**

Full archive processing of the data

(2000 - present)

</td>  
<td>

\-

</td>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

MODIS Aqua/Terra

Snow Cover 8-Day L3 Global 500m Grid, Version 6.

</th>  
<th>

WFP-02-01 -

MODIS

TERRA/AQUA

Snow Cover - Aggregations and Anomalies

</th>  
<th>

GEOTIFF

</th>  
<th>

</th>  
<th>

\-

</th>  
<th>

Central Asia - POLYGON((25.0

48.0, 94.0 48.0, 94.0 23.1, 25.0 23.1, 25.0 48.0)) **For validation:**

Years 2015, 2016 and 2017 for winter season (August - July) **For
production:**

Full archive processing of the data

(2000 - present) for winter season

(August - July)

</th>  
<th>

\-

</th>  
<th>

</th> </tr> </table>  
<table>  
<tr>  
<th>

_Sentinel-2 A/B L1C_

</th>  
<th>

WFP-02-03 - Smooth & gap-

filled Sentinel-

2

</th>  
<th>

GeoTiff

</th>  
<th>

</th>  
<th>

\-

</th>  
<th>

**For validation:**

Gambella (S2 tiles: 36PXQ,

36PYQ, 36NXP, 36NYP) Marchfeld (S2 tiles: 33UXP) **For production:**

AOI defined by Analyst triggering the pipeline

</th>  
<th>

\-

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Product 1:

  * Two Radar images, Image 

  

1 and Image 2  Sensor: Sentinel-1 A/B

  * Processing level: L1 GRD 

  

  * Acquisition mode: 

  

Interferometric

Wide Mode

  * Pass: same pass 

  

for all the stack (e.g.

DESCENDING)

  * Orbit : same 

  

orbit throughout the stack

</td>  
<td>

SATCEN-02-01

\- Change

Detection and Characterizatio n 2 (SAR

Change

Detection with

GRD data)

</td>  
<td>

GEOTIFF

</td>  
<td>

</td>  
<td>

\-

</td>  
<td>

Peru's Madre de Dios region

UL 70.5659 W 12.4567 S

BR 69.1411 W 13.0922 S

**For validation**

Two images, covering 99 % of the area, (S1 IW Descending mode) above the area
on dates

2018-08-12 and 2018-09-05

Image 1

S1B_IW_GRDH_1SDV_20180812T

101414_

20180812T101439_012228_0168

7D_BCA9

Image 2

S1B_IW_GRDH_1SDV_20180905T

101415_

20180905T101440_012578_0173

58_3259 **For Production**

images from April 2019 to

October 2019

Frequency: when a new image, acquired in the same interferometric conditions,
is

</td>  
<td>

\-

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Product 2:

  * DEM Model 

  

  * DEM image 

  

</td>  
<td>

GEOTIFF

</td>  
<td>

</td>  
<td>

\-

</td>  
<td>

\-

</td>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

should be used for the Terrain

Correction (often automatically

downloaded by

specific software)

 Orbit file should be used for Orbital Correction (often automatically

downloaded by

specific software)

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

available

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Sensor: Sentinel-2

A/B

Processing Level: Level2A (level 1B

for the test in

2018)

Bands: All 12 spectral bands (in fact, only bands 2,4, 11 and 12 are required)
and the cloud mask

</td>  
<td>

SATCEN-02-02

\- Thematic

Indexes 2 (mineral indexes with Optical data

</td>  
<td>

GEOTIFF

</td>  
<td>

</td>  
<td>

\-

</td>  
<td>

Peru's Madre de Dios region

UL 70.5659 W 12.4567 S BR

69.1411 W 13.0922 S

</td>  
<td>

\-

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Sensor: Sentinel-2 A/B

</td>  
<td>

SATCEN-02-03 - Land

</td>  
<td>

GEOTIFF

</td>  
<td>

</td>  
<td>

</td>  
<td>

Peru's Madre de Dios region UL 70.5659 W 12.4567 S BR

</td>  
<td>

\-

</td>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

Processing Level: Level2A (level 1B

for the test in

2018)

Bands: All 12 spectral bands (in fact, only bands 11,8, 4 and 2 are required)
and the cloud mask

