1. U.S. Department of the Interior U.S. Geological Survey Michael Budde, James Verdin, and James Rowland USGS – FEWS NET Sentinel-2 Agriculture User Consultation 25 April, 2012 – Frascati, Italy

2. An activity of the Office of Food for Peace at USAID, which directly supports its goal: “to ensure that appropriate… emergency food aid is provided to the right people in the right places at the right time and in the right way” FEWS NET is a food security decision support system with its own climate services

3. US Geological Survey - EROS University of California, Santa Barbara (UCSB) NOAA CPC, NOHRSC, ESRL NASA GSFC USDA FAS Chemonics International (Prime Contractor) Evidence for Development - Livelihoods Overseas Strategic Consulting - Web Dev. Action Against Hunger - Nutrition CORE PARTNERS Kimetrica NETWORK MEMBERS World Food Program (WFP) Food and Agriculture Organization (FAO) National Government Ministries Price/Market Information Systems Meteorological Centers Non-governmental Organizations (NGOs) Regional Institutions (SADC, CILSS, COMESA, IGAD, ASEAN) Other UN agencies (UNICEF, UNHCR)

4. The Purpose of FEWS NET To prevent famine and mitigate food insecurity by providing decision makers with information that is accurate, credible, timely, and actionable. To strengthen the ability of FEWS NET countries and regional organizations to provide timely early warning and vulnerability analysis. The Purpose of FEWS NET To prevent famine and mitigate food insecurity by providing decision makers with information that is accurate, credible, timely, and actionable. To strengthen the ability of FEWS NET countries and regional organizations to provide timely early warning and vulnerability analysis.

5. FEWS NET Early Warning Analysis Convergence of evidence Livelihoods based analysis Scenario building Comparable classification of food security outcomes Effective decision support Convergence of Evidence Remote sensing Rainfall Forecasts Market/Price monitoringField Assessments Nutrition Surveys Conflict

6. FEWS NET Early Warning Analysis Convergence of evidence Livelihoods based analysis Scenario building Comparable classification of food security outcomes Effective decision support Consider evidence in the context of local livelihoods

7. FEWS NET Early Warning Analysis Convergence of evidence Livelihoods based analysis Scenario building Comparable classification of food security outcomes Effective decision support Scenario Building Current FS status Other Info: Trade, Conflict, Health… Climate Forecasts Livelihoods Seasonal Calendars National/Regional Food Security Outcomes

8. FEWS NET Early Warning Analysis Convergence of evidence Livelihoods based analysis Scenario building Comparable classification of food security outcomes Effective decision support Comparable Classification East Africa Estimated food security Outcomes, Sept 2011

9. FEWS NET Early Warning Analysis Convergence of evidence Livelihoods based analysis Scenario building Comparable classification of food security outcomes Effective decision support Effective Decision Support

10. Livelihood systems are based on subsistence agriculture and/or pastoralism, and are highly climate-sensitive Conventional climate station networks are sparse and often late reporting or don’t report at all Satellite remote sensing and modeling fill the gap, and provide the basis for early detection of agricultural drought Rainfall, vegetation, snow pack, actual ET, crop water requirements are monitored for both rainfed and irrigated crop lands as well as pastoral areas Observations are focused by region and seasonality based on crop calendars

11. Operational Remote Sensing Modeled Derivatives NDVI RFE LST

12. http://earlywarning.usgs.gov/fews Africa - 60+ products Afghanistan - 25+ products S. Central Asia - 8 products S. Asia - 5 products L. America / Caribbean - 12+ products Global - 5 products USGS FEWS NET Data

13. Regional interactive map viewersEarly Warning Explorer (EWX ) RFE AnomalyMODIS LSTeMODIS NDVIeMODIS Anom Time series of eMODIS NDVI, cumulative RFE, and Dekadal RFE  User interface to dynamically compare standardized anomalies  Framework to incorporate NASA data into routine analysis  Multi-level time series analysis  Ability to analyze analog years  Time series data download

14. eMODIS (EROS MODIS) background Developed by the LP DAAC (USGS-EOS) at EROS in 2007 Funded by vegetation monitoring stakeholders Expedited and Historical products for Terra and Aqua MODIS CONUS, Alaska, C. America/Caribbean, Africa, and Central Asia eMODIS Provides Ability to customize compositing periods Consistent and timely processing (Expedited within 12 hours and Historical 3-5 days) Minimized Re-sampling Improved spatial resolution (i.e. 250m)

