Copernicus Land Monitoring Service
Pan European High Resolution
Snow & Ice monitoring
Day 1: Currently operational Products
Land Monitoring

Online user meeting, 15-10-2020

Copernicus Land Monitoring Service
Pan European High Resolution
Snow & Ice monitoring
Day 1: Currently operational Products

Land Monitoring

Welcoming and Introduction
Hans Dufourmont: Hans.Dufourmont@eea.europa.eu andMatteo Mattiuzzi: Matteo.Mattiuzzi@eea.europa.eu
EEA

Online user meeting, 15-16.10.2020

A g e n d aLand
Monitoring

Day One - Presentation of the currently operational HR-S&I service based on Sentinel-2 data
Thursday 15 October 2020
Time - duration

Topic

Presenter

09:30 - 15’

Introduction of the Pan European HR snow & ice monitoring of CLMS
- CLMS context- user needs

H. Dufourmont / M. Mattiuzzi EEA

09:45 -15’

Overview product and system description- list of Sentinel-2-based products- product access- timeliness and system

G. Salgues / F. Marti MAGELLIUM

10:00 - 30’

Snow product description- algorithm description- scientific validation- use-cases

S. Gascoin - CESBIO

10:30 - 20’

Answers to questions

All

10:50 - 30’

Ice product description- algorithm description- scientific validation- use-cases

M. Kubicki - ASTRI POLSKA

11:20 - 20’

Answers to questions

All

11:40 - 20’

Meeting Adjournment- Conclusions, perspectives

H. Dufourmont / M. Mattiuzzi EEA

P r o d u c t

a s s e s s m e n t-

P a r a m e t e r

r a n k i n g

LandMonitoring

1st survey: State of the art of European National snow and ice cover services + Assessmentof user requirements; October 2015

Users or Activity- Score 0: Parameter not mentioned or mentioned very briefly (by users)- Score 1: Parameter would be useful/nice to have (as expressed by the users)- Score 2: Parameter is essential to have (as seen by the users)

C o p e r n i c u s

P o l a r

m i s s i o n -U s e rr e q u i r e m e n t s

LandMonitoring

Based on the Polar Expert Group meeting in spring 2017 inBrussels it was confirmed that the most neededparameters regarding snow are:• Snow cover,• Fractional snow cover, and• Snow water equivalent

In situ data exists
but need forstandardisation
Methods are
there

EO data is there
It’s needed!

Services exist
Let’s do it!

Assessing user requirements - Snow Covered Area
Land
Monitoring

2nd survey: Update of user requirements + Draft technical specifications; June 2017

Report: CONTINUATION OF SNOW & ICE TASK. D 3.2: Summary Report to Support EEA , 2017

1) Desirability:
Score 0: Not desired by users
Score 1: Nice to have
Score 2: Desired by users, essential

1-2-32) Monitoring service existence:
Score 0: Monitoring service/s exist at the European level
Score 1: Monitoring service exists but with certain limitations
Score 2: Monitoring service does not exist at the European level3) Feasibility:
Score 0: Not feasible to monitor by the current observation means
Score 1: Potentially feasible to monitor with limitations; research needed
Score 2: Feasible to monitor by the current observation means

Assessing user requirements - Snow Water Equivalent
Land
Monitoring

2nd survey: Update of user requirements + Draft technical specifications; June 2017

Report: CONTINUATION OF SNOW & ICE TASK. D 3.2: Summary Report to Support EEA , 2017

1) Desirability:
Score 0: Not desired by users
Score 1: Nice to have
Score 2: Desired by users, essential
2) Monitoring service existence:
Score 0: Monitoring service/s exist at the European level
Score 1: Monitoring service exists but with certain limitations
Score 2: Monitoring service does not exist at the European level3) Feasibility:
Score 0: Not feasible to monitor by the current observation means
Score 1: Potentially feasible to monitor with limitations; research needed
Score 2: Feasible to monitor by the current observation meansK e yr a t i o n a l e/

u n d e r p i n n i n g

f o r

a

c o r eE U

s e r v i c eLandMonitoring

Expected key benefits of a European Copernicus S&I service:• Free and easy access to data

• Robustness and comparability of data across national borders• A reliable long term service is key for allowing the transition from national approaches to a European
RS based approach.

Complementary benefits:• Collaboration, exchange of experience and data between countries• Methods development:• Testing existing methods in different climate regions• Share ideas and data

• Cross National benefits:• Snow information from other (neighbouring) countries• Flooding and river basin management relies on harmonised cross boundary snow melt information• Standardisation of methods

Products overview,
timeliness & data accessLand Monitoring

Germain Salgues & Florence Marti(Magellium, France)Currently operational HR-S&I monitoring - Products overview
Land
Monitoring

High-Resolution (20 m x 20 m) Snow & Ice products over EEA-39.
SNOW

ICE

The Fractional Snow Cover (FSC) product provides foreach pixel the fraction (0% – 100%) of the surface
covered by snow at the top of canopy (FSC-TOC) andon the ground (FSC-OG).

The River and Lake Ice Extent (RLIE) product provides theriver and lake area covered by snow-covered or snow-free ice.

Near-real time products

Description

Type Projection

Raster - WGS84 / UTM

Delayed time products

Description

Type Projection

The Persistent Snow Area (PSA) product is generated
on a yearly basis and provides the extent of persistentsnow cover, i.e. the area where snow is present
throughout the hydrological year.

The Aggregated River and Lake Ice Extent (ARLIE) product isgenerated quarterly and provides the percent coverage ofsnow-covered or snow-free ice on lakes and on 10 km river
sections.

Raster
WGS84 / UTM + ETRS89 LAEA (EPSG: 3035)

VectorETRS89 LAEA (EPSG: 3035)

Technical documentation is available on the CLMS portal (product user manual, algorithms description, quality assessment)
https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-snow-and-ice-monitoringCurrently operational HR-S&I monitoring - System overview
Land
Monitoring•

The system is hosted on the
WEkEO DIAS (Data and Information
Access Services).

•

A DIAS is a cloud provider, whichallows centralized access toCopernicus EO data.

