1. Accuracy Assessment: Building Global Cropland Reference Data Updates for March 2015 Kamini Yadav and Russ Congalton

2. Continue to Work with Jim Tilton Received Hseg results for the Yolo County, California after updates in Prune version Intend to use segmentation output for post processing and object labeling, had some issues with converting output in vector format Jim Tilton helped out with some coding to resolve the issue Currently working on creating output for hierarchical segmentation vector file

3. Work with Terry and Barry Russ was able to meet with Terry and Barry during recent trip to USGS Reston – Discussed Terry providing independent image reference data samples to accuracy assessment team Currently purchasing large hard drive to facilitate this – Met briefly with Barry and discussed other sources of ag reference data Will continue to coordinate these efforts

4. Need to Determine: Global Cropland Reference Data Requirements from each Group The reference datasets have different characteristics and user requirements which determines the re-usability of global cropland datasets It is impractical to visit all the samples on the ground, therefore satellite images with temporal coverage closer to the target map are used to determine cropland characteristics over large geographic areas or continents The characteristics of reference datasets are: Data source, Temporal Coverage, Quality or Confidence, Verification (internal or external) and the Crop Attributes such as Crop/No-crop, Crop types, Irrigation/Rain-fed or Crop intensity Information on the interpretation confidence can be used for analyzing spatial variation in the accuracy of reference data

5. The Accuracy Assessment Team Will: Schedule a meeting/call with each mapping team Provide each mapping team with an Excel spreadsheet to document their reference data Work with each mapping team to help them with their own internal validation Work with each team to aid them in procedures for collecting any ground reference data Work with each mapping team to insure that the accuracy assessment team has sufficient independent reference data to conduct a final accuracy assessment

6. Requirements and Specific Criteria on Global Cropland Reference (GCR) Datasets Reference Data Source of Information Data Source Sample unit/Size Scale (Global/Regio nal) Spatial Resolution Temporal coverage/Date of Collection Crop Characteristics Suitability Quality Flag Verification Crop.No -Crop Crop Type Irrigation/ Rain-fed Crop Intensity A Existing Datasets 1 Statistical DataFAOSTAT 2 Geo-tagged photos/ Degree Confluence Perger et al., 2014 3 Volunteered dataFoody et al., 2013 4 Geo-Wiki/ Crowd Sourced Fritz et al., 2009; Fritz et al., 2011a, See et al., 2014 5 Boston/GOFC- GOLD Olofsson et al., 2012 6 Cropland Probability Map MARS-JRC Existing Land Cover/Cropland Products 7 IGBP-DIScoverScepan et al., 1999 8 GLC 2000 Mayaux et al., 2006 9 Globcover 2005/2009 Defourny et al., 2011b; Fritz et al., 2011a, Bontemps et al., 2011a 10 LC-CCIAchard et al., 2011 11 GLCNMO Tateishi et al., 2011 12 Other Sources.. B New Reference Data Collection 13 Very High Resolution Images NGA/WARP 14 Ground Observation Data

7. Detailed User Criteria/Requirements Data Source: The reference data made use of satellite images (coarse to medium resolution), open source maps, geo-tagged photos and regional maps Sample Unit/Size: The size of the sample collected ( for e.g. number of ground observations) and sample unit type and size (for e.g. Pixel, Block of Pixel/Area or Polygon). Scale: The scale of data collection/mapping such as Global/Regional Spatial Resolution: The resolution of the existing mapping products Temporal Coverage/Data Acquisition Date/ Date of Collection: The time period or the year for which the data has been collected Crop Characteristics Suitability: The suitability of the attribute information whether it proves useful to fulfil the crop/No-Crop, Crop Type/Irrigation or Rain-fed/Crop Intensity requirements Quality Flag: Information about the interpretation confidence and Verification when datasets are visually interpreted to know variability and bias. Important while re-using the datasets Verification: Internal or External Verification done or to be done. Crowdsourced or volunteered data requires such verification.

8. Reference Data Efforts Started working on selecting sample locations for very high resolution imagery frames in Africa The random samples has been chosen based on cropland probability map (IIASA-IFPRI cropland percentage map) For Asia, intend to use 26 high resolution imagery frames as samples received from Prasad to build reference data

9. Independent Reference Data Locations in Africa Basic Information: Global Cropland Probability Map and Ag-Eco zones Random spatially balanced samples (50) has been generated for the cropland regions The sample VHRI frames will help to generate Crop/No-Crop reference data

10. Independent Reference Data Locations in Asia Basic Information: Already have 26 sample very high resolution frames in Central Asia, India, China and Ghana These frames fall in different cropping and climate regions Likewise, CDL in North America, the generated reference data will be validated based on the existing ground observations