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Copernicus Global Land Operations – Lot 2 Date Issued: 19.05.2017 Issue: I1.02
Copernicus Global Land Operations
“Cryosphere and Water” ”CGLOPS-2”
Framework Service Contract N° 199496 (JRC)
PRODUCT USER MANUAL
LAKE ICE EXTENT (LIE)
COLLECTION 250M BALTIC SEA REGION
VERSION 1.0.1
Issue I1.02
Organization name of lead contractor for this deliverable: SYKE
Book Captain: Kirsikka Heinilä (SYKE)
Contributing Authors: Sari Metsämäki (SYKE)
Olli-Pekka Mattila (SYKE)
Copernicus Global Land Operations – Lot 2 Date Issued: 19.05.2017 Issue: I1.02
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Dissemination Level PU Public X
PP Restricted to other programme participants (including the Commission Services)
RE Restricted to a group specified by the consortium (including the Commission Services)
CO Confidential, only for members of the consortium (including the Commission Services)
Copernicus Global Land Operations – Lot 2 Date Issued: 19.05.2017 Issue: I1.02
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Document Release Sheet
Book captain: Kirsikka Heinilä Sign Date
Approval: Roselyne Lacaze Sign Date
Endorsement: Mark Dowell Sign Date
Distribution: Public
Copernicus Global Land Operations – Lot 2 Date Issued: 19.05.2017 Issue: I1.02
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Change Record
Issue/Rev Date Page(s) Description of Change Release
3.3.2017 All First issue I1.01
19.5.2017 All Update after external review l1.02
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TABLE OF CONTENTS
1 Background of the document ............................................................................................... 8
1.1 Executive Summary ................................................................................................................. 8
1.2 Scope and Objectives............................................................................................................... 8
1.3 Content of the document......................................................................................................... 8
1.4 Related documents ................................................................................................................. 9
1.4.1 Applicable documents .................................................................................................................................. 9
1.4.2 Input .............................................................................................................................................................. 9
2 Review of Users Requirements ........................................................................................... 10
3 Algorithm .......................................................................................................................... 13
3.1 Overview .............................................................................................................................. 13
3.2 Basic underlying assumptions ................................................................................................ 13
3.3 Methodology ........................................................................................................................ 13
3.4 The retrieval Methodology .................................................................................................... 15
3.4.1 Input data.................................................................................................................................................... 15
3.4.2 Processing chain.......................................................................................................................................... 16
3.5 Limitations of the Product ..................................................................................................... 16
3.6 Differences with the previous version .................................................................................... 17
4 Product Description ........................................................................................................... 18
4.1 File Naming ........................................................................................................................... 18
4.2 File Format ............................................................................................................................ 19
4.3 Product Content .................................................................................................................... 19
4.3.1 Data File ...................................................................................................................................................... 19
4.3.2 Quicklook .................................................................................................................................................... 24
4.4 Product Characteristics .......................................................................................................... 24
4.4.1 Projection and Grid Information ................................................................................................................. 24
4.4.2 Temporal Information ................................................................................................................................. 24
4.4.3 Data Policies ................................................................................................................................................ 24
4.4.4 Contacts ...................................................................................................................................................... 25
5 Validation ......................................................................................................................... 26
6 References ........................................................................................................................ 28
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List of Figures
Figure 1: Schematic illustration of reflectance time-series from satellite instrument (blue line),
measurements of snow thickness on ice (red crosses) and in situ observation of ice
disappearance. ...................................................................................................................... 14
Figure 2. Quicklook image of the LIE product (5.5.2017) ............................................................... 14
Figure 3. Processing chain for LIE. ............................................................................................... 16
List of Tables
Table 1. Description of version numbering .................................................................................... 18
Table 2. Classification values and descriptions for LIE 250m ........................................................ 19
Table 3. Flag values and description in the LIE 250m data product .............................................. 19
Table 4. Description of NetCDF file attributes ............................................................................... 20
Table 5. Description of NetCDF layer attributes ............................................................................ 22
Table 6. File level variables ........................................................................................................... 23
Table 7. Validation datasets .......................................................................................................... 26
Table 8. Statistics of the difference between in situ and satellite derived ice disappearance date.
