Intercomparison of Snow Extent Products from Earth ...€¦ · MODIS NSIDC (Hall et al.) AVHRR Pathfinde r Binary 5 km daily 1992 -2004 AVHRR CCRS (Zhao, et al) CryoLan d Fractiona

Intercomparison of Snow Extent Products from Earth Observation Data

Land Product Validation and Evolution Workshop 28 – 31 January 2014

Thomas Nagler, Elisabeth Ripper, Gabriele Bippus, Helmut Rott

Richard Fernandes

Kari Luojus

Sari Metsämäki

Dorothy Hall

David Robinson

Bojan Bojkov

FMI

Contents

• Motivation and Objectives

• Basics for detecting Snow from Optical Satellite Images

• Overview of Snow Extent Products

• Examples of Intercomparison Results

• Summary and Conclusions

• Overview of SnowPEX

28-30 Jan 2014 LPVE WS / ESRIN Thomas Nagler

Motivation and Objectives

3

OBJECTIVES: • Intercompare global / hemispheric (pre) operational snow

products derived from different EO sensors and generated by means of different algorithms, and assess the product quality.

• Prepare for the SNOWPEX study.

(Derksen & Brown 2012)

Time series of Northern Hemisphere June snow cover from NOAA snow chart CDR.

Rate of Snow Extent decrease: 1979-2012: -21.5% / decade


Detection of Snow with Optical Satellite Data

4

0.56 0.67 0.87 1.600

5

10

15

20

25

30

35

40

45

snow free< 25 %26-50 %51-75 %76-99 %100 %

Wavelength [mm]

TOA Reflectance [%]

Unforested areas, Baltic Region, 29/3/2011

VIS / NIR together with 1.6 µm Band provides the essential information for detecting snow and for FSC estimation

Thomas Nagler

Spectral Signatures from AATSR Data

Continental to Hemispheric Satellite Snow Extent Products

5

Name Product type

Pixel Spaci

ng

Frequency

Period Main Sensor

Organisation

NOAA IMS

Binary 4 km daily 2004 -

OPT, PMW, + manual

NOAA (Helfrich et al.)

NOAA IMS

Binary 24 km daily 1997 -2004

OPT, PMW

NOAA

GlobSnow

Fractional

1 km daily-

monthly

1996-2012

ATSR2AATSR

SYKE et al.

MOD10 Fractional

0.5 km Daily 2000 -

MODIS NSIDC (Hall et al.)

AVHRR Pathfinder

Binary 5 km daily 1992 -2004

AVHRR CCRS (Zhao, et al)

CryoLand

Fractional

(Europe)

0.5 km daily 2000 - MODIS ENVEO / SYKE et al.


Processing Line for SE Product Intercomparison

6

Fractional Snow Map at 0.01 deg Pixel size

Exclude areas with:Snow free in both products, no-data, clouds, open water

Pixel 2 Pixel Comparison

Aggregate binary or fractional SE to FSC at 0.01 deg

• Number of snow pixels• RMSD• Bias• Correlation Coefficient• Scatterplot• Standard Deviation

Fractional SE Map(Binary SE Map)

GlobCover surface mask

Cloud mask

Reference Product

Aggregate to FSC at 0.01 deg

VHR + HR EO

data

MS-Unmixing methodTopographic corrections

Separate FSC for forest/non-forest/plain areas/mountains

Product

Map of Mean Absolute FSC Difference

Aggregate to FSC at 0.01 deg


Maximum Snow Extent 1-7 March 2010

7

MOD10

GlobSnow

IMS


MOD10 (~500m)

CryoLand (~500m)


GlobSnow (~1 km)

IMS (~4 km)


MOD10 (~500m)

CryoLand (~500m)


GlobSnow (~1 km)

IMS (~4 km)

IMS (~4km)

