sentinel 2 for science workshop - esa...

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Virtual constellations, time series, and cloud screening opportunities

for Sentinel‐2 and Landsat

Sentinel‐2 for Science Workshop

20‐22 May 2014

ESA‐ESRIN, Frascati (Rome), Italy

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• Part 1: – Title: Towards a satellite virtual constellation for land characterization: Concept and roles for Sentinel‐2 andLandsat

– Authors: Mike Wulder, Joanne White, Thomas Hilker, Nicholas Coops, Patrick Griffiths, Dirk Pflugmacher, Patrick Hostert, Jeff Masek

• Part 2: – Title: Opportunities for combining Landsat and Sentinel‐2 in time series analysis for monitoring environmental change

– Authors: Curtis Woodcock (sends regrets), Zhe Zhu, Shixiong Wang

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Context and needs:• Context:

– Inventory, monitoring, climate, deforestation

– Synoptic and comprehensive data sets

– National and international reporting

– UN FAO, GEO, GFOI, REDD+, via ECV, EBV…

• Desired outcomes: – Surface reflectance

– Land cover, Land cover change, events, types, …

– Forest structure

‐‐ All at multiple time periods in a scientifically robust, transparent, and repeatable manner

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7/52Observing the land (optical) better than 100m*

25th July 1973

7th July 1991

11th July 2001

21st July 2005

9th August 2009

25th July 1973

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Number flying 1st year of each decade

Satellite

Year

* excluding Helios, Yaogan, KH, etc…Graphic: Alan Belward, JRC; See Belward and Skøien, 2014. ISPRS-JPRS

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So, there are lots of satellites…

From a user perspective: • Require a better understanding, framework, for utilizing and incorporating differing satellite measures

• Need free and open access, archive all data, seamless automatable download

• Consistent and transparent processing, “analysis ready” products

• Robust calibration and georadiometric characteristics

• Simplifying strategies to broaden user base

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Virtual Constellation

• The Committee on Earth Observation Satellites (CEOS) defines a virtual satellite constellation broadly as a “set of space and ground segment capabilities that operate in a coordinated manner to meet a combined and common set of Earth Observation requirements”.

• Thematic focus, i.e., oceans, atmosphere, land

http://www.ceos.org/index.php?option=com_content&view=article&id=275

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VC for land

• Building upon the CEOS definition

– CEOS, space agency focus

– Revisit with an applications focus

• Challenges:

– differences in bandwidth, number / location of bands, orbital considerations, signal to noise ratio, access, analysis readiness, etc.

• Interoperability is key

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Application Readiness Level (ARL)• Levels of interoperability

• Thematic applications focused VCs are driven by the need of the application and outcome information drivers

– Reporting, science, management, etc.

• Examples focus on land VC related to land cover, land cover change, and vegetation structure

• How similar are measures, can the measures be treat as “the same”, are there calibration approaches, or is the data unique but informative?

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ARL described …2

• ARL‐1. Similar sensors, minimal processing, cross‐calibration, spatial and spectral agreement, surface reflectance (Landsats, Sentinel‐2)– As available

• ARL‐2. Compatible, but fundamentally different spatial / spectral characteristics; different calibration characteristics (national and commercial satellites)– On demand opportunities

• ARL‐3. Auxiliary, not interoperable. Unique information captured (spatially or spectrally); allow for modeling, integration (e.g., radar, lidar, nano‐cube‐swarm‐sats, …)– Utility applications driven

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At ARL‐1, what is possible?

• Landsat 5/7 provide example of what can be done with two well calibrated instruments

• Sentinel‐2: USGS, NASA / ESA efforts on‐going (Masek et al., Vermote et al.)

• Surface reflectance

– Physical value

– Variable for integration

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• Why is it some important to be able to treat Landsat and Sentinel‐2 as interoperable?

• Some context and examples

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Change in understandingPixel based approaches

• Think of pixels rather than images• Set criteria, then use best available pixel

– Merging databases with image processing• Orthorectification, Geometric matching• Radiometric calibration• Cloud and shadow screening• Assign pixels with quality flags• (Griffiths / Hostert, HU, talk forthcoming)

• Large projects have had emphasis reversed– Change first, cover later

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Time series notes

• Increased knowledge on the nature of changes:– Magnitude, persistence, type

– Trends not only maps

• Labelling of change– Attribution of change important for inferring process,

impact (e.g., deforestation vs harvesting)

– Transitions informative on cover, succession

• Temporal information explanatory of structural development (disturbance through to regeneration, recovery; biomass, volume)

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1988 2012

• Free and open access to the data• Standardized, robust image products (L1T)• Automated bulk processing tools (LEDAPS, Fmask)• Increasing computing capacity

Scene‐based Pixel‐based

Paradigm Shift

Within year Between year

R. Kennedy, graphic

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Composite typeTypical compositing

periodTypical rule‐base

Annual BAP Target DOY ± 30 days (for a single year)

1. DOY (relative to a target DOY, i.e., Aug 1)

2. Distance to cloud and cloud shadow3. Sensor4. Atmospheric opacity

Multi‐year BAP For a given target year and target DOY ± 30 days(± 1 or 2 years )

1. Year (relative to a target year)2. DOY (relative to a target DOY, i.e.,

Aug 1)3. Distance to cloud and cloud shadow4. Sensor5. Atmospheric opacity

Proxy value composite

Same as per annual BAP Areas of no data or anomalous values are assigned a proxy value by examining a temporal trajectory of pixel values at the same or neighbouring pixel locations.

