1. Accuracy Assessment and Reference data Collection Kamini Yadav Dr. Russ Congalton

2. Review of the Reference data Excel spreadsheet Africa-Jun o Received excel spread sheet o Had first conference call on April 16, 2015 Australia-Pardha o Received excel spread sheet, GCE v.2 along with 1/3 rd Ground data for Validation o Working on Scheduling conference call North America-Richard/Teki o Received excel spread sheet o Working on Scheduling conference call Europe-Aparna/Mutlu o Received excel spread sheet o Working on Scheduling conference call South America-Chandra Not received

3. Ground Data Sources  Ground data (collected by our team including Murali)  Received shape files for Ethiopia, Tanzania, Malawi, Rawanda, Burundi  India (South India, Rajasthan)  Ground data sourced from other projects (e.g., CORINE)  Curt Reynolds's field data from USDA/FAS  2015 corn map for South Africa and 2014 cotton / rice map for Australia GDA Corp  Ground data from literature  Authors will be contacted to access the reference data they used or the map they produced if possible  LUCAS Data (Received photos)  2012: 250,000 locations, 85,500 for validation  2009: 200,000 locations, 66,000 for validation  2006: 150,000 locations, 49,500 for validation  Mixed pixels, Positional error and Independent data for validation  Reference data from other standard work (e.g., USDA CDL, Canada Agri)

4. Reference data from Literature Paper TitleJournalContactData 1 Crop area mapping in West Africa using landscape stratification of MODIS time series and comparison with existing global land products International Journal of Applied Earth Observation and Geoinformation, Volume 14, Issue 1, February 2012, Pages 83–93 elodie.vintrou@ci rad.fr, elodie.vintrou@g mail.com A ground data set collected during the 2009 and 2010 cropping seasons (744 GPS waypoints at the validation sites) 2 Generating plausible crop distribution maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach Agricultural Systems, Volume 99, Issues 2–3, February 2009, Pages 126–140 L.YOU@CGIAR. ORG Crop distribution map of sub Saharan Africa 3 Generating global crop distribution maps: From census to grid Agricultural Systems, Volume 127, May 2014, Pages 53–60 L.YOU@CGIAR. ORG Global Rainfed/Irrigated crop map 4 Disaggregating and mapping crop statistics using hyper temporal remote sensing International Journal of Applied Earth Observation and Geoinformation, Volume 12, Issue 1, February 2010, Pages 36–46 Khan@ITC.nl Wheat, sunflower, Barley crop maps of southern Spain 5 Global rain-fed, irrigated, and paddy croplands (GRIPC) J.Meghan Salmon, Mark A. Friedl, Steve Frolking, Dominik Wisser,Ellen M. Douglas https://dl.dropbox usercontent.com/ u/12683052/GRI PCmap.zip. Irrigated/Rainfed Map 6 Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data International Journal of Remote Sensing Volume 34, Issue 7, 2013 penggong@berke ley.edu Landsat/MODIS Mapping 91,000 Training smaples; 38,000 Test samples

5. Reference data from Literature Paper TitleJournalContactData 7Data Mining, A Promising Tool for Large-Area Cropland Mapping IEEE Journal of selected topics in applied earth observations and remote sensing, vol. 6, no. 5, october 2013 elodie.vintrou @cirad.fr The field surveys were conducted in Mali during the 2009 and 2010 crop seasons (980 Way points) 8GlobeLand30 (http://www.globallandcover.com/GLC30Download/ind ex.aspx) ISPRS Journal of Photogrammetry and Remote Sensing 103 (2015) 7–27 Jun Chen154,587 pixel samples 2010 year 9Mapping and discrimination of soyabean and corn crops using spectrotemporal profiles of vegetation indices International Journal of Remote Sensing, 2015, Vol. 36, No. 7, 1809–1824, carlos_hws@h otmail.com Field data from 19 different croplands (state of Paraná, located in the South of Brazil, between) 10Improving Crop Area Estimation in West Africa Using Multiresolution Satellite Data Proceedings of Global Geospatial Conference 2013 gerald.forkuor @uni- wuerzburg.de field survey conducted between May and July 2012. 11Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines ISPRS Journal of Photogrammetry and Remote Sensing 85 (2013) 102–119 fabian.loew@ uni- wuerzburg.de Extensive field survey conducted in Four test sites in Middle Asia. 12MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets Remote Sensing of Environment 114 (2010) 168–182 friedl@bu.edu1860 Training sites globally 13Cropland for sub-Saharan Africa: A synergistic approach using five land cover data sets Calibrated synergy map for Africa (http://onlinelibrary.wiley.com/doi/10.1029/ 2010GL046213/abstract) fritz@iiasa.ac. at 2553 samples distributed over Africa

6. Way Forward Approach the respective authors or producers who have worked on standard products mapping on small area to get either the ground data or final product along with their accuracy Generate independent reference data from existing cropland layers for 30x30m and 250x250m validation Work on VHRI to build independent reference data (trying to make use of temporal information to label the image segments) Compile the independent ground data coming from different sources (e.g. ICRISAT/GDA or others)

7. Obstacles Challenge for 30 m data as demonstrated above What about MODIS pixels?

8. Field Form 8

9. Thanks