Mean Absolute FSC Difference MOD10 versus GlobSnow - 1.3.-31.5.2010

number of pixels (N)

unforestedforest

(Σ|FSCMOD10 – FSCGlobSnow|)/N


Mean Absolute FSC DifferenceMOD10 versus CryoLand / MODIS 1.3.-31.5.2010

no forestforest (Σ|FSCMOD10 – FSCCryoLand|)/N

Number of pixels


Pixel by Pixel Intercomparison of MOD10 versus GlobSNOW FSC product


Synergistic use of Snow Products from different sources

13


1

IMS merged with SSM/I and MODIS snow extent

Products Indicating Snow

1: All2: MODIS, SSM/I3: MODIS, IMS4: MODIS5: SSM/I, IMS6: SSM/I7: IMS

10: Permanent ice11: Snow free land

2 3 4 5 6 7 10 11

February 6, 2006

Time series of weekly Max. Snow Extent March – May 2010 PanEuropean Domain

14

16/ October 2012 Sentinel-3 OLCI SLSTR Preparatory WS

0

5

10

1520

25

30

35

4045

50

55

60

12 13 14 15 16 17 18 19 20 21 22 23 24

%

week of year 2010

0

5

10

1520

25

30

3540

45

50

5560

12 13 14 15 16 17 18 19 20 21 22 23 24

%

week of year 2010

0

5

10

1520

25

30

35

4045

50

55

60

12 13 14 15 16 17 18 19 20 21 22 23 24

%

week of year 2010

MOD10

GlobSnow

CryoLand

IMS

0

5

10

1520

25

30

3540

45

50

5560

12 13 14 15 16 17 18 19 20 21 22 23 24

%

week of year 2010

IMS

% o

f to

tal a

rea

% o

f to

tal a

rea

% o

f to

tal a

rea

% o

f tot

al a

rea

Summary and Conclusions

15

• A limited data set of different snow extent products from various optical satellite data and applying different algorithms from has been analyzed and reveals systematic differences in the snow extent between the products.

• FSC differences of the various products show a typical pattern with significant differences in forested areas, and less pronounced differences in high mountain regions, but more detailed analysis is needed.

• A short time series of weekly snow extent maps from different sensors has been generated revealing differences in the snow coverage and trend of snow extent, but longer time series need to be analyzed. On the hand it indicates the need for intercalibration of snow extent products from different sensors and algorithms for studying long term trends in the snow extent.

• The synergistic use of snow information from different sensors (optical, microwave) has a high potential to improve the quality of snow cover monitoring. Thomas Nagler

SNOWPEXSatellite Snow Products Intercomparison & Evaluation Excercise An ESA initiative contributing to WMO Global Cryosphere Watch and WCRP CLiC

carried out under the lead of ENVEO in cooperation with FMI, SYKE, EC and CCRS.

US National Ice Center / NOAA/ NESDIS, Cryospheric Processes and RS Laboratory, City University of NY

Global Snow Lab, Rudgers University Cryospheric Sciences Laboratory, NASA

and associated international contributors

SnowPEX Objectives

The primary objectives are

• Intercompare and evaluate global / hemispheric (pre) operational snow products derived from different EO sensors and generated by means of different algorithms, assessing the product quality by objective means.

• Evaluate and intercompare temporal trends of seasonal snow parameters from various EO based products in order to achieve well-founded uncertainty estimates for climate change monitoring.

• Elaborate recommendations and needs for further improvements in monitoring seasonal snow parameters from EO data.

SnowPEX aims to bring together scientists and institutions of seasonal snow pack monitoring for assessing the quality of current satellite-based snow products derived from EO data, and working out guidelines for improvement.

The project will support the setup of a consolidated operational satellite snow observation system for the Global Cryosphere Watch Initiative of the WMO and help to improve the snow cover data base for climate monitoring, as addressed by the WCRP-CliC programme

Main Tasks within SnowPEX

• Review of Algorithms and products focusing on Snow Extent (SE) and Snow Water Equivalent (SWE)

• Definition of Protocols and methods for intercomparison of Snow Extent and Snow Water Equivalent products

• Compilation of global reference data sets for quality assessment of SE and SWE products

• Intercomparison of SE / SWE products from various institutions and quality assessment against reference data base

• Hemispheric/Global SE and Snow Mass Trend Analysis and potential synergy of SE and SWE products

• Conclusion and recommendations for satellite snow monitoring

SNOWPEX will organize 2 International Workshops for Snow Product Intercomparison (IWSPI) with the aim to bring together all major providers of continental to global snow products. The 1st IWSPI is planned to be held in June/July 2014.