Currently operational HR-S&I monitoring - System overview
Land
MonitoringOnce the L1C Sentinel-2 data are available on the Copernicus Service Hub, the NRT HR-S&I
products are then published within a maximum of 3 hours.

The system developed fulfills the performance requirements:••

The 3-hour turnaround requirement is met for 77% of the products (September 2020)
vs 90% in August.
The average turnaround is 1 hour in nominal conditions, and the observed delays
occurred over few days in early september.

Currently operational HR-S&I monitoring - System overview
Land
Monitoring

Every effort is made to provide NRT HR-S&I products within these turnaroundtimes.

••

The 12h turnaround requirement in reference to sensing time is met for 70%of the products (September 2020)
More than 80% of the time is spent between sensing time and publication of
the Sentinel-2 L1C by ESA

Source: Copernicus Sentinel Data Access 2019
Annual report13Currently operational HR-S&I monitoring- Extension of product time series
Land
Monitoring

An archive reprocessing campaign for Sentinel-2 data from the 1st of September
2016 to the 5th of July 2020 is on-going.
••

The extension from 09/2016 of HR-S&I products will be published by the endof 2020
Products over the Alps and Pyrenees are already available on the HR-S&I
portal.

14

Currently operational HR-S&I monitoring - Data access
Land
Monitoring●

Land Copernicus portal:○ https://land.copernicus.eu/pan-european/biophysical-parameters/highresolution-snow-and-ice-monitoring●

Web-Interfaces:○ HR-S&I Finder allows search, view and download capabilities:
https://cryo.land.copernicus.eu/finder○ HR-S&I Browser allows search and view capabilities:
https://cryo.land.copernicus.eu/browser●

HR-S&I products benefit from a WMS API○ HR-S&I FSC: https://cryo.land.copernicus.eu/wms/FSC○ HR-S&I PSA: https://cryo.land.copernicus.eu/wms/PSA○ HR-S&I RLIE: https://cryo.land.copernicus.eu/wms/RLIE

Here is a short tutorial on how to manage WMS temporal products on QGIS:→ CLMS technical library: https://land.copernicus.eu/user-corner/technical-library/hrsi-wms

Currently operational HR-S&I monitoring - Data access
Land
Monitoring●

HR-S&I products HTTP API:○ A Python script, users oriented, allows parameterised search and bulk
downloading:■ https://drive.google.com/file/d/1CS5V3Ixhkj8AZvXFcNs4Rod8EVuDrGHb/view(https://github.com/eea/copernicus-land-monitoring-service-ApiTools)

Help about this script can be obtained using: “python HRSI_downloader.py --help”> python HRSI_downloader.py output_folder -hrsi_credentials hrsi_auth.txt -query -productType FSC productIdentifier 32TMS -obsDateMin 2016-09-01T00:00:00Z -obsDateMax 2020-10-15T00:00:00Z●○

Alternative R package: https://github.com/MatMatt/clmsapi○

HTTP API Url:
■ https://cryo.land.copernicus.eu/finder/resto/api/collections/HRSI/search.json

Local access (documentation on-going):○ WEkEO users working on the cloud infrastructure will have direct access tothe data, allowing higher performance for NRT usage of the entire HR-S&I
collection.
○ This access will be available through WEKEO user dashboard (JupyterHub
and VMs)

Currently operational HR-S&I monitoring - Data access
Land
Monitoring

For any question about usage of the HR-S&I portal or additional
information related to the HR-S&I products, you can contact the CLMS
service desk at https://land.copernicus.eu/contact-formSnow products description
Land Monitoring

Simon Gascoin (Cesbio, Toulouse, France)Fractional Snow Cover product: algorithm description
Land
MonitoringFirst step snow cover area detection (origin: Theia Snow product)

19

Fractional Snow Cover product: algorithm description
Land
Monitoring

Second step fractional snow cover computation

20

Fractional Snow Cover product: algorithm description
Land
Monitoring

Second step fractional snow cover computation

21

Fractional Snow Cover product: algorithm description
Land
Monitoring

Third step on-ground fractional snow cover22

Fractional Snow Cover product: scientific validation
Land
MonitoringFirst step snow cover area detection

Results better than
Sen2CorGascoin, S., Grizonnet, M., Bouchet, M., Salgues, G., and Hagolle, O.: Theia Snow collection: highresolution operational snow cover maps from Sentinel-2 and Landsat-8 data, Earth Syst. Sci. Data, 2019.

23

Fractional Snow Cover product: scientific validation
Land
Monitoring

Second step fractional snow cover computation

Evaluation of FSC algorithm● SPOT 6-7 (French Alps)● Terrestrial cameras (Spanish Pyrenees, French Alps)● Terrestrial lidar (Austrian Alps)● Crowd-sourced data (Alps, Pyrenees)
Results● RMSE ~ 25% at 20 m resolution● Higher accuracy at coarser resolution● Results impacted by the 10 m (2σ) misregistration of Sentinel-2 images
Gascoin, S.; Barrou Dumont, Z.; Deschamps-Berger, C.; Marti, F.; Salgues, G.; López-Moreno,
J.I.; Revuelto, J.; Michon, T.; Schattan, P.; Hagolle, O. Estimating fractional snow cover in open
terrain from Sentinel-2 using the Normalized Difference Snow Index. Remote Sens., 202024

Fractional Snow Cover product: scientific validation
Land
Monitoring

Second step fractional snow cover computation

FSC

25

Fractional Snow Cover product: scientific validation
Land
Monitoring

Second step fractional snow cover computation

26

MetricFSC

Sample size

337

RMSE

23%

Mean error-7.2%Standard deviation

21%

Correlation

0.67

Quality layer (QC FSC)
Land
Monitoring

Use the QC layer to get highest quality retrievals
0: highest
quality

True color

FSC

QC

27

Fractional Snow Cover product: use case #1
Land
Monitoring

Prepare your hike

Hiking trails

29

Fractional Snow Cover product: use case #1
Land
Monitoring

Prepare your hikeno snowsnow

30

Fractional Snow Cover product: use case #1
Land
Monitoring

Prepare your hikeno snowsnow (take your crampons!)