</th>  
<th>

Use/Land

Cover (Illegal deforestation

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

69.1411 W 13.0922

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

  * 1.Weekly report on active volcanoes  _https_ _://volcano.si.ed_

  

_u/reports_wee_ _kly.cfm#_ ;

  * 2\. Sentinel-1 imagery; 

  

  * 3\. SRTM-1 (30m 

  

</td>  
<td>

ETHZ-02-01 - Radar interferometry in active volcanic regions

</td>  
<td>

GEOTIFF

</td>  
<td>

</td>  
<td>

</td>  
<td>

Volcanoes with ongoing activity and with new activity (from

WVAR)

</td>  
<td>

\-

</td>  
<td>

</td> </tr>  
<tr>  
<td>

:S1 GRD high resolution imagery;

</td>  
<td>

ETHZ-02-02 - Large surface displacements measured with feature tracking

</td>  
<td>

GEOTIFF

</td>  
<td>

</td>  
<td>

</td>  
<td>

Galapagos Islands (Isabela and

Fernandina); Great Aletsch Area, Switzerland; Slumgullion landslide, Colorado.

</td>  
<td>

\-

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Wrapped or unwrapped interferogram

</td>  
<td>

ETHZ-02-03 - Systematic modeling of

</td>  
<td>

GEOTIFF

</td>  
<td>

</td>  
<td>

</td>  
<td>

All volcanoes systematically monitored by DLR with S1

</td>  
<td>

\-

</td>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

</th>  
<th>

surface deformation at

active volcanoes

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Sensor: Sentinel-1

A/B

  * Processing level: L1 GRD 

  

  * Acquisition mode: IW 

  

  * Pass: same pass 

  

for all the stack

(ASCENDING / DESCENDING)

  * Orbit : same 

  

orbit throughout the stack

</td>  
<td>

EXT-02-01 -

Change

Detection in Rural Localities for SemiAutomated

Border

Surveillance

Applied to Insecure Areas in Lake Chad

Basin

</td>  
<td>

GEOTIFF

</td>  
<td>

</td>  
<td>

</td>  
<td>

(ideally areas where there is already ground truth)

**For Validation**

AOI2 (border with Burkina Faso) - 5m

Xmin= 1.47, Xmax= 2.17¡

Ymin=,12.56 Ymax= 13.65

AOI1 (Diffa/Geidam Region - Lake

Chad) - 10m

Xmin=11, Xmax=15

Ymin=12.5, Ymax=15

**For production**

AO3 (border with Nigeria - region

of Zinder) - 5m

Xmin=8.39, Xmax=10.65

Ymin=12.17, Ymax= 15.49

Polygon corner coordinates

X1=8.94, Y1=12.64

X2=9.86, Y2=12.37

X3=10.54, Y3=12.73

X4=10.50, Y4=15.05

X5=10.06, Y5=15.43

X6=8.40, Y6=15.31

X7=8.94, Y7=12.64

AOI4 (Markoye/Teguey Region) - 10m

Xmin=-0.4, Xmax=1

</td>  
<td>

\-

</td>  
<td>

</td> </tr>  
<tr>  
<td>

DEM Model. DEM image should be used for the Terrain Correction (often
automatically downloaded by specific software). Orbit file should be used for
Orbital Correction (often automatically downloaded by specific software)

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

Ymin=13.5, Ymax=15.

AOI5 (Agadez Region) - 10 m

Xmin=4, Xmax=8.5

Ymin = 16.8, Ymax=19.45

Polygon corner coordinates

X1=0.76, Y1=11.74

X2=2.46, Y2=12.24

X3=1.99, Y3=14.40

X4=1.02, Y4=13.97

X5=0.76, Y5=11.74

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Sentinel 1 (A or B)

Mode: IW

(Interferometric

Wide Swath)

Product Level: GRD

Polarization: VV or

VV+VH

</td>  
<td>

EXT-02-02 - Satellite observations of oil sheen of natural oil seepage in

Disko Bay,

West

Greenland

</td>  
<td>

GEOTIFF

</td>  
<td>

Sentinel Open Data Hub

</td>  
<td>

</td>  
<td>

Location of known seepage in

Disko – Nuussuq – Svartenhuk Halvø region along the West coast of Greenland.
Map from Bojesen-Koefoed et al., 2007. Petroleum seepages at Asuk Disko, West
Greenland: Implications for regionals petroleum exploration. Journal of
Petroleum Geology 30, 219–236.