19

Pixel by Pixel Intercomparison of MOD10 versus CryoLand/Modis FSC product

20

SNOWPEXSatellite Snow Products Intercomparison &Evaluation Excercise An ESA initiative in response to the recommendations of the WMO Global Cryosphere

Watch WS Toronto, January 2013

carried out under the lead of ENVEO in cooperation with FMI, SYKE, EC and CCRS.

OBJECTIVESThe project aims to bring together scientists and institutions monitoring

seasonal snow for improving the quality of snow products from EO data.

The project will intercompare and evaluate global to continental (pre-) operational snow products (snow extent; SWE) from different sensors

and algorithms for improving the observation of the spatial and temporal variability of the seasonal snow pack.

Planned Project Start: Q1 / 2014

Us National Ice Center, Cryospheric Processes and RS Laboratory, NYC

Interactive Multisensor Snow and Ice Mapping System (IMS)

22

Thomas Nagler

Interactive Multisensor Snow and Ice Mapping System (IMS)

Satellites

GOES (E & W)

MeteoSat (MSG & 7)

MTSAT

NOAA Automated Snow & Ice

AVHRR (Channels 1 & 3)

MODIS (Channel 8)

ASCAT

AMSU (Derived snow, ice, rain)

Other Sources

Radar

Models

Surface Observations

Webcams

Buoys

Charts

Pre-Processing

Indirect Sources

Analyst Derived Output

February 6, 2006

4 km & 24 km Northern Hemisphere Analysis

Snow & Ice Cover Produced daily at U.S. National

Ice Center

GlobSnow AATSR Snow Extent Product

23

• SE product (1 km pixel) is based on: ATSR-2 (1995) & AATSR (2002)

• Hierarchical Cloud Detection Alg. using (A)ATSR Bands only (SCDA developed at SYKE)

• SE Mapping Procedure• Masking of snow free areas (0.67, 1.6 µm) • Fractional Snow Extent:• SCAmod Alg. (0.67µm). (Metsämäki et al, 2005).

Monthly Snow Extent Product (May 2003)

24/5/200824-25/5/2008

24-26/5/200824-29/5/2008

MODIS Fractional Snow Product

24

Thomas Nagler

Fractional Snow Cover (FSC) is estimated by a regression relationship using the NDSI:

FSC = -0.01 + 1.45 * NDSI

where NDSI = (band 4* – band 6*) / (band 4 + band 6)

FSC is calculated for the Normalized Difference Snow Index (range 0 – 1.0):

Quality Maps:Several screens are the applied to a FSC pixel; if a screen is failed then the pixel is set to a “not snow” value and/or a quality assessment (QA) flag is set. Results of the screens are stored in a QA data layer in the product. The purpose of these screens is to alleviate snow commission errors.

Maximum Snow Extent 1-7 April 2010

MOD10_ (~500m)

CryoLand (~500m)

GlobSnow (~1 km)

IMS (~4 km)

Maximum Snow Extent 15-22 May 2010

MOD10_ (~500m)

CryoLand (~500m)

GlobSnow (~1 km)

IMS (~4 km)

CryoLand MODIS Fractional Snow Extent Product

27

Thomas Nagler

Processing:

• Input: MODIS data as input

• Binary snow classification using NDSI with spatially and seasonal variable threshold map

• FSC calculation applies SCAmod algorithm using transmissivity map and predefined reflectance thresholds for forest canopy, bare ground and wet snow

• Covers Pan-European Domain

Documents

Intercomparison of Snow Extent Products from Earth ...€¦ · MODIS NSIDC (Hall et al.) AVHRR Pathfinde r Binary 5 km daily 1992 -2004 AVHRR CCRS (Zhao, et al) CryoLan d Fractiona