15. LANCE is an Earth Observing System Data and Information System (EOSDIS) and provides near real-time capabilities for applications, operational agencies, and researchers. The LANCE system supports eMODIS expedited processing through the provision of timely surface reflectance data. Expedited LANCE Level-2 Surface Reflectance Final eMODIS product delivered within 12 hours of acquisition Historical LAADS Level-2 Surface Reflectance (ephemeris & atmosphere) Final eMODIS product delivered within 3-5 days of acquisition

16. eMODIS System USGS/EROS Terra MODIS LANCE USGS FEWS NET Processing System (stack, smooth, create products, develop time series, package data for distribution) EDOS MODIS L0 Data T+3hrs T+9hrs Composite +18 hrs FEWS NET Decision Support System (USAID FFP) MODIS L2 Data FEWS NET eMODIS NDVI and Anomaly Maps Input to Impact Assessment s and Food Security Decision Making

17. Extension of Existing Waterhole Monitoring Active for 41 waterholes in Kenya / Ethiopia Website provides daily information on the status of a waterhole relative to the median and previous year High resolution multi-spectral imagery needed for waterhole identification/delineation Validation with field data captures trends and seasonal variation successfully

18. Targeted Rangeland / Cropland Monitoring Delineation of known dry-season grazing areas and water-points. Identification of robust indicators and analysis Refined mapping of migratory patterns. Identification of key livestock markets & trade flows. Potential constraints i.e human/wildlife conflicts, animal diseases and Govt. policies (X-border). Continuous monitoring of rangeland resources; focusing on critical period(s) of livestock migration to “dry-season” grazing areas. Focused Cropland Assessments

19. Irrigated Area Mapping / Delineation False Color Landsat Image : May 27, 2004 Irrigated Areas 2004 Using peak NDVI threshold Helmand Province, Afghanistan Utilizes high resolution imagery in combination with 250m MODIS NDVI to annually map irrigated areas in Afghanistan. Provides knowledge of expanding or abandoned areas under irrigation. Allows for more accurate monitoring of irrigated agriculture conditions.

20. Cropped Area Estimation High-resolution imagery allows us to “visit” a large number of locations with no access restriction Imagery is interpreted as crop, no-crop, and other using a dot grid approach Dots are attributed with external data – geophysical and demographic Statistical models are built - generalized additive mixed model Models predict cropped area for locations where we do not have imagery Produce country-level estimate of cropped area Cropped area is a critical component of estimating food production in the developing world and is often inaccurately reported. Independent cropped area estimates provide an unbiased assessment. The Need The Approach High resolution image (~1 M) for Haiti with a 500 M dot grid overlain (yellow dots)

21. Near real-time processing capabilities (e.g. LANCE system for MODIS surface reflectance) - expedited data delivery is key to being applicable as real-time monitoring tools Ability to streamline data delivery process - similar to the Web- enabled Landsat data (WELD) Project Sentinel-2 data available at no cost – even low cost creates a bottleneck in the ability to utilize the frequent revisit time for early warning applications Interoperability with Landsat 8 – the ability to leverage observations from both Sentinel-2 and Landsat 8 to create comparable products at even better frequency than either sensor can offer is a true benefit to the earth observing community

22. The need for a “closer look” – as FEWS NET, we need to assess broad-scale food security issues, but are increasingly requested to focus our attention at finer scales - an example from S.Somalia Worst Drought in 60 Years The worst drought in 60 years, on the heels of two failed rainy seasons, had devastating impacts on the eastern Horn of Africa in 2011 and led to a declaration of famine in southern Somalia. The drought conditions coupled with conflict in Somalia created uncertainty in the state of the agricultural system going into the short rainy season (Oct – Dec, 2011) 2010/11 rainfall compared to historical totals since 1950/51 in select pastoral areas of Kenya and Ethiopia

23. Together with the Food Security and Nutrition Analysis Unit (FSNAU), FEWS NET monitored the short rainy season and potential crop conditions for specific irrigated and rainfed areas on a frequent basis, updating time series every 5 days based on eMODIS NDVI. Extremely good rains throughout the season led to improvements in pastoral conditions and cropped areas appeared to perform remarkably well (time series below). However, there was still concern over whether traditionally cropped areas were actually planted due to the likelihood of outmigration resulting from conflict. Sentinel-2 observations with their frequent revisit schedule, fine resolution, and applicable spectral characteristics would have been suitable to provide that “closer look”.

24. Thank you