The unit used is days. ............................................................................................................ 27
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List of Acronyms
ATDB The Algorithm Theoretical Basis Document
C-GLOPS2 Copernicus Global Land Operations Lot 2
ECMWF European Centre for Middle Range Weather Forecasts
EO Earth Observation
ESA European Space Agency
FCDR Fundamental Climate Data Records
FMI Finnish Meteorological Institute
FSC Fractional Snow Covered area
LIE Lake Ice Extent
MODIS Moderate Resolution Imaging Spectroradiometer
MSI Multi-Spectral imaging Instrument
NIR Near Infrared
OLCI Ocean and Land Colour Instrument
OLI Operational Land Imager
SCE Snow Cover Extent
SLSTR Sea and Land Surface Temperature Radiometer
SYKE Finnish Environment Institute
TIRS Thermal Infrared Sensor
TOA Top of Atmosphere
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1 BACKGROUND OF THE DOCUMENT
1.1 EXECUTIVE SUMMARY
The Global Land Service is a component of the Copernicus Land service that provides a series of
bio-geophysical products on the status and evolution of land surface at global scale at mid and low
spatial resolution. Copernicus Global Land Lot 2 (C-GLOPS2) is addressing the thematic domain
of cryosphere and water.
Lake Ice Extent (LIE) is one of the Copernicus Global Land Service products. LIE product is an ice
classifier for freshwater bodies. The LIE is classified on pixel basis as 1) Fully snow covered ice; 2)
Partially snow covered ice/clear ice; 3) Open water. Classification is provided only for cloud free
pixels, with dedicated cloud masks for the Lake Ice Extent product. The gridded data product
covers Northern-Europe [5-45°E and 45-71°N] with 0.0025 degree (~250m) resolution.
1.2 SCOPE AND OBJECTIVES
One of the main objectives of Copernicus program is to provide to the scientific community as well
as other stakeholders, including policy makers, information required for several applications. This
has been detailed in the Service Specifications Document (GIOGL1-ServiceSpecifications). The
products are then operationally generated and delivered freely through the Copernicus Global
Land portal (http://land.copernicus.vgt.vito.be) in near real-time as well as in offline.
This Product User Manual (PUM) document provides user of the data with description of the
algorithm and its validation and description of the data product.
1.3 CONTENT OF THE DOCUMENT
This document is structured as follows:
Chapter 2 recalls the users requirements, and the expected performance
Chapter 3 summarizes the retrieval methodology
Chapter 4 describes the technical properties of the product
Chapter 5 summarizes the results of the quality assessment
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1.4 RELATED DOCUMENTS
1.4.1 Applicable documents
AD1: Annex I – Technical Specifications JRC/IPR/2015/H.5/0026/OC to Contract Notice 2015/S
151-277962 of 7th August 2015
AD2: Appendix 1 – Copernicus Global land Component Product and Service Detailed Technical
requirements to Technical Annex to Contract Notice 2015/S 151-277962 of 7th August 2015
AD3: GCOS-154, Systematic Observation Requirements for Satellite-Based Data Products for
Climate, WMO/GGOS Technical Document (2011)
AD4: IGOS, 2007: Cryosphere Theme Report 2007. WMO/IGOS Technical Document (2007).
1.4.2 Input
Document ID Descriptor
CGLOPS2_SSD Service Specifications of the Global Component of
the Copernicus Land Service.
CGLOPS2_ATBD_LIE-250m Algorithm Theoretical Basis Document of the LIE-
250m product
CGLOPS2_QAR_ LIE-250m Report describing the results of the scientific quality
assessment of the LIE-250m product
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2 REVIEW OF USERS REQUIREMENTS
According to the applicable document [AD2], the user’s requirements relevant for European LIE
250m are:
Definition:
For the baseline dataset for European Lake Ice Coverage (/Extent), the pixel size shall be
provided, depending on the final product, at resolutions of 100m and/or 300m and/or 1km.
Other finer or coarser resolutions may be defined based on specific user feedback and in
agreement with the contracting authority.
Geometric properties:
The target baseline location accuracy shall be 1/3 of the at-nadir instantaneous field of
view.
Pixel coordinates shall be given for centre of pixel.
Geographical coverage:
projection: geographical latitude, longitude
geodetical datum: WGS84
pixel size: 0,0025°
coordinate position: pixel centre
o Window coordinates:
Upper left: 5ºE, 71ºN
Lower right: 45ºE, 45ºN
Accuracy requirements:
Baseline: Wherever applicable the bio-geophysical parameters should meet the
internationally agreed accuracy standards laid down in document AD3. The lake ice extent
is not covered in the Fundamental Climate Data Records (FCDR) (AD3). Therefore, the
requirements are taken from sea ice variable. Target requirements for sea ice extent are
following:
Horizontal Resolution: 1-5 km
Temporal resolution: weekly
Accuracy: 5km
However, these are coarse assumptions for lake ice. Present product is based on Terra
satellite Moderate Resolution Imaging Spectroradiometer (MODIS) data and offers following
accuracy:
Horizontal Nominal Resolution: 250m
Temporal resolution: daily
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Accuracy: 250 m
User requirements related to LIE products are described by the IGOS Cryosphere Theme
report (AD4), in the ESA STSE North Hydrology (Fernández-Prieto et al. 2012) and in
publication on the User Requirements of CryoLand Snow and Land Ice services - Cryoland
(Malnes et al. 2015).