https://labo.obs-mip.fr/multitemp/usingsentinel-2-to-prepare-your-hike/
31

Fractional Snow Cover product: use case #2
Land
Monitoring

Explore snow line elevation variability

Sentinel FSC product on 27 Sep 202032

Fractional Snow Cover product: use case #2
Land
Monitoring

Explore snow line elevation variability

Sentinel FSC product on 27 Sep 202033

Fractional Snow Cover product: use case #2
Land
Monitoring

Explore snow line elevation variability

34

Fractional Snow Cover product: use case #2
Land
Monitoring

Explore snow line elevation variability
FSC productSnowline

DEM

35

Fractional Snow Cover product: use case #2
Land
Monitoring

Explore snow line elevation variability
Snow line elevation vs. northing

36

Fractional Snow Cover product: use case #2
Land
Monitoring

Explore snow line elevation variability
Snow line elevation vs. northingelevation vs. easting

Snow line

37Fractional Snow Cover product: use case #2
Land
Monitoring

Explore snow line elevation variability
Snow line elevation vs. northingelevation vs. easting

Snow line

38Fractional Snow Cover product: use case #2
Land
Monitoring

Explore snow line elevation variability

Etc.. See notebook:https://github.com/sgascoin/snowLineElevation

39

Fractional Snow Cover product: use case #3
Land
Monitoring

Track the snow melt

Andorra40

Fractional Snow Cover product: use case #3
Land
Monitoring

Track the snow melt

Andorra2 km
41

Fractional Snow Cover product: use case #3
Land
Monitoring

Track the snow melt

Andorra42

Fractional Snow Cover product: use case #3
Land
Monitoring

Track the snow melt

Melted areas

Andorra43

Fractional Snow Cover product: use case #3
Land
Monitoring

Track the snow melt

Melted areas

Andorra44

Fractional Snow Cover product: use case #3
Land
Monitoring

Track the snow melt

Melted areas

Andorra45

Delayed-time product: Persistent Snow Area
Land
Monitoring% of snow observations in clear observations

46

Delayed-time product: Persistent Snow Area
Land
Monitoring

Probability of snow detection is >95% from 01 May to 30 Sep
Persistent snow area (2020)

Bernina Range

47

Delayed-time product: Persistent Snow Area
Land
Monitoring

Probability of snow detection is >95% from 01 May to 30 Sep
Persistent snow area (2020)
Randolph Glacier Inventory

Bernina Range

48

Delayed-time product: Persistent Snow Area
Land
Monitoring

Probability of snow detection is >95% from 01 May to 30 Sep
Persistent snow area (2020)
Randolph Glacier Inventory

Bernina Range
Piz Cancian

49

Delayed-time product: Persistent Snow Area
Piz Cancian

Land
Monitoring

Persistent snow area (2017)

Persistent snow area (2020)

Piz Cancian

50

Make your own product: annual snow cover duration
Land
Monitoring

https://frama.link/snow

51

Make your own product: annual snow cover duration
Land
Monitoring

Spot the avalanches!

52

Snow products: conclusion
Land
Monitoring-

Sentinel-2 provides unprecedented observations of the snow cover-

Known issues of FSC products-Some snow areas can be classified as no-snow (steep shaded slopes)
Some turbid water surfaces can be detected as snow
Some cloud areas can be classified as snow
Accuracy in forest areas is not documented-

User feedbacks are welcome (even positive feedbacks!)-

All software is open source-

Atmospheric correction and cloud mask
https://github.com/CNES/MAJA
Snow detection https://gitlab.orfeo-toolbox.org/remote_modules/let-itsnow

53

Ice products description
Land Monitoring

Michal Kubicki (Astri Polska, Poland)Currently operational HR-S&I monitoring - Ice products
Land
MonitoringHigh Resolution Snow & Ice
monitoring (HR-S&I)

River and Lake Ice Extent(RLIE)

Aggregated River and Lake
Ice Extent (ARLIE)ARLIE product:● ARLIE● ARLIE - MTD

RLIE product:● RLIE● RLIE - QC
● RLIE - QC FLAGS● RLIE - MTD

55

Ice product description - RLIE algorithm
Land
Monitoring

River and Lake Ice Extent productSentinel-2
L1C
Cloud & cloud
shadow detection

Atmosphere &terrain correction

Sentinel-2 L2A+(surface reflectances)

Cloud
mask

open watersnow-covered or snow-free ice
cloudsother features
no data

EU-HYDRO

RLIEclassification

RLIE is produced on rivers, canals, ditches, inland
waters and transitional waters defined by EUHYDRO over EEA-39.
56

Ice product description - RLIE algorithm
Land
Monitoring

Sentinel-2
L1C product

Preprocessing
Atmosphere &terrain correction

Cloud & cloud
shadow detection

MAJA L2A
processor

Spectral indices computation:●

NDSI - Normalised Difference Snow
Index

●

NDWI2- Second Normalised
Difference Water Index●

NDVI - Normalised Difference
Vegetation Index●

BSI - Bare Soil Index

Sentinel-2 L2A(surface reflectances)

Downsampling
to 20 m x 20 m
Spectral indicescomputing
Masking by
AOI
57

EU-HydroIce product description - RLIE algorithm
Land
Monitoring
Sentinel-2 L2A(surface reflectances)Classification

Downsampling

Minimum Distance Classification●

clear contrast between mainfeatured classes●

10 Sentinel-2 tiles selected for the
preparation of training polygons for:- snow coverage- ice coverage- water coverage- vegetation- soilSpectral indicescomputing

Masking by AOIClassification

58

EU-HydroIce product description - RLIE algorithm
Land
Monitoring

Sentinel-2 L2A(surface reflectances)

Postprocessing
Downsampling

Spectral indicescomputing

Assigning each pixel to its final RLIE
class:
open watersnow-covered or snow-free
icecloudsother features
no data

Masking by AOI

Cloud Mask
layer fromMAJA

Classification

Recoding
No Data
Mask layerfrom MAJA

RLIE product
59

EU-HydroIce product description - RLIE product samples
Land
Monitoring

05 February 2019. Mietkowskie Lake, Poland
Sentinel-2 L1C RGB composition
product

EU-Hydro database outline

RLIE

open water snow-covered or snow-free ice
clouds other features no data60

Ice product description - RLIE product samples
Land
Monitoring

01 April 2019. Nisser, Fyresvatn and Nesvatn Lakes, Norway
Sentinel-2 L1C RGB composition
product

EU-Hydro database outline

RLIE

open water snow-covered or snow-free ice
clouds other features no data61

Ice product description - RLIE product samples
Land
Monitoring

08 January 2017. Zegrzynskie Lake and Vistula river, Poland
Sentinel-2 L1C RGB composition
product

EU-Hydro database outline

RLIE

open water snow-covered or snow-free ice
clouds other features no data

62

Ice product description - scientific validation
Land
Monitoring

RLIE scientific validation
As the ARLIE is derived by means of a spatial aggregation of individual RLIE products, itsquality assessment is based on RLIE product results.

