1 N EW TECHNICAL SPECIFICATIONS
The technical specifications below have been derived from the requirements expressed by the users, the technical feasibility with satellite remote sensing data and priorities expressed by the EEEA.
Our proposal is to join the production of the former HRL on permanent water and wetlands into one layer and production process using “wetness” as concept. The HRL shall encompass two products:
a primary layer (Water & Wetness Presence Index - WWPI)
a secondary layer with defined classes 1 .
1.1
N
The “Water & Wetness Presence Index” indicates the occurrence of water and / or wet areas (with high soil moisture) during a prolonged part of the year over a number of years. The index (i.e. primary layer) is derived from a stack of images in which each pixel in a given layer is classified as “dry”, “wet” or “(open) water”.
The degree of wetness is assessed independent of the actual vegetation cover and thus not limited to a specific land cover class 2 .
The layer is conceived as a baseline for the identification of a secondary layer with information on:
 permanent water (e.g. rivers, lakes)
 temporary water (e.g. inundated areas)
 permanently wet areas (e.g. areas permanently high soil moisture)
 temporary wet areas (e.g. area of changing soil moisture)
 dry areas.
The layer does not map “wetlands” according to their ecological concept, but areas of elevated soil moisture or water over a period of 6 years. This information can subsequently be used to derive probability / potential maps for “real wetlands”.
The layer is derived from a time series analysis of optical satellite data from the Landsat archive (using data between 2009 and 2015), IMAGE2009, IMAGE2012 and IMAGE2015.
Wherever available, the optical data analysis shall be supported by Radar data with a geometric resolution of < 30m (e.g. Sentinel-1).
IMAGE2015 is used as geometric reference for any other satellite data.
Table 1: Secondary layer of the Water & Wetness Presence Index (WWPI)
Water / wetness layer
0: No water / no wet area
Explanation Examples always dry
1 Due to its specifities, water is discussed in detail the report of the HRL water specifications.
2 The applicability of SAR imagery for further differentiation according to vegetation type is currently being investigated in parallel Task 6. Based on outcomes of that task, more secondary layers than proposed now might be realistic to produce.

1: Permanent water
Water surface with floating vegetation included
2: Temporary water always
water alteration of
dry and
water or alteration of
wet and
water
• Permanent inland lakes (natural)
• Artificial ponds (permanent fish ponds, reservoir)
• Liquid dump sites (permanent)
• Rivers
• Channels (permanently with water)
• Coastal water surfaces: lagoons, estuaries
• Temporary water surfaces associated to permanent water bodies
• Temporary natural (eg. steppe) lakes and temporary artificial lakes (e.g. cassettes of fishponds)
• Intermittent rivers
• Flood areas
• Water-logged areas
• Wet agricultural fields, including rice fields
• Intertidal area always wet • Reeds
• Peat land
• Inland and coastal wetlands alteration of
dry and wet
• Inland saline marshes
• Intermittent wetlands
3: Permanently wet areas
(wetness)
4: Temporary wet area
(wetness)
1.2
I
It includes:
 Open water bodies (permanent lakes, reservoirs, ponds, rivers) including floating vegetation
 Temporary open water bodies (intermittent rivers, changing lake/reservoir levels)
 Areas with a permanently high degree of soil moisture 3 (no open water)
 Areas with temporarily high degree of soil moisture
 Temporarily inundated areas (due to snow melt, floods or rain)
 Wet agricultural fields, including rice fields
 Wet grasslands and pastures
 Coastal water bodies (lagoons)
It excludes:
 Sea and ocean (sea water beyond a boundary provided by the EEA)
 Permanent snow and glaciers
3
See also Table 2

1.3
T
HRL Wetness Probability 2015 Specification
Source: ESA WOIS
Description:
Index for the occurrence of water and wet areas based on the analysis of a 6-year time series
(baseline information). The index represents the occurrence of water bodies and high soil moisture in a given pixel. The number of images of dry, wet and water is combined to build the
Wetness Presence Index (WPI), with values from 0 (=dry pixels) to 100 (permanent water bodies).
This layer will provide a baseline mapping of different types of wet lands and serve as reference for change detection. The change detection not only allows identifying the increase or decrease in wet areas, but also to discriminate the changes in wetness occurrence in a given area. The drying up of land as well as the increase in flooding or water logging can be detected.
Input data sources (EO & ancillary):
 IMAGE2009 high resolution imagery (COV-1 and COV-2)
 IMAGE2012 high resolution imagery (COV-1 and COV-2)
 IMAGE2015 o HR_IMAGE_2015 is defined as 2 high-resolution coverages of EEA-39 within vegetation season 2014-2015 based on missions Spot-5 and Resourcesat-2
 Sentinel-2 (10-20m)
 Sentinel-1 (20m)
 Landsat archive (last 6 years, i.e. 2009 to 2015) (minimum 3 images / year: spring,
 summer, fall)
EU-DEM (to derive HR slope mask, which will be used to mask out improbable areas)
 Coastal water mask (to delineate between marine and inland waters)

