Virtual constellations, time series, and cloud screening opportunities for Sentinel 2 and Landsat

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1 Virtual constellations, time series, and cloud screening opportunities for Sentinel 2 and Landsat Sentinel 2 for Science Workshop May 2014 ESA ESRIN, Frascati (Rome), Italy 1 Part 1: Title: Towards a satellite virtual constellation for land characterization: Concept and roles for Sentinel 2 and Landsat 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 2/102 1

2 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 4 Year Observing the land (optical) better than 100m* Number flying 1 st year of each decade th th July /52 7 th July th July st July th August 2009 Satellite * Graphic: excluding Helios, Alan Belward, Yaogan, KH, JRC; etc See Belward and Skøien, ISPRS-JPRS 7/102 2

3 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 8/102 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 9/102 3

4 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 10/102 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? 11/102 4

5 ARL described 2 ARL 1. Similar sensors, minimal processing, crosscalibration, 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, nanocube swarm sats, ) Utility applications driven 12/102 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 13/102 5

6 Why is it some important to be able to treat Landsat and Sentinel 2 as interoperable? Some context and examples 14/102 Change in understanding Pixel 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 15 6

7 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) 16 Paradigm Shift Scene based Pixel based Free and open access to the data Standardized, robust image products (L1T) Automated bulk processing tools (LEDAPS, Fmask) Increasing computing capacity Within year Between year R. Kennedy, graphic 17/102 7

8 18/102 A lexicon of pixel based image composites Composite type Annual BAP Typical compositing period Target DOY ± 30 days (for a single year) Typical rule base 1. DOY (relative to a target DOY, i.e., Aug 1) 2. Distance to cloud and cloud shadow 3. Sensor 4. 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 shadow 4. Sensor 5. 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. 19/102 8

9 Areas of NO DATA shown in white BAP annual composite (2003) Aug 1 ± 30 days, /102 Annual composite 2002 August 1 ± 30 days 21/102 9

10 Annual composite 2003 August 1 ± 30 days 22/102 Annual composite 2004 August 1 ± 30 days 23/102 10

11 Areas of NO DATA persist despite using multiple years of imagery BAP multi year composite (target year = 2003) Aug 1 ± 30 days, /102 Proxy value composite 2003 Areas with persistent no data are assigned a synthetic value, which is determined using a trajectory of available values for the pixel. 25/102 11

12 Years Cumulative proportion Less than 1% of pixels have 14+ years of data Proportion of pixels Number of years 52% of pixels have 10+ years of data ALL pixels have at least 3 years of data Observation yield for Newfoundland 26/ Proportion of pixels Number of years Cumulative Years proportion % of pixels have 15 years of data 74% of pixels have 13+ years of data ALL pixels have at least 7 years of data Observation yield for Saskatchewan 27/102 12

13 26/05/2014 Percentage and origin of data gaps in the study area for the target years , 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 (note that water bodies are masked). 28/102 Different phases of image compositing process for the years 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 30/102 13

14 What happens when a disturbed area is captured in different time periods? 31 Different phases of image compositing process for the years 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. Txomin Hermosilla, Nicholas, UBC 32/102 14

15 Multi year BAP ( ) L5 and L7 36/102 Best Available Pixel (BAP) Composite for Saskatchewan 2010 source images 2010 BAP composite Disturbance history /102 15

16 38/102 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 * 39/102 16

17 Disturbed trends Greatest disturbance year Hermosilla, UBC 42/102 Disturbed trends Persistance of disturbance event 43/102 17

18 Disturbed trends Greatest disturbance magnitude 44/102 Disturbed trends Post disturbance slope 50/102 18

19 59/102 Closing the disturbance loop Characterization of post disturbance recovery. Classification (herb to shrub) Regeneration success Biomass uptake Txomin Hermosilla, Nicholas, UBC 60 19

20 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? 61/102 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, /102 20

21 Seven sites located from a variety of landscape and different parts of the world p7r5 North America p45r30 p199r26 Europe Asia p135r25 p196r44 Africa p223r61 South America Australia p92r86 Oceania Antarctica 63/102 Oregon p45r30 Band 5, 4, and 3 composites 64/102 21

22 Thermal Band 65/102 Cirrus Band Cirrus band TOA reflectance: /102 22

23 TM/ETM+ Fmask results Clear Cloud Shadow Snow/Ice Cloud 67/102 Sentinel Fmask results Clear Cloud Shadow Snow/Ice Cloud 68/102 23

24 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% 69/102 Paris p199r26 Band 4, 3, and 2 composites 70/102 24

25 Thermal Band 71/102 Cirrus Band Cirrus band TOA reflectance: /102 25

26 TM/ETM+ Fmask results Clear Cloud Shadow Snow/Ice Cloud 73/102 Sentinel Fmask results Clear Cloud Shadow Snow/Ice Cloud 74/102 26

27 How heritage Landsat Fmask results differ from Sentinel results Sentinel TM/ETM+ Clear Land Clear Water Cloud Shadow Snow/Ice Cloud 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% 75/102 Amazon p233r61 Band 4, 3, and 2 composites 76/102 27

28 Thermal Band 77/102 Cirrus Band Cirrus band TOA reflectance: /102 28

29 TM/ETM+ Fmask results Clear Cloud Shadow Snow/Ice Cloud 79/102 Sentinel Fmask results Clear Cloud Shadow Snow/Ice Cloud 80/102 29

30 How heritage Landsat Fmask results differ from Sentinel results Sentinel TM/ETM+ Clear Land Clear Water Cloud Shadow Snow/Ice Cloud 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% 81/102 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 88/102 30

31 Sentinel Fmask results Paris p199r26 Clear Cloud Shadow Snow/Ice Cloud 89/102 OLI/TIRS Fmask results Clear Cloud Shadow Snow/Ice Cloud 90/102 31

32 Percentage of Fmask difference OLI/TIRS Sentinel Clear 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% 91/102 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: 92/102 32

33 93/102 94/102 33

34 Land surface water ca /102 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. 96/102 34

35 Contact Information: Mike Wulder Publications: Thank you! /80 97/52 97/102 35

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