Very High Resolution satellite opticalimages (VHR)
5 SPOT 6/7 images at 1.5 m resolution from:● Austria● Norway● Poland● Sweden● Romania
1 Pleiades 1A/1B image at 0.5 m resolution
from:● PolandIn situ measurements

Ground truth ice sheet information provided
by:● Finland● Hungary● Serbia● Czech Republic (in situ data delivery
ongoing)63

Ice product description - scientific validation
Land
Monitoring

Scientific validation: VHR images
ICE algorithm:
validation results
Overallaccuracy

95.5 %

Kappa

88.7 %

Number ofobservations

168 91864

Ice product description - scientific validation
Land
Monitoring

Scientific validation: in situ measurements
ICE algorithm:
validation results
Overallaccuracy

86.8 %

Kappa

75.2 %

Number ofobservations

114

65

Ice product description - scientific validation
Land
Monitoring

Validation of mobile ice (e.g. flowing floe) based on
VHR images and in situ measurements is not possible

ICE algorithm
scientific validation HIGHLIGHTS

VHR images and in situ measurements acquired not at
the exact time as Sentinel-2 images

The dataset of in situ measurement restricted by:● cloud cover on Sentinel-2 images● type of the ice declared by observers (e.g. borderice, ice drift, moving ice cover)66

Ice product description - RLIE limitations
Land
Monitoring❏ Dark/thin ice misclassification

EU-Hydro database outline

open water snow-covered or snow-free ice
clouds other features no data

Sentinel-2 L1C RGB and RLIE products, 03 March 2018. The example of the dark/thin ice
misclassification by the ICE algorithm. Szczecin Lagoon, Poland.
67

Ice product description - RLIE limitations
Land
Monitoring❏ Water bodies affected by water bloom

EU-Hydro database outline

open water snow-covered or snow-free ice
clouds other features no data

Sentinel-2 L1C RGB and RLIE products, 31 August 2019. The example of ICE algorithm
misclassification due to water bloom. Lake Balaton, Hungary.
68

Ice product description - RLIE limitations
Land
Monitoring❏ Turbid water

EU-Hydro database outline

open water snow-covered or snow-free ice
clouds other features no data

Sentinel-2 L1C RGB and RLIE products, 09 May 2020. The example of the turbid water
misclassification by the ICE algorithm. Rhône River and Geneva Lake, Switzerland.
69

Ice product description - RLIE limitations
Land
Monitoring❏ Constantly changing riverbeds or lake tables

EU-Hydro database outline

open water snow-covered or snow-free ice
clouds other features no data

Sentinel-2 L1C RGB and RLIE products, 14 September 2020. The example of the EU-Hydro
polygon referring to non-water features. Víztároló Reservoir, Hungary.
70Ice product description - ARLIE algorithm
Land
Monitoring

ICE algorithm for ARLIE processing
Aggregated River and Lake Ice Extent (ARLIE) - the spatial aggregation of the RLIE product providing at eachsatellite acquisition date the summary statistics of each RLIE class on individual lakes and over 10 km long river
sections

INPUT DATA●RLIE product

OUTPUT DATA●ARLIE product

RLIE product provided by the ICE algorithm●○

EU-Hydro database

The ARLIE AOI is defined as:○ Subset of the River_Net_p:■ polygons longer than 40 km■ rivers connecting lakes○ Subset of the Canals_p:■ polygons longer than 20 km○ Ditches_p○ InlandWater○ Transit_p

○
○
○
○
■
■
■
■
■

quarterly delivered product - to be
asked via the user survey
data format: SHP - to be asked via theuser survey
provided for individual lakes and 10 km
longitudinal sections of rivers
projection: ETRS89 LAEA
ARLIE attributes:

OBJECT_ID
object_nam
river_km
datewater_perc
71

■
■
■
■
■

ice_perc
other_perc
cloud_pernd_perc
QC

Ice product description - ARLIE algorithm
Land
Monitoring

Aggregated River and Lake Ice Extent
RLIE

EU-Hydro
CGNELIN
attribute

Intersection with
EU-Hydro

Overlapping
rules

RLIE data merging

EU-Hydrosegment
geometries

Calculating summary
statistics for individuallakes and 10 km long river
sections

ARLIE
72

Ice product description - ARLIE product samples
Land
Monitoring

ARLIE, 01.01.2019 - 31.03.2019. Example of an ARLIE product on a river basin for the first quarter ofyear 2019. Product attributes are filtered to show the ARLIE results on a 10 km long river section.
Background: Sentinel-2 L1C RGB composition, 15.02.2019.
73

River and Lake Ice Extent product: use-case #1
Land
Monitoring❏ Detection of ice jams and better planning of ice breaking activities

Ice jams together with ice-breakers on the Oder River, Poland. Source: Iwicki S.,‘Prowadzenia akcji lodołamania na rzece Odrze, Warcie i Noteci’74

River and Lake Ice Extent product: use-case #1
Land
Monitoring❏ Detection of ice jams and better planning of ice breaking activities

EU-Hydro database outline

open water snow-covered or snow-free ice
clouds other features no data

Sentinel-2 L1C RGB and RLIE product, 03 March 2018. Ice jam on the Oder river which
occured in March 2018. Oder river course near Widuchowa, Poland.
75