A lexicon of pixel‐based image composites

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BAP annual composite (2003)Aug 1 ± 30 days, 2003

Areas of NO DATA shown in white

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2002

August 1 ± 30 days

Annual composite

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2003

August 1 ± 30 days

Annual composite

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2004

August 1 ± 30 days

Annual composite

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BAP multi‐year composite (target year = 2003)

Aug 1 ± 30 days, 2002–2004

Areas of NO DATA persist despite using multiple years of imagery

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2003Proxy value composite

Areas with persistent no data are assigned a synthetic value, which is determined using a trajectory of available values for the pixel.

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• Less than 1% of pixels have 14+ years of data

• 52% of pixels have 10+ years of data

• ALL pixels have at least 3 years of data

YearsCumulative proportion

15 0.12

14 0.92

13 6.19

12 16.50

11 32.57

10 52.37

9 71.26

8 85.49

7 94.14

6 98.30

5 99.65

4 99.96

3 100.00

2 100.00

1 100.00

0

20

40

60

80

100

120

15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

Proportion of pixels

Number of years Observation yield for Newfoundland

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• 29% of pixels have 15 years of data

• 74% of pixels have 13+ years of data

• ALL pixels have at least 7 years of data

YearsCumulative proportion

15 28.66

14 54.91

13 74.43

12 87.36

11 94.97

10 98.56

9 99.74

8 99.98

7 100.00

6 100.00

5 100.00

4 100.00

3 100.00

2 100.00

1 100.00

0

20

40

60

80

100

120

15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

Proportion of pixels

Number of years

Observation yield for Saskatchewan

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Percentage and origin of data gaps in the study area for the target years 2000‐2010, and (b) cumulative histogram and (c) spatial distribution of both pixel‐scoring and noise‐detection data gaps along the study area for the whole time series 1998‐2012 (note that water bodies are masked).

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Different phases of image compositing process for the years 2004‐2007. First and second lines respectively show the data gaps resulting from the BAP scoring (included buffered clouds, shadows and haze, e.g., 2006) and noise removal processes (including residual clouds or smoke, e.g., 2004). Third line shows the final image composites infilled with the proxy values.

Txomin Hermosilla, Nicholas, UBC

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• What happens when a disturbed area is captured in different time periods?

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Different phases of image compositing process for the years 2000‐2003. First lines show the data gaps resulting from both BAP scoring and noise removal processes. Second line shows the disturbances detected after the analysis of the pixel series (temporal domain), and after performing the contextual analysis (spatial domain). Third line shows the final image composites infilled with the proxy values.


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Multi‐year BAP (2009‐2011)L5 and L7

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Best‐Available Pixel (BAP) Composite for Saskatchewan

2010 source images 2010 BAP composite Disturbance history 2000‐2010

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Trend descriptors for the 3 indices (NBR, TCA and TCG):

• Trend type• RMSE• Trend magnitude difference• Trend slope

• Greatest disturbance year• Greatest disturbance duration• Greatest disturbance magnitude

• Pre and post greatest disturbance magnitude• Pre and post greatest disturbance duration• Pre and post greatest disturbance slope

• Pre and post greatest disturbance monotonic segment magnitude• Pre and post greatest disturbance monotonic segment duration• Pre and post greatest disturbance monotonic segment slope

• Disturbances after and before the greatest disturbance

* Metrics suitable as products or model inputs *

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Disturbed trends

Greatest disturbance year

Hermosilla, UBC

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Disturbed trends

Persistance of disturbance event

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Disturbed trends

Greatest disturbance magnitude

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Disturbed trends

Post disturbance slope

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Closing the disturbance loop

• Characterization of post‐disturbance recovery.

– Classification (herb to shrub)

– Regeneration success

– Biomass uptake


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Cloud screening

• Critical to automated, rule‐based, compositing

• Fmask, developed by Zhe Zhu, Woodcock, BU– https://code.google.com/p/fmask/

• Based upon spectral (optical and thermal), spatial (objects), and angular information.– Masks cloud, shadow, water, land, snow/ice

• So, what can we expect from S2 for cloud screening?

