Service S2: Arctic-alpine growing season mapping service · Service S2: Arctic-alpine growing...

Promoting Earth Observation Services

Service S2: Arctic-alpine growing season mapping serviceThe Arctic-Alpine Growing Season service will provide a service for mapping the onset, peak, and end of the growing season in selected areas of the arctic and alpine parts of northernmost Europe.


- changes in the growing season are among the best bio-indicator of changes in

climate in Arctic and alpine areas

- it is the first indication of changes in the vegetation cover

- it is important for the feedback loop to the climate system

- it highly influence the population size of most birds, animals, and insects


Simulated Sentinel-2 data

- Time-series of interpolated cloud-free mosaics of MODIS data

for the period 2000 – 2012

- Time-series of Landsat 8 data from 2013 (2014). Almost daily

data from high arctic areas


Two study sites

1) Svalbard (mask out glaciers and non-

vegetated areas)

2) Northern Fennoscandia (mask

out forests and non- vegetated areas)

Three Sentinel-2 based products:

P2.1 Onset of the growing season

P2.2 Peak of the growing season

P2.3 End of the growing season

Svalbard


Animation


Internal auxiliary data

Field observation of the onset and end of the growing season used to determine NDVI thresholds

Mountain avens ( Dryas octopetala) – Adventdalen, Svalbard 2014

11 July 31 July 10 August

5 September24 August 9 September


Example of onset of the growing season map from Svalbard, based on MODIS-NDVI data

Karlsen et al. 2014. Remote Sensing.6:8088-8106


Example of onset of the growing season map from Svalbard, based on Landsat data


First step in the processing is cloud detection and to give

interpolated values for the cloudy pixels.

However, to develop time-series of cloud free data is done in

Service S4.

Then we calculate NDVI, and mask out areas that will not be

mapped.


P2.1 Onset of the growing season


P2.3 End of the growing season


The experience from V1 of the framework was that the processing time was completely

dwarfed by the time spent staging data into and out of processing nodes.

For V2 of the service, we will therefore simplify the design by computing the onset

reference map and all products in two passes through a complete stack of tiles, all in

one job.


Service S3: Soil frost serviceThe Soil frost service will provide a service for mapping the onset and end of the freezing of soil


Two study sites

1) Svalbard

2) Northern Fennoscandia

Sentinel-1 based products:

• Daily freeze/thaw status

• Onset of frozen soil (DOY)

• End of frozen soil (DOY)


Processing workflow

• Generate stack of geocoded SAR data for hydrological year (Sept1-Aug 30)

• Extract reference data & generate 3D cube ∆σ(x,y,t)

• Generate soil frost maps every day (temporal/spatial filtering)

• Check quality of time series (manual step)

• Export soil frost maps to archive


Detection of soil frostParameters:

FD1,FD2,SD1&SD2

Detect zero crossings

at times: t1 and t2

-annual pattern for ∆σ(t)


Code range

Class Relevant products

0Outside area of

interestALL

20 Sea mask ALL

21 Lake mask ALL

22 River mask SFS

30 Cloud mask SFS

50Bare soil, free of

snowALL

70 Glacier mask ALL

80 Forest mask SFS

81Dense forest

maskSFS

90 Urban area SFS

91 Mountains

240Thawed soil

SFS

241 Frozen soil SFS

Soil frost productOutput products Product coding


Envisat ASAR data (test scenario)

- Time-series of ASAR WMS mode for

the season 2008/2009: 290 scenes

Sentinel-1 data (demonstration)

- Time-series of S1 IW mode data: Oct

2014 –present : 35 scenes

ASAR WSM 20081118_200502

S1A IWH 20141215_155839


2008_DOY=268 2008_DOY=300 2009_DOY=90

2009, DOY=100 2009, DOY=110 2009, DOY=120 2009, DOY=130 2009, DOY=140

2009_DOY=12008_DOY=330


Validation

Users are involved.

Validation dataset

• Temperature sensors (ground)

• Meteorology stations (Air temperature)

• Frost depth measurements (Holt, Gibostad)

• Large scale patterns (Comparison with models SeNorge.no)

Method

Correlation of SF-algorithm and first/last day of soil frost for

sensors

Service S2: Arctic-alpine growing season mapping service · Service S2: Arctic-alpine growing...

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