The main user groups interested in these satellite products are the climate and hydrological
modelling community and weather forecasting authorities. The following paragraphs will
summarize thematic, temporal and geometrical aspects concerning the LIE product.
Thematic aspects
The LIE product classifies a section of the freshwater body as either ice-covered or ice-free.
Additionally, a class is given for fully snow covered ice. The extent is usually defined in
terms of area (in square kilometres). The targeted thematic accuracy for this parameter is 5
% according to AD4 and less than 5 % according to ESA STSE North Hydrology project
(Fernández-Prieto et al. 2012).
Geometrical aspects
Mapping of ice on lakes requires fine spatial resolution due to the small size of some lakes.
The current spatial resolution for global and basin-scale applications for the parameter LIE
is 250 m for optical data with location accuracy of 1 pixel (Fernández-Prieto et al. 2012).
Temporal aspects
Spatial and temporal consistent data sets are important for climate modellers and numerical
weather predictions. Climate modellers need data sets over large areas (continental scale)
and for long time periods (decades). Aggregated (gridded) data sets in space are preferred.
The data sets do not need to be very frequent in time (monthly information is sufficient
according to Climate modellers). For other than climatic use, Malnes et al. (2015)
summarize that the majority of users request one day temporal resolution. When bridging
the often wishful thinking from users with what is actually possible to achieve with current
and near future satellite sensors, it should be realistic to obtain a temporal resolution of one
week, due to frequent cloud coverage at high latitudes where lake ice occur. There is hence
a need to also provide a level of refinement to the ice products, where temporal
interpolations provide the data regularity desired.
Near real-time data:
The climate modellers have little need for real-time data: annual updates (mostly
concerning ice break-up) are sufficient.
Weather predictors may in principle need near real time data, or at least data for the last
day. The most urgent need for real time data seems to be within the hydrological
community, where real time data is a pre-requisite for flood management.
Other aspects
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Satellite data on freezing and break-up dates of lake ice is also of interest for all
approached users, and in particular for validation of models. These data types can also be
of interest for environmental assessment and for biological monitoring.
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3 ALGORITHM
3.1 OVERVIEW
The Lake Ice Extent (LIE) in the Copernicus Global Land service is a three class lake ice
classification, which can be derived from optical satellite sensors of different resolutions. The main
use of the data is to define timing of events in the annual cycle of freshwater ice, i.e. the initiation of
ice formation, freezing up of lakes (the lake is completely covered by ice), initiation of melting
period, ice-out date (the day when the lake is completely ice-free). The gridded data product can
also be used to monitor the extent of ice, and this is usually defined in terms of area (in square
kilometers) or percentage of coverage.
3.2 BASIC UNDERLYING ASSUMPTIONS
Snow, (clear) ice and open water can be clearly distinguished from reflectance in
the near infrared (NIR, ~850nm) wavelengths (Perovich 1996; Latifovic and Pouliot
2007)
Water properties, i.e. content of clorophyl-a and suspended matter in water cause
mixing between clear ice and open water, only after reaching very high values
(Doxara et al., 2002; Ritchie et al., 2003).
The algorithm uses the Top-of-Atmosphere (TOA) reflectance. The errors due to
atmosphere and viewing geometry are considered acceptable due to the simplicity
of the algorithm.
The above mentioned errors are also small in calculation of dates for ice phenology
events, where lacking of observations due to cloud cover and low light conditions
are causing considerably higher error.
3.3 METHODOLOGY
The algorithm is based on differences in Near Infrared (NIR) -reflectance between ice, snow and
water. There are large differences in the reflectance from snow, ice and water in the visible and
NIR spectrum. There is clear difference in the reflectance of ice and dry snow cover. Therefore, the
distinction of lake ice with full snow cover is also relatively straightforward. What lies in between full
snow cover and clear ice (albedo between 0.08-0.2, respectively) are patches of snow over clear
ice or snow-ice (flooded and re-frozen snow) in different composition.
The reference reflectances were determined using observations from the extensive in situ
monitoring network of The Finnish Environment Institute (SYKE). SYKE monitors the ice coverage,
ice thickness and thickness of snow cover over lake ice and conducts observations of ice
phenology at several sites in Finnish inland waters. Finding the reference reflectances for the
algorithm was based on comparing time series of MODIS/Terra reflectances to the snow thickness
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observations over selected monitoring sites. The temporal change of the reflectance over a lake
follows the description is shown in Figure 1. The different surface classes on a lake 1) Fully snow
covered ice; 2) Partially snow covered ice/clear ice; 3) Open water can be clearly discriminated
using reflectance values from optical satellite data. The principal of determining reference
reflectances is shown in Figure 1. An example dataset is illustrated in Figure 2.