doi:10.1111/j.17475457.2007.00219.x

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Landsat-7 and -8 Level 2A

(atmospherically corrected, includes cloud and cloud shadow mask)

</td>  
<td>

EXT-02-03 -

Crop Loss Detection using NDVI anomalies

</td>  
<td>

GEOTIFF

</td>  
<td>

EarthExplorer

</td>  
<td>

</td>  
<td>

**For validation:** one of the events identified by

period for the parcels affected by

that event **For production:**

All events/parcel coordinates in

Portugal to be provided by

Fidelidade

</td>  
<td>

</td>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

L1T ASTER, L1T

LANDSAT8 (TIRS), L1 SLSTR Sentinel 3, L1C Sentinel2.

</th>  
<th>

EXT-02-04 - Surface temperature map evolution

</th>  
<th>

GEOTIFF

</th>  
<th>

EarthExplorer

</th>  
<th>

</th>  
<th>

Etna, Vesuvio, Campi Flegrei, Stromboli

</th>  
<th>

</th>  
<th>

</th> </tr> </table>  
<table>  
<tr>  
<th>

</th>  
<th>

</th>  
<th>

**Output Datasets included in all second cycle challenges**

</th> </tr>  
<tr>  
<td>

**Name**

</td>  
<td>

**Challenges involved**

</td>  
<td>

**Data format**

</td>  
<td>

</td>  
<td>

**Preferential Data source**

</td>  
<td>

**Access restrictions**

</td>  
<td>

**Area of interest**

</td>  
<td>

**Expected data**

**Volume**

</td>  
<td>

**long-term data preservation**

</td> </tr>  
<tr>  
<td>

Time series of

MODIS MOD11C2 LST 2000present gap-filled, smoothed and interpolated to 10 days

</td>  
<td>

WFP-02-01 - MODIS TERRA

Land Surface

Temperature

(LST) -

Aggregations and Anomalies

</td>  
<td>

Geotiff

</td>  
<td>

</td>  
<td>

\-

</td>  
<td>

\-

</td>  
<td>

\- Global

**For validation:**

Years 2015, 2016 and 2017 **For production:**

Full archive processing of the data

(2000 - present)

</td>  
<td>

\-

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Time series of LST temporally aggregated values

</td>  
<td>

Geotiff

</td>  
<td>

</td>  
<td>

\-

</td>  
<td>

\-

</td>  
<td>

\-

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Land Surface

Temperature Time Series products for a reference number of years

</td>  
<td>

Geotiff

</td>  
<td>

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Time series of LST

</td>  
<td>

Geotiff

</td>  
<td>

</td>  
<td>

\-

</td>  
<td>

\-

</td>  
<td>

\-

</td>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

anomalies

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Snow cover characterization time series start date = 8 cons. snow days finish
date= 8 cons.

no snow days

</td>  
<td>

WFP-02-01 -

MODIS

TERRA/AQUA

Snow Cover - Aggregations and Anomalies

</td>  
<td>

Geotiff

</td>  
<td>

\-

</td>  
<td>

\-

</td>  
<td>

Central Asia - POLYGON((25.0

48.0, 94.0 48.0, 94.0 23.1, 25.0 23.1, 25.0 48.0) **For validation:**

Years 2015, 2016 and 2017 for winter season (August - July) **For
production:**

Full archive processing of the data

(2000 - present) for winter season

(August - July)

</td>  
<td>

\-

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Long Term snow season characterization

</td>  
<td>

Geotiff

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Difference between annual snow cover characterization maps and the reference
snow cover climatology

</td>  
<td>

Geotiff

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Time series of temporally smoothed and gap-

filled Sentinel-2

NDVIs

</td>  
<td>

WFP-02-03 - Smooth & gap-

filled Sentinel-

2

</td>  
<td>

Geotiff

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

**For validation:**

Gambella (S2 tiles: 36PXQ,

36PYQ, 36NXP, 36NYP) Marchfeld (S2 tiles: 33UXP) **For production:**

AOI defined by Analyst triggering the pipeline

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

time series of temporally smoothed and gap-

filled Sentinel-2 reflectances.