River and Lake Ice Extent product: use-case #1
Land
Monitoring❏ Detection of ice jams and better planning of ice breaking activities

EU-Hydro database outline

open water snow-covered or snow-free ice
clouds other features no data

Sentinel-2 L1C RGB and RLIE product, 08 January 2017. The flowing flow on the Danube
river which can lead to ice jams. Danube river course in Budapest, Hungary.
76

River and Lake Ice Extent product: use-case #2
Land
Monitoring❏ Regulating hydropower work

EU-Hydro database outline

open water snow-covered or snow-free ice
clouds other features no data

Sentinel-2 L1C RGB and RLIE products, 10 February 2017. The Wloclawek hydropower,
Poland.
77

River and Lake Ice Extent product: use-case #2
Land
Monitoring❏ Regulating hydropower work

EU-Hydro database outline

open water snow-covered or snow-free ice
clouds other features no data

Sentinel-2 L1C RGB and RLIE products, 04 February 2018. The Bergeforsen hydropower
plant on Indalsälv river with the completely frozen water. Bergeforsen, Sweden.
78

Copernicus Land Monitoring
Service
Pan European High Resolution
Snow & Ice monitoring –Service Evolution
Day 2: Planned service extensions for HR-S&I
Land Monitoring

Online workshop, 15-16.10.2020

79

Copernicus Land Monitoring
Service
Pan European High Resolution
Snow & Ice monitoring –Service Evolution
Land Monitoring

Day 2: Welcoming and Introduction
Hans Dufourmont:
Hans.Dufourmont@eea.europa.eu andMatteo Mattiuzzi:
Matteo.Mattiuzzi@eea.europa.eu
EEA
Online workshop, 15-16.10.2020

80

Planned service extensions for HR-S&I - Introduction
Land
Monitoring

Day Two - Planned service extensions based on Sentinel-1 and user feedback
Friday 16 October 2020
Time duration

Topic
Presenter
Introduction of planned HR-S&I extensions based on
Sentinel-1
09:30 - 30’ - objectives- integration with the existing HR-S&I component
H. Dufourmont / M.
- user consultation
Mattiuzzi - EEA

Planned snow products description
10:00 - 60’ - list of products and format- algorithm description

T. Nagler / K. Luojus ENVEO/FMI

11:00 - 20’ Answers to questions

All

Planned ice products description
11:20 - 40’ - list of products and format- algorithm description

M. Kubicki - ASTRIPOLSKA

12:00 - 20’ Answers to questions

All

Meeting Closure- schedule
12:20 - 10’- reprocessing- user survey objectives

H. Dufourmont / M.
Mattiuzzi - EEA

81

Planned service extensionsfor HR-S&I - Snow products(wet/dry snow conditions)Land MonitoringThomas Nagler (ENVEO, Austria)

Planned service extensions for HR-S&I - wet/dry Snow products
Land
Monitoring

Product
Wet / Dry Snow Conditions within
HR Snow Extent
This product provides the extent ofwet (melting) snow within the totalsnow extent (dry and wet) given by
the Sentinel-2 HR Snow Mask.
The Wet / Dry Snow conditions arederived using Sentinel-1 SAR data,acquired on the same date as the
HR Snow Extent Product.

Sentinel-1 SAR – AlpsLand
Monitoring

Lake Constance

Innsbruck84

Physical Background of Wet Snow Detection using C-Band SAR data
Land
Monitoring

Groundbased Backscatter Signatures – Leutasch/Alps

Using C-Band backscatter wet snow can be detected against dry snow andsnowfree areas. While, dry snow and snowfree areas can hardly bediscriminated.
85

SAR Wet Snow Processing LineLand
Monitoring86

Iceland - Detection of Wet Snow during Melt PeriodLand
Landsat-8, 27 June 2015;
Monitoring

Sentinel-1, 26 June 2015;

Confusion matrix for the classes snow (S) and snow-free, for snow classification based on Landsat (LS) andSentinel-1 (S1) data. S1 results are shown for snow maps based on . — overall agreement rate ().

Alps - Detection of Wet Snow during Melt Period
Land
Monitoring

Landsat-7, 5 June 2015

Sentinel-1, 2 June 2015

Confusion matrix for the classes snow (S) and snow-free (F) inÖtztal Alps, for snow classification based on Landsat (LS) andSentinel-1 (S1) data. — overall agreement rate ().

Land
Monitoring

SAR Wet Snow Products – Example
Land
Monitoring

Innsbruck90

Dry Snow
Wet Snow
Layover / foreshortening

Wet / Dry Snow Conditions for HR Snow Map from Sentinel-2
Land

Kaunertal, 27 May 2017
Monitoring

S2 27.5.2017

S2 snow product• Fractional SC
S1 Snow Melt product:• binary

Fractional
Snow CoverS1 - Wet snow
Radar shadow

S1 – nodataWet Snow State on top of Optical (dry and wet) Snow Extent
Land
Monitoring92

Product Specification
Land
Monitoring

Product nameSAR wet snow
product (HR-SWS)(incl. metadata andQA layer);

Map
Projection

UTM,(organised in
S2 tiles)Wet snow
Product Pixel
spacing(40 m x 40 m)
or
60 m x 60 m
or
100 m x 100 m

Equ. number
Correspondingof looks
Optical snow
(ENL) of SAR
product
data

Coverage~17 ENL~40 ENL

Sentinel-2 S&I
Part 1, FSC map~110 ENL(highest
accuracy)93

Overlap of S2 FSC
map and S1 data
(forests and water
bodies are masked)

Spatial Coverage
Land
Monitoring

Wet / Dry snow condition will be provided as separate file matching Sentinel-2
based HR snow product (FSC)

Coverage of
S&I snow
servicesusing theSentinel-2
tile schema,
which is also
applied for
SAR wetsnow
mapping94

Overlap of Sentinel-1 and Sentinel-2 HR SnowLand
Monitoring

•
•
•
•

The wet / dry information is given only for cloud free snow areas in the S2
FSC map
S1 and S2 cover the same area and are acquired on the same day
S1 ascending and descending passes are combined providing the maximum
melt extent for this day
Coverage: daily overlapping S1 and S2 tracks over EEA39 domain

Coverage and Overlap of Sentinel-1 IWS and Sentinel-2 tracks for 1 and 2 Nov. 2019.