Figure 1: Schematic illustration of reflectance time-series from satellite instrument (blue line),
measurements of snow thickness on ice (red crosses) and in situ observation of ice disappearance.
Figure 2. Quicklook image of the LIE product (5.5.2017)
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3.4 THE RETRIEVAL METHODOLOGY
3.4.1 Input data
The algorithm uses top-of-atmosphere (TOA) data from optical satellite instruments. The LIE
classifier uses data from near-infrared channel (~850 nm). Other channels are used in cloud
masking (470 nm, 1.4 µm, 1.6 µm, 2.1 µm, 3.7 µm and 8.5 µm). Currently the product is based on
Terra/MODIS data. Because the algorithm is based on the reflectance thresholds for each class, it
is easily transferred to any other optical satellite instrument. The algorithm benefits from the spatial
averaging of medium resolution satellites (spatial resolution in the order of 100 m), but is also
applicable to higher -resolution instruments (spatial resolution in the order of 10-20 m).
Following satellite instruments are foreseen for producing LIE:
Terra/MODIS: An instrument employed in the current operative product.
Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR): Next instrument for
operative product.
Sentinel-3 Ocean and Land Colour Instrument (OLCI): Possibility for enhancing resolution.
Sentinel-2 Multi-Spectral imaging Instrument (MSI): Can be used for validation of medium
resolution data, as well as for producing higher resolution ice cover.
Landsat-8 Operational Land Imager (OLI)/ Thermal Infrared Sensor (TIRS): Can be used
for validation of medium resolution data, as well as for producing higher resolution ice
cover.
Suomi-NPP/VIIRS: Could be used as back-up data source in case of failure of Sentinel-3.
3.4.1.1 Auxiliary data
Land areas are masked out for better visualization and for masking out mixed pixels. The
land/sea/lake mask needs to be as accurate as possible to avoid mixed pixels of land and water.
Especially small featured water bodies, such as the lakes in Finland, are in need of accurate
masking. A 300 m buffer is used from shorelines derived from the datasets used to derive the
interpreted areas to avoid interpretation of shallow waters, with vegetation. The current
land/sea/lake mask is based on following datasets:
Finnish national 10m resolution land/water boundaries
EEA High Resolution Layer: Permanent Water Bodies 2012
GlobCover for Areas outside EU
HELCOM Marine area
Land and sea areas are masked out from the final product. Cloud masks are overlaid to the final
product.
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3.4.2 Processing chain
The processing chain of LIE is displayed in Figure 3. Direct broadcast Terra/MODIS data is
received at the National Satellite Data Centre (NSDC) of the Finnish Meteorological Institute (FMI).
The data is pre-processed to calibrated top-of-atmosphere (TOA) reflectance and emissivity data
by FMI. This data is collected to the LIE processing server.
The processing uses channels from 500m (Modis bands: 3, 6 and 7) and 1km reflectance bands
(Modis band: 26), as well as 1km emissive bands (Modis band: 20) for cloud detection. The LIE
estimation is made from 250m NIR-band (Modis band: 2). Two cloud layers are separated (high
and low clouds).
The LIE estimate, cloud masks and lake/sea/land masks are combined to create products from
individual swaths (there are 2-4 swaths used per day). Individual swaths are mosaicked, rejecting
data from sun elevation < 25°. The result is written to a GeoTIFF format. The data is then
converted to NetCDF4 with associated metadata for the Copernicus Global Land Service
distribution environment. Then the data is pushed to FTP server for ftp-pull from the services side.
Figure 3. Processing chain for LIE.
3.5 LIMITATIONS OF THE PRODUCT
Lake Ice Extent (LIE) is monitored using optical satellite data. Observations from optical satellite
sensors are restricted by cloud cover as well as low light conditions during late autumn and early
spring in the high northern latitudes due to polar night. Classification is provided only for cloud free
pixels, with dedicated cloud masks for the LIE product. The algorithm is not applied in sea areas.
Mapping of ice on lakes requires, especially in Scandinavia, fine spatial resolution due to the small
size of the lakes and relatively high amount of islands in some lakes. The gridded LIE data product
covers Northern-Europe (see geographical coverage above) with 0.0025 degree (~250m)
resolution. Currently the processing includes larger uniform water bodies (> 500 pixels).