</td>  
<td>

Geotiff

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Raster with three bands: the two intensities (after

</td>  
<td>

SATCEN-02-01

\- Change

Detection and

</td>  
<td>

Geotiff

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

Peru's Madre de Dios region

UL 70.5659 W 12.4567 S

BR 69.1411 W 13.0922 S

</td>  
<td>

</td>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

the pre-processing) and the map of changes.

</th>  
<th>

Characterizatio n 2 (SAR

Change

Detection with

GRD data)

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

**For validation**

Two images, covering 99 % of the area, (S1 IW Descending mode) above the area
on dates

2018-08-12 and 2018-09-05

Image 1

S1B_IW_GRDH_1SDV_20180812T

101414_

20180812T101439_012228_0168

7D_BCA9

Image 2

S1B_IW_GRDH_1SDV_20180905T

101415_

20180905T101440_012578_0173

58_3259

**For Production**

images from April 2019 to

October 2019

Frequency: when a new image, acquired in the same interferometric conditions,
is available

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

RGB time series of the indexes above and cloud mask

</td>  
<td>

SATCEN-02-02

\- Thematic

Indexes 2 (mineral indexes with Optical data

</td>  
<td>

GEOTIFF

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

Peru's Madre de Dios region

UL 70.5659 W 12.4567 S BR

69.1411 W 13.0922 S

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

NDVI, Bare Soil Index, vegetation

</td>  
<td>

SATCEN-02-03 - Land

</td>  
<td>

GEOTIFF

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

Peru's Madre de Dios region UL 70.5659 W 12.4567 S BR

</td>  
<td>

</td>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

mask and cloud mask

</th>  
<th>

Use/Land

Cover (Illegal deforestation

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

69.1411 W 13.0922

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Surface displacements

</td>  
<td>

ETHZ-02-01 - Radar interferometry in active volcanic regions

</td>  
<td>

GEOTIFF

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

Volcanoes with ongoing activity

and with new activity (from

WVAR)

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Surface displacements

</td>  
<td>

ETHZ-02-02 - Large surface displacements measured with feature tracking

</td>  
<td>

GEOTIFF

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

Galapagos Islands (Isabela and

Fernandina); Great Aletsch Area, Switzerland; Slumgullion landslide, Colorado.

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Surface displacement

(synthetic) in LOS

</td>  
<td>

ETHZ-02-03 - Systematic modeling of surface deformation at

active volcanoes

</td>  
<td>

GEOTIFF

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

All volcanoes systematically monitored by DLR with S1

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Difference between

surface displacement (synthetic) and measured (InSAR

)in LOS

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Change Detection

Map at 5m (Vector with polygons covering the changes between two

</td>  
<td>

</td>  
<td>

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

(ideally areas where there is already ground truth)

**For Validation**

AOI2 (border with Burkina Faso) - 5m

Xmin= 1.47, Xmax= 2.17¡

</td>  
<td>

</td>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

periods and one attribute: No change = 0;

Change =1)

</th>  
<th>

EXT-02-01 -

Change

Detection in Rural Localities for SemiAutomated

Border

Surveillance

Applied to Insecure Areas in Lake Chad

Basin

</th>  
<th>

SHP

</th>  
<th>

</th>  
<th>

</th>  
<th>

Ymin=,12.56 Ymax= 13.65

AOI1 (Diffa/Geidam Region - Lake

Chad) - 10m

Xmin=11, Xmax=15

Ymin=12.5, Ymax=15

**For production**

AO3 (border with Nigeria - region

of Zinder) - 5m

Xmin=8.39, Xmax=10.65

Ymin=12.17, Ymax= 15.49

Polygon corner coordinates

X1=8.94, Y1=12.64

X2=9.86, Y2=12.37

X3=10.54, Y3=12.73

X4=10.50, Y4=15.05

X5=10.06, Y5=15.43

X6=8.40, Y6=15.31

X7=8.94, Y7=12.64

AOI4 (Markoye/Teguey Region) - 10m

Xmin=-0.4, Xmax=1 Ymin=13.5, Ymax=15.