Thematic Content:
 Layer stack of satellite images over 6-year time period
 Information content of each pixel in the layer stack o Total number of images with ”dry” surface o Total number of images with “wet” surface (i.e. soil moisture content [ θ ] > 0.25) o Total number of images with ”water” surface o Frequency of changes between “dry” & “water” during the observation period o Water &Wetness Presence Index (WWPI)
Methodology:
 Creation of a layer stack from IMAGE2009, IMAGE2012 and IMAGE2015 high resolution data as well as Sentinel-2 data (when available).
 Integration of images from the Landsat archive (to limit the processing effort, the number of Landsat images per year is restricted to 3 images, optimally from spring, summer and fall, with at least 6 weeks’ time between individual acquisition dates) into the layer stack.
 Add Sentinel-1 time series to the stack and determine pixels with elevated soil moisture
(i.e. soil moisture content [ θ ] > 0.25).
 Use of IMAGE2015 as geometric reference.
 Determine for each pixel in each layer whether it is “dry”, “wet” or “water”. A pixel is classified as “water” only when open water is visible in the image. Any other degree of high soil moisture (e.g. wet pastures) is classified as “wet”. Pixels with no particular elevated soil moisture or visible water are classified as “dry”. The degree of wetness is
 assessed independent of the actual vegetation cover.
Record the total number of “dry”, “wet” and “water” pixels in the water & wetness presence layer.
 Calculate the Water & Wetness Presence Index (WWPI) from the number of wet / water pixels vs. the total number of images in the stack o In the calculation water pixels receive a weight of 1 o Wet pixels are weighted with a factor of 0.75
WWPI =
∑𝑤𝑎𝑡𝑒𝑟+(0.75∗ ∑𝑤𝑒𝑡)
∑𝑤𝑎𝑡𝑒𝑟+ ∑𝑤𝑒𝑡+ ∑𝑑𝑟𝑦

Derivation of the secondary layer (see also
)
 Record the frequency of changes between “dry” and “water” pixels in order to support the assessment of major changes in permanent water surfaces (e.g. new reservoir creation).
Geometric resolution (Scale)
Pixel resolution 20m x 20m, MMU – 1 pixel
Geographic projection / Reference system
European Terrestrial Reference System 1989 (ETRS89)
Coverage
EEA 32 Member Countries and 7 Cooperating Countries, i.e. the full EEA39 (Albania, Austria,
Belgium, Bosnia- Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia,
Finland, France , Germany, Greece, Hungary, Iceland, Ireland, Italy, Kosovo (under the UN

Security Council Resolution 1244/99), Latvia, Liechtenstein, Lithuania, Luxembourg, Former
Yugoslavian Republic of Macedonia, Malta, Montenegro, the Netherlands, Norway , Poland,
Portugal , Romania, Serbia, Slovakia, Slovenia, Spain , Sweden, Switzerland, Turkey, United
Kingdom) who are participating in the regular CLC dataflow. The number of countries involved is 39. The area covered is, approximately, 6 Million km 2 .
Geometric accuracy (positioning accuracy)
Less than half a pixel. According to ortho-rectified satellite image base delivered by ESA
(IMAGE2015).
Spatial resolution / Minimum Mapping Unit (MMU):
20 m / 1 ha
Min. Width of linear features:
20 m
Topological correctness:
No void cells in raster
Raster coding
WWPI: Water & Wetness Presence Index (0 – 100),
254: unclassifiable (no satellite image available, or clouds, shadows, or snow),
255: outside the production unit
DRY: Total number of images in stack classified as “dry”
WET: Total number of images in stack classified as “wet”
WATER: Total number of images in stack classified as “water”
FRQ: Frequency of changes between “dry” & “water” during the observation period
Thematic accuracy (in %) / quality method
Quantitative (stratified random points sample compared to external datasets)
The thematic accuracy will be determined for the secondary products:
 Permanent water:
 o target accuracy 85%
Temporary water: o target accuracy 80%
 Permanent wet areas:
 o target accuracy 80%
Temporary wet areas: o target accuracy 80%
 Dry areas: o target accuracy 85%
Target year
2015 (including a baseline from 2009)
Up-date frequency
Every 3 years

