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This paper describes the method used to derive 30 meter resolution 2011 US cultivated data sets based on multi-year National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) data. This paper presents different sets of rules (models) to build the cultivated data sets, and a comparison of the resulting cultivated data set accuracies to the accuracies of the original CDL input data. Nine models to create 2011 cultivated data sets for nine US states are tested. Each model provides a set of rules for merging pixels of multi-year (2007-2011) CDL data. The cultivated data accuracy was assessed against in situ 2011 Farm Service Agency (FSA) Common Land Unit (CLU) data. It was found that accuracies were close among the cultivated data generated using the different models. The strongest models for all states achieved overall (producer and user) accuracies greater than 94% for cultivated and non cultivated categories.Index Terms-cultivated data layer, CDL, land cover, crop mask, multi-year cultivated data layer.

This paper, for the first time, proposes to apply USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) geospatial data for stratifying U.S. agricultural land. A new automated method is proposed to stratify the NASS state level area sampling frames (ASFs) by automatically calculating percent cultivation at the primary sampling unit (PSU) level based on the CDL data. The NASS CDLs are 30-56.0 m raster-formatted, georeferenced, cropland cover classifications derived from satellite data. The CDL stratification experiment was successfully conducted for Oklahoma, Ohio, Virginia, Georgia, and Arizona. The stratification accuracies of the traditional (visual interpretation) and new automated CDL stratification methods were compared based on 2010 June Area Survey data. Experimental results indicated that the CDL stratification method achieved higher accuracies in the intensively cropped areas, while the traditional method achieved higher accuracies in low or nonagricultural areas. The differences in the accuracies were statistically significant at a 95% confidence level. It was found that using multiyear composite, CDL-based cultivated layers did not improve stratification accuracies as compared to the results of single-year CDL data. Two applications of the CDL-automated stratification method in official USDA NASS operations are described. The novelty of the proposed method was using geospatial CDL data to objectively and automatically compute percent cultivation of the ASF PSUs as compared to the traditional method that subjectively determines percent cultivation using visual interpretation of satellite data. This proposed new CDL-based process improved efficiency, objectivity, and accuracy as compared to the traditional stratification method.Index Terms-Area sampling frame (ASF), automated stratification, cropland data layer (CDL), cultivated data layer, land cover-based stratification.

Information on future crop specific planting is valuable for improving agricultural survey estimates. This information is critical for agricultural production planning, agricultural product commodity inventory control, natural resource allocation and conservation, etc. However, future crop planting details are generally unavailable. This paper proposes to use crop specific planting frequency data as indicators to indirectly provide information regarding future crop planting. A methodology to derive crop planting frequency data layers based on 2008-2013 Cropland Data Layers has been presented in this paper. Crop frequency layers for corn, soybeans, wheat and cotton were successfully built at the national level and for two states including Indiana and Mississippi, USA. Multi-year (2008-2013) Farm Service Agency (FSA) Common Land Unit (CLU) data were utilized to assess the accuracy of the derived crop frequency data layers. The accuracies of the national scale crop frequency data layers are 91.00%, 90.13%, 87.67% and 85.96% for corn, cotton, soybean and wheat respectively.

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