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Comparison of the Sentinel 2 observing scenario with legacy Landsat for cloud and cloud shadow

detection

• Work done by Zhe Zhu, Shixiong Wang, and Curtis Woodcock, Boston University

• S2, 1375 nm for cirrus detection

• optical + thermal vs optical + OLI cirrus

Graphic: Drusch et al. RSE, 2012

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p45r30

p223r61

p7r5

p196r44

p135r25

p92r86

p199r26AsiaAsia

Antarct icaAntarct ica

Afr icaAfr ica

EuropeEuropeNorthNorth

Amer icaAmer ica

SouthSouth

Amer icaAmer icaAust ral iaAust ral ia

OceaniaOceania

Seven sites located from a variety of landscape and different parts of the world

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Oregon p45r30Band 5, 4, and 3 composites

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Thermal Band

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Cirrus Band

Cirrus band TOA reflectance: 0‐0.01 0.01‐0.03 0.03‐0.04 0.04‐1

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“TM/ETM+” Fmask results

Clear Cloud Shadow Snow/Ice Cloud

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“Sentinel” Fmask results


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How heritage Landsat Fmask results differ from “Sentinel” results

Sentinel

TM/ETM+Clear Land Clear Water Cloud Shadow Snow/Ice Cloud

Clear Land 66.00% 0.00% 1.23% 0.06% 15.61%

Clear Water 0.00% 1.25% 0.02% 0.00% 0.10%

Cloud Shadow 1.20% 0.01% 0.86% 0.38% 0.54%

Snow/Ice 0.00% 0.00% 0.01% 1.63% 0.01%

Cloud 2.16% 0.01% 0.56% 1.64% 6.74%

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Paris p199r26Band 4, 3, and 2 composites

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Thermal Band

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Cirrus Band


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Sentinel


Clear Land 25.00% 0.00% 6.11% 0.00% 36.06%

Clear Water 0.00% 0.03% 0.00% 0.00% 0.00%

Cloud Shadow 0.33% 0.00% 0.61% 0.00% 4.03%

Snow/Ice 0.00% 0.00% 0.00% 0.00% 0.00%

Cloud 0.17% 0.00% 0.04% 0.00% 27.61%

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Amazon p233r61Band 4, 3, and 2 composites

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Thermal Band

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Cirrus Band


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Sentinel


Clear Land 45.13% 0.00% 2.48% 0.00% 28.35%

Clear Water 0.00% 0.84% 0.04% 0.00% 0.11%

Cloud Shadow 0.14% 0.01% 1.17% 0.00% 1.88%

Snow/Ice 0.00% 0.00% 0.00% 0.00% 0.00%

Cloud 0.00% 0.00% 0.01% 0.00% 19.84%

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Section conclusions

• Based on a limited sample, the “Sentinel 2” observing scenario (optical + a cirrus band) is superior to the legacy Landsat observing scenario (optical plus thermal) for finding clouds and cloud shadows

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Paris p199r26

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“OLI/TIRS” Fmask results


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Percentage of Fmask difference

OLI/TIRS

SentinelClear Land Clear Water Cloud Shadow Snow/Ice Cloud

Clear Land 39.15% 0.00% 0.59% 0.00% 5.54%

Clear Water 0.00% 0.83% 0.00% 0.00% 0.01%

Cloud Shadow 0.77% 0.03% 1.80% 0.00% 1.10%

Snow/Ice 0.00% 0.00% 0.00% 0.00% 0.00%

Cloud 1.11% 0.08% 0.20% 0.00% 48.79%

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Dense time series derived water mask product

• Each image that we ingest for processing has a mask created that indicates: clear land pixel, cloud, shadow, water, snow/ice (fmask).

• Using this mask, we can overlay all pixels and interrogate by these classes, such as presence of water. All images are eligible, not just the images used in final composites (many images per year and between years)

• A fine resolution water mask is the output• created a circa 2010 water bodies map for Canada• It will be of the greatest spatial detail generated to‐date. Binary maps and likelihood maps are envisioned.

• Preliminary results follow:

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Land surface water ca. 2010

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Messages

• Virtual constellations:– Role for space agencies; opportunities for commercial programs– Land constellation and satellites focused on information needs

• Integration, ARL‐1, reflectance– Ideally S2 / Landsat can be integrated seamlessly– Landsat could effectively become a shared historical archive for S2– Temporal density of imagery, increased revisit offered by S2 will

support rule based compositing, as well as, unique attributes (e.g., automated lake mapping)

• Promise for S2 in cloud screening demonstrated• Not just about clouds,

– Inter‐year – change, state– Intra‐year – phenology, compound evidence

• Free and open access critical. Global coverage. Analysis ready, automate‐able into processes. Ready to use.

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Thank you!Contact Information:

Mike [email protected]

Publications:

http://cfs.nrcan.gc.ca/publications/authors/read/11091

sentinel 2 for science workshop - esa...

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