Information actuality
Plus and minus 1 year from target year (2014 – 2016)
Delivery format
TIFF for raster (pixel-based) products
Data type
Raster
Naming conventions
[E.g. ‘service_provider’_’date’_’document_name’]
Medium
[E.g. Digital carrier (DVD, CDRom), online (ftp-server)]
Delivery reliability
[E.g. may leave that to EEA]
Delivery time
[E.g. may leave that to EEA]
Archive
[E.g. All raw and processed data and products archived. Data and meta data burnt to DVD and placed on ftp-server.]
There are four standard water contents that are routinely measured and used, which are described in the following table. The threshold for the identification of wet soils considers an average value for field capacity and saturated water content.
Table 2: Soil water content classes
Source: https://en.wikipedia.org/wiki/Water_content
1.4
H
Summary of production steps
1.
Creation of a layer stack
Integration of all HR optical and SAR images from 2009 to 2015 into one layer stack with
20m geometric resolution (IMAGE2015 geometry and precision) in European projection.
2.
Determination of the wetness / water content

Determination of the wetness / water status (“dry”, “wet”, “water”) for each pixel in each individual layer. For SAR images an initial soil water content [θ] > 0.25 is suggested to identify “wet” soils. This threshold might need to be adapted to regional situations and soil types.
Production of a support information that is used for the calculation of the final WWPI and the secondary layers. Summary of the total number of “dry”, “wet”, “water” instances for each location in the layer stack.
 DRY

- Total number of images in stack classified as “dry”
WET - Total number of images in stack classified as “wet”
 WATER - Total number of images in stack classified as “water” (to be used to derive a secondary layer on permanent and temporary water surfaces)
 FRQ - Frequency of changes between “dry” & “water” during the observation period (used to determine appearance or disappearance of permanent water bodies)
 TOTAL - Total number of valid images (sum of “dry”, “wet” and “water”)
4.
Calculation of the WWPI
Calculation of the WWPI from the number of WET / WATER pixels vs. the TOTAL number of images in the stack:
WWPI =
∑𝑤𝑎𝑡𝑒𝑟+(0.75∗ ∑𝑤𝑒𝑡)
∑𝑤𝑎𝑡𝑒𝑟+ ∑𝑤𝑒𝑡+ ∑𝑑𝑟𝑦
To account for the difference of open water and wet soils, the “wet” instances are weighted with an initial factor of 0.75. This threshold needs to be confirmed.
5.
Calculation of secondary layers
The information from the WWPI HRL is used to derive a secondary layer with information on:
 Permanent water (1) The highest ratio of the WATER / TOTAL instances (>
85%) will be classified as permanent water surfaces.
 Temporary water (2) Temporary water surfaces will have a ratio between
25% to 85% (WATER / TOTAL instances ) with varying
degrees of wetness (see also Table 1 for specific
alterations)
 Permanently wet areas
(3)
Areas with > 75% WET / TOTAL instances
 Temporary wet areas
(4)
Areas with 25% to 75% WET / TOTAL instances) and minor instances of WATER
 No water / no wet areas
(0)
Always DRY or mostly dry with minor instances of WET or WATER

6.
Automated clean-up of status layers
Classification errors will be automatically removed by applying filters based on the HR support layers and ancillary data (EU-DEM slope layer, coastal mask).
7.
Aggregation from 20m to 100m cell size
The 100m x 100m reference layers for WWPI, support layer and secondary layer is to be produced by the aggregation of 20m x 20m HRL in European projection.
Special care is taken on 254 and 255 codes using the following decision tree:
 If the majority (>=13) of 20m pixels within a 100m cell has a valid (other than 254
/ 255) value, o then the aggregated data set will be calculated as number of target class pixels / number of all valid pixels
 If the majority of 20m pixels within a 100m cell has a 225 or 254 value, the following decision will be taken: o Out of area (255) pixels >= unclassifiable (254) pixels?
 Yes: assign "no data (255)" to the cell
 No: assign "unclassifiable (254)" to the cell o In case of equality, 255 would be preferred.
The resulting 100m resolution grid shall be conform to the 100m resolution EEA Reference
Grid.

User feedback
Please review the proposal above and provide us some feedback paying special attention to:
 Applicability of the concept at your scale of work (regional, national, European)
 Added value of the proposed information
 Feasibility of the approach