The ability of the Canadian agriculture sector to make better decisions and manage its operations more competitively in the long term is only as good as the information available to inform decision-making. At all levels of Government, a reliable flow of information between scientists, practitioners, policy-makers, and commodity groups is critical for developing and supporting agricultural policies and programs. Given the vastness and complexity of Canada’s agricultural regions, space-based remote sensing is one of the most reliable approaches to get detailed information describing the evolving state of the country’s environment. Agriculture and Agri-Food Canada (AAFC) - the Canadian federal department responsible for agriculture - produces the Annual Space-Based Crop Inventory (ACI) maps for Canada. These maps are valuable operational space-based remote sensing products which cover the agricultural land use and non-agricultural land cover found within Canada’s agricultural extent. Developing and implementing novel methods for improving these products are an ongoing priority of AAFC. Consequently, it is beneficial to implement advanced machine learning and big data processing methods along with open-access satellite imagery to effectively produce accurate ACI maps. In this study, for the first time, the Google Earth Engine (GEE) cloud computing platform was used along with an Artificial Neural Networks (ANN) algorithm and Sentinel-1, -2 images to produce an object-based ACI map for 2018. Furthermore, different limitations of the proposed method were discussed, and several suggestions were provided for future studies. The Overall Accuracy (OA) and Kappa Coefficient (KC) of the final 2018 ACI map using the proposed GEE cloud method were 77% and 0.74, respectively. Moreover, the average Producer Accuracy (PA) and User Accuracy (UA) for the 17 cropland classes were 79% and 77%, respectively. Although these levels of accuracies were slightly lower than those of the AAFC’s ACI map, this study demonstrated that the proposed cloud computing method should be investigated further because it was more efficient in terms of cost, time, computation, and automation.

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Huffman, T., Ogston, R., Fisette, T., Daneshfar, B., Gasser, P-Y., White, L., Maloley, M. and Chenier, R. 2006. Canadian agricultural land-use and land management data for Kyoto reporting. Can. J. Soil Sci. 86: 431-439. The land use and management data requirements for assessing, monitoring and reporting on the impact of agricultural production practices on the environment, especially in a country as large as Canada, are considerable. In view of the fact that environmental assessments are a relatively new phenomenon, data collection activities targeted toward these needs are not widespread. As a result, we find it necessary to acquire and integrate a variety of data sources with differing time lines, spatial scales and sampling frameworks. This paper uses our current activities with respect to Kyoto reporting as a focus to present and discuss the types of data required and the spatial analysis and integration procedures being developed to provide them. The essential data for this activity include the area of crop and land use types, land use changes since 1990, farm and land management practices and biomass production. The spatial framework selected for national analysis is the Soil Landscapes of Canada, and the primary existing data sources are the Census of Agriculture, sample-derived yield estimates and satellite-based land cover products. These are supplemented with detailed, multi-season, multi-year satellite image interpretations conducted at an ecologically and statistically stratified sample of sites across the country. The use of these data in preparing an account of greenhouse gas sources and sinks identified a number of gaps and problems, and a brief outline of future work designed to improve the data inputs is presented.

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