95

Reprocessed versus Near Real time products
Land
Monitoring

NRT Operations• The SAR Wet snow processor is triggered automatically when new overlapping
data of S1 and products of S2 are becoming available for the EEA-39 countries.
• The production will be monitored in terms of generated products• Product will be provided through the Copernicus Land Monitoring Portal
Reprocessing Operations• Reprocessing of wet snow products are covering the period September 2016 toAugust 2020, covering the EEA39 domain and applying the same processing
system as for the Near Real Time service. The production will be monitored interms of generated products .
• The processing is applied if all needed input data, such as Sentinel-1 data andoverlapping S2 HR-Snow maps are available.
• Product will be provided through the Copernicus Land Monitoring Portal
Consistency between NRT and Reprocessed Products is given as the sameprocessor is applied.
96

Validation of Wet / Dry snow conditions on HR Snow Extent Map
Land
Monitoring

Validation Approaches:
1. Validation of S1 wet snow within FSC mask from Sentinel-2 (from S&I Part 1)
using in-situ snow measurements combined with temperature data (insitu, high resolution numerical weather model data).

1. Validation of S1 wet snow within FSC mask from Sentinel-2 (from S&I Part 1)
with high resolution (HR) grids of numerical weather models (NWM),
using combined temperature and humidity information as indicator for melting
conditions.

97

Quality Assessment and Validation – Pan-European domain
Land
Monitoring

Validation of wet snow product is challenging due to the high spatial andtemporal variability of wet/ refrozen/dry snow caused by weather, solar
illumination, height, etc. and representativity of in situ data

Recall, Precision, F-Score, False Alarm Rate, Hit Rate, Kappa Coefficient98Summary on Wet / Dry Snow Condition Product for HR Snow ProductLand
Monitoring

•
•
•

•

Product consists of (i) binary map on Wet / Dry snow conditions for HR Snow
Extent (FSC > 90%) derived from Sentinel-1 SAR data, (ii) quality layer and (iii)
metadata
Sentinel-1 SAR data are acquired on the same day as HR-Snow Extent Product.
In the case an area is covered by Sentinel-1 crossing orbits at the same day, themaximum extent of wet snow conditions is provided.
Product (wet /dry snow map, quality layer) is provided in UTM/WGS84
coordinates, matching the tiles of HR-Snow Extent, GEOTIFF-8bit, pixel spacing< 100m.
Limitations:– In forested areas and for open water bodies the SAR wet snow detection
fails forested areas and open water areas are masked using EO-hydroand Tree Cover Density (also applied in S2 HR-Snow Extent).
– Wet / dry snow detection requires closed snow cover (FSC>90%)99

Planned service extensionsfor HR-S&I - Snow products(Gap-filled product)
Land Monitoring

Kari Luojus (FMI, Finland)

Planned extensions for HR-S&I - Gap-filled snow cover products
Land
Monitoring

Overview
•

•
•
•
•
•

S2 Based fractional snow cover maps to be gap-filled– spatially using S1 based wet snow maps and DEM data in nonforested mountainous regions– temporally retaining the last valid observation in latest X days
Spatial resolution of 40m x 40m
Daily production (a new gap-filled product every day)
1 hour after last S1 based WS and S2 based FSC products become
available on the DIAS
The date of each observed product pixel is recorded -> end-user can
decide to use data from only the certain last days (e.g. some may want toretain 3 days of data, some 14 days)
Limitations for spatial gap-filling:– S1-based snow products available to non-forested mountainous
regions– S1-based data mainly available during the snow melt season– Applies the available S1 and S2 data of the same day + including
historical information (increases the overall spatial completeness)-> a new product is generated every dayPlanned extensions for HR-S&I - Daily cumulative gap-filled snow coverLand
Monitoring

Spatial gap-filling using DEM

Snow line
Cloud gaps
(snow covered)
Cloud gaps
(snow free)Planned extensions for HR-S&I - Daily cumulative gap-filled snow coverLand
Monitoring

Temporal gap-filling -> improves spatial completenessS-2 (290 km swath width)
below example from AATSR (500 km swath)

Credit: ESA, FMI

Planned extensions for HR-S&I - Daily cumulative gap-filled snow coverLand
Monitoring

Spatial gap-filling

Part 2 S1 Wet Snow
Map
Find snow coveredareas

S1 Based Snow Cover
Map

DEM

Merge

Part 1 S2 Snow Cover
Map(Cloud-covered pixels of S2
snowmap are gapfilled with
S1-based data)

Previous S1+S2 snow
cover map
(last observed snow
conditions)Gap-filled Snow Cover
Map

Planned extensions for HR-S&I - Daily cumulative gap-filled snow coverLand
Monitoring

Data format

•
•
•
•
•
•

Spatial coverage: EEA39 Area
Temporal coverage: Latest 14 days (or acc. to user feedback?)
Data format: GeoTIFF
Projection: According to S1 + S2 -based products
Spatial resolution: 40m x 40m
Rasters:– Snow presence (on/off)– Observation age (age of information in each pixel - days)– Observation source (Sensor: S1, S2, DEM-based)– Other QI?

Planned extensions for HR-S&I - Daily cumulative gap-filled snow coverLand
Monitoring

Validation activities
Using snow cover information from• Snow courses (FI,SE,NO)• Using synoptic snow depth information(FMI)– Daily snow depth measurements at
6:00 AM in 250 - 300 locations• Using model-based data (SAFRAN,
Crocus)• Uncertainty estimation using artificial gap(for cumulative products)• Examination of seasonal snow evolution
using model-based data for meteorologicaleventsSynoptic and automatic weather stations of FMI

Credit: FMI

Planned extensions for HR-S&I - Daily cumulative gap-filled snow coverLand
Monitoring

Validation activities
Using snow cover information from• Webcam networks– Daily averaged data fromprocessed 30 mins/1 hour
time resolution images– 7 locations in Finland, probably
more in future in NH– More locations by visualinspection also possible

MONIMET Camera Network Locations

Credit: FMIPROT & Camera Network Portal, MONIMETPlanned extensions for HR-S&I - Daily cumulative gap-filled snow coverLand
Monitoring

Validation activities
Using snow cover information from• Webcam networks– Daily averaged data fromprocessed 30 mins/1 hour
time resolution images– 7 locations in Finland, probably
more in future in NH– More locations by visualinspection also possible

Sodankylä peatland camera view
and processing results in 2019 melting
seasonCredit: MONIMET, Tanis, 2020.