AOI5 (Agadez Region) - 10 m

Xmin=4, Xmax=8.5

Ymin = 16.8, Ymax=19.45

Polygon corner coordinates

X1=0.76, Y1=11.74

X2=2.46, Y2=12.24

X3=1.99, Y3=14.40

X4=1.02, Y4=13.97

X5=0.76, Y5=11.74

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Change Detection

Map at 10 m (Vector with polygons covering the changes between two periods and
one attribute: No change = 0;

Change =1)

</th>  
<th>

\-

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr> </table>  
<table>  
<tr>  
<th>

Oil sheen location map

(0-1 data with

locations of oil sheen)

</th>  
<th>

EXT-02-02 - Satellite observations of oil sheen of natural oil seepage in

Disko Bay,

West

Greenland

</th>  
<th>

GEOTIFF

</th>  
<th>

\-

</th>  
<th>

</th>  
<th>

Location of known seepage in

Disko – Nuussuq – Svartenhuk Halvø region along the West coast of Greenland.
Map from Bojesen-Koefoed et al., 2007. Petroleum seepages at Asuk, Disko, West
Greenland: Implications for regionals petroleum exploration. Journal of
Petroleum Geology 30, 219–236. doi:10.1111/j.17475457.2007.00219.x

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

NDVI values

</td>  
<td>

EXT-02-03 -

Crop Loss Detection using NDVI anomalies

</td>  
<td>

GEOTIFF

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

**For validation:** one of the events identified by

Fidelidade

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

NDVI statistics values for entire growing season for each year

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

NDVI statistics values for entire growing season for each year

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

NDVI statistics values for entire growing season for each year

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

NDVI statistics values for entire growing season for each year

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Long term averages

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

of NDVI statistics values for entire growing season for each year

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

the reference time period for the parcels affected by that event **For
production** :

All events/parcel coordinates in

Portugal to be provided by

Fidelidade

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<th>

NDVI statistics values for entire growing season for each year

</th>  
<th>

\-

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Long term averages of NDVI statistics values for entire growing season for
each year

</th>  
<th>

\-

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<th>

NDVI statistics values for entire growing season for each year

</th>  
<th>

\-

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Long term averages of NDVI statistics values for entire growing season for
each year

</th>  
<th>

\-

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<th>

NDVI statistics values for entire growing season for each year

</th>  
<th>

\-

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<th>

Long term averages of NDVI statistics values for entire growing season for

</th>  
<th>

\-

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr> </table>  
<table>  
<tr>  
<th>

each year

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<th>

NDVI statistics values for entire growing season for each year

</th>  
<th>

\-

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Delineation map

</td>  
<td>

EXT-02-04 - Surface temperature map evolution

</td>  
<td>

GEOTIFF

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

Etna, Vesuvio, Campi Flegrei, Stromboli

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Temperature map

(in °C)

</td>  
<td>

\-

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr> </table>

  * source-err: a series for tracking the systematic processing where the identifiers not successfully processed are inserted 

  

  * results: a series for the thematic data where the generated products metadata and enclosures are stored 

  

An entry in the tracking index (and associated series) is an OWS Context
document that contains at least:

  * An identifier (the identifier of the input product) 

  

  * A title 

  

  * A date/time coverage 

  

  * A spatial coverage 

  

  * A via link pointing to the input resource (required for recovery scenarios) 

  

  * A published date (the date of the source-in stage) 

  

  * A category expressing the processing stage (source-queue, source-in, sourceout, source-err) 

  

2.2

Catalogue and Metadata of the BETTER data pipelines

The systematic processing of

a BETTER data pipeline is supported by a tracking catalogue index

and its associated tracking series.

A BETTER pipeline has an index in the catalog.