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3.6 DIFFERENCES WITH THE PREVIOUS VERSION
The Copernicus Global Land LIE product and service is an evolution of the pre-operational LIE
product and service for Northern-Europe, developed and implemented within the FP7 project
CryoLand Snow and Land Ice Service. The algorithm for ice classification was first developed at
SYKE. The original algorithm at SYKE, used for following the development of lake ice in Finnish
inland waters (snow cover on lake ice) was a direct application of the Fractional Snow Covered
area (FSC) -algorithm, which was using Terra/MODIS channel 1 (620-670 nm) data (Metsämäki et
al. 2012; Metsämäki et al. 2005). Later it was realized that the distinction between the melting
stages of lake ice and overlying snow cover are difficult to distinguish from medium resolution
optical images. As the main application areas of the earlier lake ice product was to follow the
spring break-up of the lake ice cover and to potentially use of the product in weather simulations,
there did not seem to be a need for identifying the percentage of snow cover, but rather to get the
ice extent. Therefore a new algorithm has been developed for observations of inland waters. Also,
the channel was changed from MODIS channel 1 to channel 2 (841-876 nm), for the channel 2 is
more sensitive to changes between ice, water and snow and it is not so sensitive to changes to
water properties in low turbidity waters. As an additional value, the new product was given three
ice classes and water class: "Snow covered ice", "Partial snow/white ice", "Clear ice", "Open
water". The Copernicus Global Land LIE- classification is made more robust by reducing the
classes to 1) Fully snow covered ice; 2) Partially snow covered ice/clear ice; 3) Open water. As the
last stages of melting, before final break-up of the ice, are not as well pronounced as other
changes the “partial snow/ice covered” and “clear ice” classes were combined. On the other hand
the open water areas can be clearly distinguished from ice cover. Therefore, the product has
stronger focus now on the extent of ice on lakes. The fully snow covered ice still remains as a
separate class.
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4 PRODUCT DESCRIPTION
4.1 FILE NAMING
The Lake ice Extent (LIE) 250m product follows the naming standard:
LIE250_<YYYYMMDDhhmm>_<YYYYMMDDhhmm>_<AREA>_<SOURCE>_V<VERSION>.<EXTENSION>
Example:
LIE250_201702250700_201702261058_Baltic_MODIS _V1.0.1.nc
where,
first <YYYYMMDDhhmm> gives the start time of first swath and second
<YYYYMMDDhhmm> indicates the start time of last swath used in the data. YYYY, MM,
DD, hh, and mm denote the year, the month, the day, the hour, and the minutes (UTC
time), respectively.
<AREA> gives the spatial coverage of the file. In LIE, <AREA> is Baltic as it covers the
Baltic Sea Drainage basin area.
<SOURCE> gives the name of the sensor used to retrieve the product. MODIS referring to
Terra/MODIS satellite sensor
<VERSION> shows the processing line version used to generate these LIE 250m products.
The version denoted as M.m.r (e.g. 1.0.1), with ‘M’ representing the major version (e.g. V1),
‘m’ the minor version (starting from 0) and ‘r’ the production run number (starting from 1)
(Table 1).
Table 1. Description of version numbering
Version Differences Recommendations Announcement
Major Significant change to the algorithm.
Do not mix various major versions in the same applications, unless it is otherwise stated.
Via technical mailing list / website
Minor Minor changes in the algorithm
Can be mixed in the same applications, but require attention or modest modifications
Via technical mailing list / website
Run
Fixes to bugs and minor issues. Later run automatically replaces former
Consider it as a drop-in replacement.
To affected users
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4.2 FILE FORMAT
The LIE 250m products are delivered as a set of files:
Single-band Network Common Data Form version 4 (netCDF4) file with metadata attributes
compliant with version 1.6 of the Climate & Forecast conventions (CF V1.6) and containing
the following layers:
◦ LIE: Lake ice extent; The LIE layer also includes flag values for sea pixels, cloud pixels
and land pixels
A quicklook in a coloured GeoTIFF format. The quicklook is provided in factor 8 spatial sub-
samping from the LIE layer. (see Figure 2)
4.3 PRODUCT CONTENT
4.3.1 Data File
The data is a classification of lake ice state. The classes are described in Table 2.
Table 2. Classification values and descriptions for LIE 250m
Variable Class value Description Scaling factor Offset
LIE 1 Fully snow covered ice NA NA
2 Partially snow covered or snow free ice NA NA
3 Open water NA NA
The LIE data layer also contains flag values (Table 3) to describe missing data (no satellite image
data available), sea areas, cloud pixels and land pixels.