Planned extensions for HR-S&I - Daily cumulative gap-filled snow coverLand
Monitoring

Validation activities
Using snow cover information from• Webcam networks– Daily averaged data fromprocessed 30 mins/1 hour
time resolution images– 7 locations in Finland, probably
more in future in NH– More locations by visualinspection also possible

Camera views in Torgnon

Credit: The PhenoCam Camera Network

Planned service extensionsfor HR-S&I - Ice products
Land Monitoring

Michal Kubicki (Astri Polska, Poland)

Planned service extensions for HR-S&I - Ice products
Land
MonitoringHigh Resolution Snow & Ice
monitoring (HR-S&I)

Sentinel- 1 based River andLake Ice Extent

S-1 and S-2 based AggregatedRiver and Lake Ice Extent

RLIE S-1

ARLIE S-1 + S-2
Combined River and Lake Ice
ExtentRLIE S-1 + S-2
111

River and Lake Ice Extent based on Sentinel-1 data: algorithm description
Land
Monitoring

Sentinel-1 based River and Lake Ice Extent productSentinel-1 GRDH Copernicus Data Hub
Speckle Filtering &Terrain correction

Calibration

Sentinel-1 GRDH Sigma0 dB
no iceice

EU-HYDRO

RLIE S-1
classification

RLIE S-1 is produced on rivers, canals, ditches,inland waters and transitional waters definedby EU-HYDRO over EEA-39.
112

River and Lake Ice Extent based on Sentinel-1 data: algorithm description
Land
Monitoring

S-1 based River and Lake Ice Extent (RLIE S-1)
RLIE S-1 product - providing the ice and snow extent on inland waters using Sentinel-1 images

INPUT DATA●

Sentinel-1 Level-1 product:○ GRD - Ground Range Detected○ two polarisations: VV, VH○ Interferometric Wide Swath mode(IW)
● EU-Hydro database
The discussion about range of the polygon layersof the EU-Hydro database overlapping the EEA39 countries to be used is ongoing.
Probable range of AOI:
○ river wider than 150 m○ selected lakes for which S-1 basedclassification is confirmed to be reliable

OUTPUT DATA●RLIE S-1 product○ NRT product○ data format: GeoTIFF○ spatial resolution: 20 m x 20 m
○ projection: UTM/WGS84○ RLIE thematic classes:■ ice■ no ice■ possible no ice and no water
areas derived from HRL layers

113

River and Lake Ice Extent based on Sentinel-1 data: processing path
Land
Monitoring

Sentinel-1 GRD
product

Calibration

Reprojection
and rescaling

Rescaling to dB

EU-Hydro

Masking by AOIWater/icethreshold

RLIE S-1

11
4

River and Lake Ice Extent based on Sentinel-1 data: product samples
Land
Monitoring

03 March 2018. Lower Elbe river, Germany
Sentinel-2 L1C RGB composition
1 product

EU-Hydro database outline

no iceice
11
5

RLIE S-River and Lake Ice Extent based on Sentinel-1 data: product samples
Land
Monitoring

08 January 2017. Danube river, Hungary
Sentinel-2 L1C RGB composition
1 product

EU-Hydro database outline

no iceice
11
6

RLIE S-River and Lake Ice Extent based on Sentinel-1 data: restrictions
Land
Monitoring

Restrictions of Sentinel-1 based ice detection● areas covered by smooth ice‘When perfectly plain water is covered by perfectly plain ice,
microwaves penetrate the ice without absorption and are reflected at
the ice-water interface away from the sensor, and the ice covered lake
would in theory appear completely black.’ (Tom M. et all, 2020)‘In the formation phase, ice is indistinguishable from water because it
has a similar reflectivity to water, the signal is very weak on the noiselevel of the device, the dialectric constant is almost identical. (...)
Automating such recognition can be very difficult, it is better wheninterpreted visually.‘ (Prof. Magnuszewski A., Warsaw University, e-mailconsultations)

117

River and Lake Ice Extent based on Sentinel-1 data: restrictions
Land
Monitoring

05 February 2019. Mietkowskie Lake, Germany
Sentinel-2 L1C RGB composition
1 product

EU-Hydro database outline

no iceice
11
8

RLIE S-River and Lake Ice Extent based on Sentinel-1 data: restrictions
Land
Monitoring=

Sentinel-2 RGB combination and Sentinel-1 GRD image band combination (VV,VHmVV*VH)
from the same date and area. 05 February 2019, Mietkowskie Lake, Poland.

11
9

River and Lake Ice Extent based on Sentinel-1 data: restrictions
Land
Monitoring● wet snow on ice cover‘As the wet snow absorbs microwave radiation, it is not possible to obtain
information about the ice cover underneath.’ (Łoś H., Pawłowski B., 2017)● narrow rivers, streams and canals● availability VV and VH polarisations‘The ice sheets which appear dark in the radar image are also partly
misclassified as water, which makes the image recorded with VV-polarization
the least suitable choice for automatic image classification.’ (Sobiech J.,
Dierking W., 2013)‘For the RADARSAT-2 image, the different polarizations were classified
separately and the classification results were significantly different,
demonstrating the importance of polarization.(...) HH-polarization is the mostsuitable polarization and VV-polarized data deliver the poorest results.’
12(Sobiech J., Dierking W., 2013)
0River and Lake Ice Extent based on Sentinel-1 data: restrictions
Land
Monitoring

Providing lake ice information from Sentinel-1 data
Identification of the problem:
Since the reflectivity of water and ice cover surfaces are similar, very often it is not possible todiscriminate ice cover from water (especially during the ice formation process).