This concept applied to the Word Food Programme data processor for the WFP

\-

01

\-

01

\-

01

Sentinel

\-

1

backscatter

timeseries gives us the index:

_https://catalog.terradue.com/bette_

_r_

_-_

_wf_

_p_

_-_

_0000_

_1_

This index has five series, accessible at the URL

_htt_

_p_

_s://catalog.terradue.com/bette_

_r_

_-_

_wf_

_p_

_-_

_0000_

_1_

/

series/search

:



source

\-

queue: a series for tracking the identifiers in the queue



source

\-

in: a series for tracking the systematic processing where the identifiers
being

processed are inserted



source

\-

out: a series for

tracking the systematic processing where the identifiers

successfully processed are inserted

  * The generator as version of the processing 

  

  * The output opensearch offerings limited by the geo:uid element 

  

  * The processing offering 

  

  * The URLs for the WPS GetCapabilities and Describe Process GET requests 

  

  * The URL for the WPS GetStatus GET request 

  

  * The URL for the WPS Execute POST request 

  

  * The WPS POST request 

  

The model can be extended with elements from the OGC EO Metadata profile of
O&M (a.k.a EOP) [EOP_OM] or the ISO-19115 metadata model.

This project has received funding from the European Union’s Horizon 2020
Research and Innovation Programme under grant agreement no 776280

#  FAIR data

##  Making data findable, including provisions for metadata

As introduced in the section above, the catalogue containing the BETTER data
pipeline uses the OGC® OpenSearch Geo and Time extensions standard as a
possible metadata model to provide discovery functions to all the elements of
a data pipeline. The baseline of the standard is the OpenSearch v1.1
specification (  _A9.com_ ,  Inc, n.d.) [OS]. The Geo and Time extensions
provide with the main queryables to manage geospatial data products, and the
specification is standardised at the Open Geospatial Consortium (OGC, n.d.).

The data generated by the BETTER data processing pipelines have an entry in a
data catalogue that includes metadata with at least the geo and time
attributes. This minimum set of metadata ensures this data can be discovered
using geographical and time search criteria.

The discoverability of data generated by the BETTER data processing pipelines
is guaranteed with the data catalogue OpenSearch search engine that exposes an
OpenSearch Description document (OSDD) describing the search template and
returns several search output formats such as Atom or JSON. The response
includes the enclosure to the data file (or files).

The OSDD will have a DOI assigned using the ZENODO platform, which is
associated to results of a data pipeline. The process requires to upload the
OSDD XML document to Zenodo and add all the content needed for the **dataset
landing page.** The DOI will thus resolve to item in Zenodo that contains the
OSSD XML document and the description of the dataset.

The landing page will include the following:

  * Info necessary to create citations in various different citation standards ( I.e title, authors, data created etc) 

  

  * provisions for machine readability 

  

  * Long term preservation - if the data is no longer available, the landing page should remain to explain why 

  

  * Links to the data - where possible, these should be links to the data itself, but it may also be contact information to coordinate having the data sent on physical media for extremely large datasets. 

  

The landing page will be hosted by Zenodo.

According to the selected standard baseline and extensions, the data catalogue
queryable elements are discovered by requesting the OpenSearch description
document containing the URL templates and response formats.

For each of the associated data pipeline series, the OpenSearch description
document can be retrieved with the URL:

_https://catalog.terradue.com/ < _ d  ata pipeline
identifier>/series/<stage>/description

Where

  * <data pipeline identifier> is the data pipeline unique identifier that can be traced in the BETTER deliverable D3.1. Example better-wfp-00001 for the WFP-01-01-01 Sentinel-1 backscatter timeseries data pipeline.  <stage> is one of: o source-queue: to search the messages in the data pipeline queue o source-in: to search the messages of the data pipeline being processed o source-out: to search the messages of the data pipeline successfully processed o source-err: to search the messages of the data pipeline not successfully processed o results: to search for the products generated by the data pipeline 

  

The policy for the discovery functions is open.

The OpenSearch description document obtaining from the previous URL contains
the list of parameters to do a query against the metadata, such as:

  * start: start of the temporal interval (RFC-3339) 

  

  * stop: stop of the temporal interval (RFC-3339) 

  

  * bbox: Rectangular bounding box 

  

##  Making data openly accessible

The Terradue Ellip Cloud Platform is the privileged storage and cataloguing
resource for managing the dataset produced by the BETTER data pipelines and
described above in the section of Data summary. In principle, all these
datasets would be make open accessible if not access or size restrictions are
declared. As mandatory, the datasets used in publications will be open
accessible and available at openAIRE.