Table 3. Flag values and description in the LIE 250m data product
Variable Flag value Flag name Description
LIE
0 missing No satellite image data
4 sea_pixel Sea area
5 cloud_pixel Cloud covered area
6 land_pixel Land area
The netCDF files contain a number of netCDF metadata attributes
on the file-level (Table 4);
on the variable-level (Table 5);
Also the file level variables are given in Table 6.
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Table 4. Description of NetCDF file attributes
Attribute Description Data
Type Example(s)
Conventions Version of the CF-Conventions used string CF-1.6
title A description of the contents of the file string Baltic Lake Ice Extent (LIE) 250m
institution
The name of the institution that
produced the product, one of:
IPMA, TUWIEN, VITO NV, ZAMG,
BROCKMANN, FMI, CLS
string VITO NV
source
The method of production of the original
data. Note that the name of the platform
and sensor are described separately.
This is used more to distinguish from
e.g. ground-based measurements.
string Derived from EO satellite iamgery
history
A global attribute for an audit trail. One
line, including date in ISO-8601 format,
for each invocation of a program that
has modified the dataset.
It is recommended to start each line with
the date and time.
string
2006-01-01T00:00:00Z Preceding product
of Snow on Ice (SOI): Created at SYKE in
2006
2014-01-01T00:00:00Z Modified to Lake Ice
Extent in FP7 Cryoland -project (2010-2013)
2017-01-01T00:00:00Z Further developed
and validation in FP7 SEN3APP -project
(2014-2016)
2017-03-01T00:00:00Z Renamed to Lake
Ice Coverage (LIE) and made operational in
Copernicus Global Land Services - Lot2
Cryosphere (2017->)
references
Published or web based references that
describe the data or methods used to
produce it.
At least the link to the website product
page. Links to PUM and VR can be
added when we have links set up that do
not change over time.
string Contact to the key persons. See “Contacts”
in Table 12.
archive_facility
Specifies the name of the institution that
archives the product
IPMA
VITO
CLS
string VITO
product_version Version of the product of the form
VM.m.r string V1.0.1
time_coverage_start Start date and time of the total coverage
of the data for the product. string
<YYYYMMDDhhmm> formatted date on the
data product filename (start of the first
swath)
time_coverage_end End date and time of the total coverage
of the data for the product. string
<YYYYMMDDhhmm> formatted date on the
data product filename (start of the last
swath)
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Attribute Description Data
Type Example(s)
platform Name(s) of the orbiting platform(s) string Terra (EOS AM-1)
sensor Name(s) of the sensor(s) used string Moderate Resolution Imaging
Spectroradiometer (MODIS)
identifier Unique identifier for the product string
urn:cgls:lie_v1.0.1_250m:<LIE product
filename without extension>
example:
urn:cgls:lie_v1.0.1_250m:LIE250_20170225
0700_201702261058_Baltic_MODIS_SYKE
_V1.0.1
parent_identifier
Identifier of the product collection that
the product belongs to in Copernicus
Global Land Service metadata
catalogue.
string urn:cgls:lie_v1.0.1_250m
long_name Extended product name string Lake Ice Extent
orbit_type
Orbit type of the orbiting platform(s), one
of
GEO
LEO
GEO, LEO
string SSO, LEO
processing_level Product processing level of the form Lx string L3
processing_mode
Processing mode used when generating
the product.
Near Real Time: produced within fixed
time after end of acquisition
Consolidated: improvement of NRT
values within a defined
consolidation time window after
NRT
Reprocessing: (once-off) archive back-
processing activity, completing the
historical time series with maximal
quality/consolidation
Offline: other production independent of
NRT schedule (e.g. assembly of
climatology or time series product)
string Near Real Time
copyright
Text to be used by users when referring
to the data source of this product in
publications (copyright notice), of the
form
Copernicus Service Information [Year]
[Year] is to be replaced by the year
when the product is processed.
string
The product was originally created at SYKE
and further developed with funding from
European Commission FP7 Cryoland and
FP7 SEN3APP projects. The product is
jointly operated under the Copernicus
Global Land Services Lot2 Cryosphere team
(SYKE, FMI and ENVEO).
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Table 5. Description of NetCDF layer attributes
Attribute Description Data
Type Example(s)
CLASS Internal attribute that distinguishes between
e.g. data variables and dimensions.
String DATA
standard_name A standardized name that references a
description of a variable’s content in CF-
Convention’s standard names table. Note
that each standard_name has corresponding
unit (from Unidata’s udunits).
String sea_ice_area_fraction
long_name A descriptive name that indicates a
variable’s content. This name is not
standardized.
Mandatory when a standard name is not
available.
String Baltic lake Ice Extent
(LIE)
units Units of the variable’s content, taken from
Unidata’s udunits library.
Omit for dimensionless indicators.
Fractions should be indicated by
scale_factor.
