1. Accuracies, Errors, and Uncertainties of Global Cropland Products Kamini Yadav, PhD Student Advisor: Dr. Russell Congalton Natural resource & the environment University of New Hampshire

2. Introduction Lack of interoperability, insufficient information on accuracy highlighting strengths and weaknesses in existing land cover maps (Herold et al., 2008) Assessing the map accuracy in an objective manner is fundamental to most mapping projects (Foody 2002; Strahler, 2006) With the advent of advanced digital remote sensing classification, there are currently five major global cropland maps: (1) Thenkabail et al. (2009a, b, 2011), (2) Ramankutty et al. (1998), (3) Goldewijk et al. (2011), (4) Portmann et al. (2009), Siebert and Doll (2009), and (5) Pittman et al. (2010). The solution to improve the monitoring of global croplands lies in mapping them routinely, rapidly, consistently with sufficient accuracy (Congalton and Green 2009) Significant impediments in crop type mapping is the lack of quality training data (Shao et al., 2010) Similarity in spectral reflectance of different crops, variability in field to field reflectance of the same crop, particular combinations of crops (Wheeler and Misra, 1980; Buechel et al., 1989 In practical situations cropping environments exhibit smaller average field extent than the pixel size of the imagery (Wardlow et al., 2007)

3. Overarching Goal The overarching goal of this research is to develop methods and approaches as well as conduct comprehensive evaluation of accuracies, errors, and uncertainties of various global cropland products which are produced using a wide array of remotely sensed and geospatial data. This will be performed based on novel approaches using a large volume of reference data from different reliable sources.

4. Objectives 1.Evaluate and develop methods and approaches of determining accuracies, errors, and uncertainties for global cropland products 2.Organize various distinct global cropland data products (e.g., croplands, irrigation versus rain fed, cropping intensity, crop type), produced at number of different resolutions of remotely sensed data 3.Establish novel global reference data for accuracies, errors, and uncertainties sourced from very high resolution imagery (VHRI), ground or field data, secondary data, and crowd sourced data 4.Measure the accuracy, error and uncertainty of various global cropland products (e.g., croplands, irrigation versus rain fed, cropping intensity, crop type) that are in various resolutions using espoused methods and approaches and the novel reference datasets 5.Accuracy confidence with different spatial and spectral resolution remote sensing data in diverse crop growing environments

5. Review of existing accuracy methods MethodsReferences StrengthsLimitations 1 Looks right or goodDicks &Lo, 1990 Ease of good looking maps Subjective approach 2 Comparison of areal extent of classes in thematic map (non-site specific) Objective approach, correct proportions Locational accuracy ignored, report high accuracy, use same data as used by training classifier, incorrect locations 3 Accuracy metrics based on comparison of class labels and ground data for specific locations (Site specific) Independent datasets, accuracy metrics Includes only percentage of cases correctly allocated 4 Confusion or error matrix (pattern of class allocation relative to reference data) Congalton, 1994; Congalton and Green,1999 Measures of accuracy use information content of confusion matrix fully More scope to extend the analysis 5 Spatial distribution of accuracy, errors and uncertainty Kriging (Steele et al. 1998), Geographically weighted Regression and difference measure (Comber et al., 2012) for Boolean and Fuzzy classes

6. Organize global cropland products ProductsData usedSpatial ResolutionCropland classesOther classes 1 Pittman et al 2010 MODIS 250 m Cropland extent 2 Thenkabail et al 2009 AVHRR 1 Km Cropland, Irrigated and Rain fed dominance, Natural vegetation with minor cropland fractions 3 FAO Aquastat Inventory data Crop Statistical data 4 Portmann et al. 2008 National Sub-National statistical data 10 kmIrrigated and Rain fed 5 Friedl et al. 2010 MODIS 500 m Global Croplands 6 Loveland et al. 2000 AVHRR 1 kmGlobal Croplands 7 Goldewijk et al. 2011 Population, Cropland pasture statistics combined with satellite information 10 km Cropland statistics 8 Siebert & Döll,2009 Growing areas and cropping season Climate and Soil data 10 km Crop production in irrigated and rain fed agriculture 9 Ramankutty et al 2000 Agriculture inventory data and satellite derived land cover data (MODIS & GLC2000) 10 km Global agriculture land 10 Yu et al 2013 Landsat and MODIS 30mCropland extentBare Cropland * Best available current state-of-art Global cropland extent maps

7. Global reference data from very high resolution imagery Segmentation of Very High Resolution Imagery Multi - resolution segmentation Crop/No-Crop Selection of reference image objects Extract Reference Points Object based or Point Sampling Reference locations Classification Geo-physical Parameters Spectral Profile from another year Crop type information on each reference location Testing Compare the quality of reference data with the one collected from ground

8. Accuracy confidence with different types of remote sensing data in diverse crop growing environments Selection of 4-5 sites with different crop growing regions Based on cropping calendar, each site has: Hyperion data, MODIS, Landsat & Ikonos/Quick bird Ground Reference Data Optimum number of acquisition dates and most suitable temporal windows Image Classification Techniques Implementing classification technique based on the number of bands and temporal windows Classification accuracy and confidence Predicting measures of classification accuracy for mapping 8 crop types

9. Perform accuracies, errors, and uncertainties in different continents based on the collected reference data Produce accuracy standards and protocols to perform the accuracy assessment and spatial uncertainty analysis for different cropland products Developing methods and strategies using high quality reference data to achieve best accuracy results in each continent

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