As declared in the previous section, the discovery of a given BETTER data
pipeline is open and accessible via the OpenSearch mechanism (available on the
Web). The data access requires software tools allowing to do GET requests to
the data enclosure pointing to the storage where the data is hosted. The data
access can be done with the same set of tools as for the data discovery.

The metadata associated to the the data generated by the BETTER data
processing pipelines is deposited on the data catalog accessible at the URL
_https://catalog.terradue.com._ One must know the BETTER data processing
pipeline catalog index to access the OSDD URL.

The data generated by the BETTER data processing pipelines is hosted on the
data storage accessible at the URL  _https://store.terradue.com_ .  The
metadata associated to the the data generated by the BETTER data processing
pipelines contains the enclosure.

The access to a given BETTER data pipeline results may:

  * Be subject to an authentication/authorization process according to an agreed policy with the BETTER Challenge Promoter (e.g. embargo period before full public release). 

  

  * Be open for download without any authentication or authorization 

  

Even when the results are subject to an authentication process, there will be
an registration process for getting a user credentials freely. If any access
policy is applied to a data set, it is described in the column 'access
restrictions' in the table of the section 2.1.

##  Making data interoperable

As introduced above, the results produced by the BETTER data pipelines are
discoverable using OpenSearch [OS]. The response of an opensearch query is a
list of records containing an OWS Context document [OWS_C]. The contents of
the OWS Context document are described in the section 2.2 and they can be
extended with elements from the OGC EO Metadata profile of O&M [EOP_OM].

As can be found in the column Format in the table of the section 2.1, the
results themselves are formatted in general in well known formats: geotiff,
netcdf, DIM or SAFE. Most of these formats are supported by standard tools.

Furthermore, the outputs of a data pipeline can be used as an input to another
data pipeline.

##  Increase data re-use (through clarifying licences)

The data is available and catalogued as soon as the data pipeline generated
it.

The owner of the data will be the BETTER Challenge Promoter of the
corresponding challenge. They are responsible for setting the right policies
of use of the generated data. If a data usage license is applied to any data
it should be compatible with the FAIR data principles. The BETTER Promoters
have not defined any license for the re-use of the data generated at the
moment. If they are identified in the future, they will be reported in the
column 'access restrictions in the updates of the table in section 2.1.

The data retention policy of the BETTER data pipelines results is to agree and
define with the BETTER Challenge Promoter a period of time (retention time) in
which this data is stored and replicated on the Ellip platform. After that
period, the results are eliminated in a FIFO approach. At the moment, we have
not defined yet measures for the long term preservation of the data generated
in the project such as publishing the data in other platforms or repositories.
The data in the Ellip platform will not persist even during the duration of
the project. The long term preservation of the data will be addressed in the
updates of the table in section 2.1.

#  Other issues

##  Allocation of resources

The cost for making data FAIR is covered by the tasks in the WP3 of the
project. The Ellip Platform, provided in Task 3.1, supports the catalogue
opensearch for the data pipelines. The data ingestion modules of the data
pipelines are developed in the task 3.3, they will format the input and output
data adding the associated metadata for their cataloguing. Some effort is
allocated to the Challenge Promoters to define the access and use policy of
the data generated by the data pipelines.

The resources for the long term preservation have not been discussed yet, they
will be addressed in the next version of this document.

##  Data security

The Ellip platform ensures the safety of the data during the configured
retention time. The data is protected at different levels of the platform
storage system:

  * At the service level, the storage is a “distributed-replicated” type meaning that “distributed-replicated” volumes distribute files across replicated servers for the same volume. 

  

When replicating, servers are organised in pairs. Therefore, it’s possible to
lose up to one entire server for each pair of servers constituting the storage
service.

  * At the server level, the storage is configured with RAID 6 using a hardware-RAID Controller and 2 HotSpace disks. 

  

This configuration ensures double-parity RAID and thus resilient up to 4 disks
failures within the RAID set before any data is lost.

For the long term preservation of the data, as said in previous sections, the
measurements to take with the data are still to be defined.

**END OF DOCUMENT**