Abstract
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.
Highlights
Knowledge of the location, extent, and type of croplands are important for food security systems, poverty reduction, and water resource management [1]
Three zoomed regions were explored and illustrated along with the corresponding high-resolution images available in ArcGIS to present the visual accuracy of the produced Annual Space-Based Crop Inventory (ACI) map
A vast portion of Canada is covered by different types of croplands, advanced machine learning and big geo data processing methods have not been investigated for producing ACI maps
Summary
Extent, and type of croplands are important for food security systems, poverty reduction, and water resource management [1]. The study sites of the Group on Earth Observation Global Agricultural Monitoring (GEOGLAM) Joint Experiments for Crop Assessment and Monitoring (JECAM) are no larger than 25 × 25 km2 Such studies have large value for demonstrating the capability of new methodologies and algorithms for crop mapping purposes, their ability to be scaled for operational application over much larger regions often remains unclear. In this regard, one of the important issues to be considered is processing big geo data (e.g., thousands of satellite images, often comprising tens of terabytes of data) over a large area in the classification procedure [3]. Several platforms, such as Google Earth Engine (GEE), have been developed to effectively process big geo data and produce large-scale maps
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