String NA
scale_factor Multiplication factor for the variable’s
contents that must be applied in order to
obtain the real values. Omit in case the scale
is 1.
Float32. Float64 in
case the data
variable is 32-bit
integer. See section
8.1 of CF
conventions
document for details
NA
add_offset Number to be added to the variable’s
contents (after applying scale_factor) that
must be applied in order to obtain the real
values. Omit for offset 0.
Float32. Float64 in
case the data
variable is 32-bit
integer. See section
8.1 of CF
conventions
document for details
NA
valid_range Smallest and largest possible digital values
that the variable can take (not the actual
min/max occurring in the data).
Vector of 2 values (min, max).
Missing data and flags are to be represented
by one or several values outside of this
range.
Same as data
variable
(1,3)
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_FillValue Single value used to represent missing or
undefined data and to pre-fill output in case
a non-written part of data is read back. Value
must be outside of valid_range.
Typically same as missing_value.
Same as data
variable
0
missing_value Single value used to represent missing or
undefined data, for applications following
older versions of the standards. Value must
be outside of valid_range.
Typically the same as _FillValue.
Same as data
variable
0
grid_mapping Reference to the grid mapping variable String crs
ancillary_variables Optional identification of other closely related
layers in the same file.
String NA
flag_masks Provides a list of bit fields expressing
Boolean or enumerated flags. Data variable
is logically AND’ed with this bit field value to
check for presence of corresponding flag (in
list of flag_meanings).
Only for bitwise quality (QFLAG) layers.
Same as data
variable
NA
flag_values Provides a list of the flag values. Used in
conjunction with flag_meanings.
Used when the data layer contains multiple
flags rather than single _FillValue, or when
the data layer is a classification.
Same as data
variable
4,5,6
flag_meanings Descriptive words or phrases for each flag
value.
Required when either flag_masks or
flag_values (or both) are used.
String (blank
separated list)
sea_pixel, cloud_pixel,
land_pixel
Table 6. File level variables
File level variables Description Data
Type Example(s)
Time Labels used in NETCDF metadata. Hours
since base date: 1.1.1950
The variable has also attributes (not listed
here)
Float 588 655 (for 2.3.2017)
Lon X-coordinate
The variable has also attributes (not listed
here)
Float 5.0025
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Lat Y-coordinate
The variable has also attributes (not listed
here)
Float 70.9975
LIE Lake Ice Extent classification
The variable has also attributes (not listed
here)
Int 1
crs Spatial reference. The variable has as attributes: grid_mapping_name inverse_flattening longitude_of_prime_meridian semi_major_axis spatial_ref GeoTransform EPSG_code
String Float Float Float String String String
latitude_longitude 298.257223563 0 6378137 proj b EPSG:4326
4.3.2 Quicklook
The quicklook is a GeoTIFF file. The spatial resolution is sub-sampled, using nearest neighbour re-
sampling by factor 8, i.e. to 1km resolution.
The quicklook is coded equal to LIE. It is a coloured image. The quicklook is coded in 1 byte.
The quicklook is provided with an embedded colour table (Table 2 and Table 3 above).
4.4 PRODUCT CHARACTERISTICS
4.4.1 Projection and Grid Information
The product is displayed in a regular latitude/longitude grid (plate carrée) with the ellipsoid WGS 1984 (Terrestrial radius=6378km). The resolution of the grid is 0.0025°. Coordinate reference is the centre of the pixel. It means that the longitude of the upper left corner of the pixel is (pixel_longitude – angular_resolution/2.)
4.4.2 Temporal Information
The LIE products are daily composites. The temporal information from two labels constructed as: <YYYYMMDDHHMM> in the filename corresponds to the start date and time of the first and last swath used in the composite. Example: LIE250_201702250700_201702261058_Baltic_MODIS_SYKE_V1.0.1.nc
4.4.3 Data Policies
Any use of the LIE 250m Product Collection implies the obligation to include in any publication or
communication using these products the following citation:
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"The LIE product was generated by the land service of Copernicus, the Earth Observation program
of the European Commission. The research leading to the current version of the product has
received funding from various European Commission Research and Technical Development
programs. The product is based on TERRA/MODIS 250m data ((c) NASA and downlinked and
distributed by FMI).”