Lake ice detection possibilities:●●

lakes with gas bubbles rising to the water surface (regular possibility)
lakes with wavy surface due to the strong wind during ice formation process(unpredictable possibility)

Possible solutions:●●

Selection of particular lakes where ice cover classification can provide reliable results
Comparison of cloudless RLIE S2 products with wet snow map usage

Risk:
RLIE S-1 cannot be provided for lakes at satisfactory level of confidence.
12
1

River and Lake Ice Extent based on Sentinel-1 data: restrictions
Land
Monitoring

Selection of lakes for reliable ice classification based on Sentinel-1 data
● RLIE S-1 produced for the entire EU-Hydro database extent including lakes● RLIE S-1 Quality Control layers with the low level of confidence for lakes - to inform users
about possibly degraded quality● Providing RLIE S-1 scientific validation only for rivers
During the HR-S&I operational phase:● Comparison of the ice classification results of Sentinel- 2 and Sentinel-1 data (RLIE S1 and
RLIE S2 products) and limitation of the lakes for which RLIE S-1 products are produced byanalyzing regularities of the ice classification for both S-1 and S-2 data. This can be
performed when historical HR-S&I data (from September 2016) for both Sentinel-1 andSentinel-2 observations will be available.

Due to large amount of data necessary for processing, final results of this approach are
expected at the end of 2021 or at the beginning of 2022.
12
2

River and Lake Ice Extent based on Sentinel-1 data: restrictions
Land
Monitoring

Selection of lakes for reliable ice classification based on Sentinel-1 data approach#1
Starting from2021/2022

EU-Hydro databaseextend for Inland
Water and Transitional
Water

RLIE S-1 data from2016-2020
Comparison of theclassification ofcorresponding RLIE S-1
and RLIE S-2 products forthe same date

RLIE S-2 data from2016-202012
3

Lakes identified asreliable for S-1 basedclassification

River and Lake Ice Extent based on Sentinel-1 data: restrictions
Land
Monitoring

Selection of lakes for reliable classification of ice covered by wet snow from
Sentinel-1 data - approach#2*
EU-Hydro databaseextend for Inland
Water and Transitional
Water

Wet Snow Map

Information provided by
the Wet Snow Map

Lakes identified asreliable for S-1 basedclassification in case of
wet snow occurrence on
the ice* Possibility of the wet snow map usage for the lake areas will be consulted with consortium partners, therefore
this approach may be impossible for implementation.
124

River and Lake Ice Extent based on Sentinel-1 data: quality layers
Land
Monitoring

Quality layers for S-1 based River and Lake Ice Extent (RLIE S-1 QC)
QC layers analogously to S2-based RLIE product generated using High Resolution Layers
INPUT DATAHRL Imperviousness

HRL Grassland

HRL Tree Cover Density

HRL Water and Wetness

125

River and Lake Ice Extent based on Sentinel-1 data: scientific validation
Land
Monitoring

Scientific validation activities
As ARLIE S1 + S2 is derived by means of a spatial aggregation of individual RLIE S1 + S2
products, its quality assessment is based on RLIE S1 and RLIE S2 product results.

RLIE S-1 scientificvalidation

in- situ
measurements

Finland

Hungary

Corresponding
Sentinel-2 images

Czech
Republic

Serbia12
6

Combined River and Lake Ice Extent based on S-1 and S-2 observations
Land
Monitoring

Combined S-1 and S-2 based River and Lake Ice Extent (RLIE S-1 + S-2)●●●

only for the rivers wider than 150 m and selected lakes
for RLIE S-1 and S-2 data from the same day
Prioritise classes ‘snow-covered or now-free ice’, ‘open water’, ‘other features’ derived from S2-based
RLIE product in case of overlapping with a S-1 based RLIE product.

●

for the same acquisition dates of RLIE S1 and RLIE S2: clouded areas in RLIE S2 product replaced withRLIE S1 product information

RLIE S-1
Merging policy

RLIE S-1 + S-2

RLIE S-2
RLIE S-2 class clouds < RLIE S-1
classes

RLIE S-2 classes: open
water, snow-covered orsnow-free ice, otherfeatures > RLIE S-1

12
7

RLIE S-2 class nodata < RLIE S1classesAggregated River and Lake Ice Extent based on S-1 and S-2 observations
Land
Monitoring

Combined S-1 and S-2 based Aggregated River and Lake Ice Extent (ARLIE S-1 + S-2)
create combined ARLIE data in the same way as ARLIE S-2 based but using combined RLIE asa source [following approach as for ARLIE based on S-2 observations]● only for the rivers wider than 150 m and selected lakes● generated for 10 km long river sections● frequency of generation:○ daily update○ monthly○ quarterly○ twice a year for periods: 1st of November – 30th of April and 1st of May –31th of October● data format: SHP

Users will be asked to decide.
128Aggregated River and Lake Ice Extent based on S-1 and S-2 observations
Land
Monitoring

Aggregated River and Lake Ice Extent S1 + S2
RLIE S1 + S2

EU-Hydro
CGNELIN
attribute

Intersection with
EU-Hydro

Overlapping
rules

RLIE data merging

EU-Hydrosegment
geometries

Calculating summary
statistics for individuallakes and 10 km long river
sections

ARLIE S1 + S2
12
9

Copernicus Land Monitoring
Service
Pan European High Resolution
Snow & Ice monitoring –Service Evolution
Land Monitoring

Day 2: Meeting adjournment
Online user meeting, 15-16.10.2020

Planned service extensions for HR-S&I - Meeting adjournment
Land
Monitoring• User Survey on product specifications:– Open to public from 16.10-13.11.
– Accessed from the land portal: https://land.copernicus.eu/paneuropean/biophysical-parameters/high-resolution-snow-and-icemonitoring• Project schedules:– Product consolidation and system setup until end of June 2021
– Start of operational phase of HR S&I part 2: July 2021
• Operational NRT processing• Archive reprocessing back to September 2016 (analogue to HR-S&I part 1)

High-resolution
Snow & Ice monitoring
Land MonitoringThanks you for your participation.
Visit: https://land.copernicus.eu