The user accepts to inform Copernicus about the outcome of the use of the above-mentioned
products and to send a copy of any publications that use these products to the following address
4.4.4 Contacts
Accountable contact: European Commission Directorate-General Joint Research Centre
Email address: [email protected]
Scientific, Production and Distribution contact: Flemish Institute for Technological Research
(VITO), Belgium
Email address: [email protected]
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5 VALIDATION
The validation was conducted under EU FP7 development project SEN3APP, aiming to build
satellite data product services for dedicated customers. One of the products in the SEN3APP
portfolio was Lake Ice Extent from optical satellite data, developed at the Finnish Environment
Institute (SYKE). The product performance was evaluated by comparing the date of disappearance
of ice cover from the field of view from the observation point to the satellite date of ice
disappearance derived from optical satellite data; in this case Terra/MODIS satellite data. The in
situ variable for ice disappearance is determined as the date when ice has disappeared from the
observation point (field of view) entirely. The validation was carried out for 34 Finnish lakes,
stretching across the country in latitude and spanning four years, namely 2010-2013. Table 7
below lists the in situ datasets available for validation. The product was validated with following
steps:
1. Determine in situ site locations used for validation
2. Extract a time-series of sub-regions of LIE- data product from radius of 2.0 km around the
in situ observation site
3. Extract the days when the observation areas are not totally covered by clouds (this omits
the possibility of ice underneath the clouds).
4. Compare the in situ observation date and the satellite observation date (calculate statistical
measures; average, STD and median)
Table 7. Validation datasets
In situ dataset Freezing and breakup Ice thickness and snow on ice thickness
Unit Date cm
Accuracy ±1 day ±1 cm
Observation interval Daily observations 3 per month (10th, 20th and 30th)
Areal extent Entire observation period: 122 sites Entire observation period: 161 sites
Temporal coverage 1753–2015 (changing network) 1912–2015 (changing network)
Considering the simple implementation and adaptation of the method for different optical satellite
sensors, the performance of the method was good. Table 8 shows the average difference between
in situ and satellite observations and the standard deviation of the difference for the four years,
2010-2013, used for validation. This shows that on average the difference is around +3 days and
median of the dataset being +2 days, the standard deviation being 5.2 days.
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Table 8. Statistics of the difference between in situ and satellite derived ice disappearance date. The
unit used is days.
2010 2011 2012 2013 All
Average 3.4 5.4 2.5 1.8 3.3
Median 2 5 2 1 2
STD 5.8 5.5 4.4 4.1 5.2
In future, the Lake Ice Extent product will be spatially extensively validated against high resolution
LIE products derived from optical satellite data of Sentinel-2, with Landsat-8 as back-up applying
procedures and protocols developed in the FP7 project CryoLand, including statistical information.
Methodology also exists (developed in FP7 SEN3APP project) for validation against ice in situ
observations. These data are also used off-line as the data becomes available after the ice
season.
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6 REFERENCES
Doxaran, D., D., Froidefond, J. M., Lavender, S., & Castaing, P. 2002. Spectral signature of highly turbid waters: Application with SPOT data to quantify suspended particulate matter concentrations." Remote sensing of Environment 81(1), 2002: 149-161.
Fernández-Prieto, D., Duguay, C., Gauthier, Y., Gustafsson, D., Malnes, E., Mattila, O.-P., Rontu, L., Rott, H., Samuelsson, P., & Solberg, R. (2012). ESA STSE North Hydrology: Development of multi-mission satellite data products in support of atmospheric and hydrological modeling of cold regions. In, EGU General Assembly. Vienna, Austria
Latifovic, R., & Pouliot, D. (2007). Analysis of climate change impacts on lake ice phenology in Canada using the historical satellite data record. Remote Sensing of Environment, 106(4), 492–507 Malnes, E., Buanes, A., Nagler, T., Bippus, G., Gustafsson, D., Schiller, C., Metsämäki, S., Pulliainen, J., Luojus, K., Larsen, H.E., Solberg, R., Diamandi, A., & Wiesmann, A. (2015). User requirements for the snow and land ice services – CryoLand. The Cryosphere, 9, 1191-1202 Metsämäki, S., Mattila, O.-P., Pulliainen, J., Niemi, K., Luojus, K., & Böttcher, K. (2012). An optical reflectance model-based method for fractional snow cover mapping applicable to continental scale. Remote Sensing of Environment, 123, 508-521 Metsämäki, S.J., Anttila, S.T., Markus, H.J., & Vepsäläinen, J.M. (2005). A feasible method for fractional snow cover mapping in boreal zone based on a reflectance model. Remote Sensing of Environment, 95, 77-95 Perovich, D.K. (1996). The optical properties of sea ice / Donald K. Perovich ; prepared for Office of Naval Research. [Hanover, N.H.]: US Army Corps of Engineers, Cold Regions Research & Engineering Laboratory ; [Springfield, Va. : Available from National Technical Information Service
Ritchie, J. C., Zimba P. V., and Everitt, J. H. 2003. Remote sensing techniques to assess water quality. Photogrammetric Engineering & Remote Sensing 69(6): 695-704.