{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T06:52:43Z","timestamp":1782283963643,"version":"3.54.5"},"reference-count":50,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,10,30]],"date-time":"2020-10-30T00:00:00Z","timestamp":1604016000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2019s agricultural regions, space-based remote sensing is one of the most reliable approaches to get detailed information describing the evolving state of the country\u2019s environment. Agriculture and Agri-Food Canada (AAFC)\u2014the Canadian federal department responsible for agriculture\u2014produces 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\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/rs12213561","type":"journal-article","created":{"date-parts":[[2020,10,30]],"date-time":"2020-10-30T21:34:47Z","timestamp":1604093687000},"page":"3561","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":84,"title":["Application of Google Earth Engine Cloud Computing Platform, Sentinel Imagery, and Neural Networks for Crop Mapping in Canada"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9495-4010","authenticated-orcid":false,"given":"Meisam","family":"Amani","sequence":"first","affiliation":[{"name":"Wood Environment &amp; Infrastructure Solutions, Ottawa, ON K2E7L5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2318-8216","authenticated-orcid":false,"given":"Mohammad","family":"Kakooei","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Babol Noshirvani University of Technology, Babol 4714871167, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0455-4882","authenticated-orcid":false,"given":"Armin","family":"Moghimi","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 1996715433, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8406-683X","authenticated-orcid":false,"given":"Arsalan","family":"Ghorbanian","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 1996715433, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8324-4339","authenticated-orcid":false,"given":"Babak","family":"Ranjgar","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 1996715433, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1670-151X","authenticated-orcid":false,"given":"Sahel","family":"Mahdavi","sequence":"additional","affiliation":[{"name":"Wood Environment &amp; Infrastructure Solutions, Ottawa, ON K2E7L5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Davidson","sequence":"additional","affiliation":[{"name":"Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thierry","family":"Fisette","sequence":"additional","affiliation":[{"name":"Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick","family":"Rollin","sequence":"additional","affiliation":[{"name":"Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8439-362X","authenticated-orcid":false,"given":"Brian","family":"Brisco","sequence":"additional","affiliation":[{"name":"The Canada Center for Mapping and Earth Observation, Ottawa, ON K1S 5K2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3329-5063","authenticated-orcid":false,"given":"Ali","family":"Mohammadzadeh","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 1996715433, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A., and Skakun, S. (2017). Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping. Front. Earth Sci., 5.","DOI":"10.3389\/feart.2017.00017"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"525","DOI":"10.5589\/m03-069","article-title":"The application of C-band polarimetric SAR for agriculture: A review","volume":"30","author":"McNairn","year":"2004","journal-title":"Can. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Amani, M., Mahdavi, S., Afshar, M., Brisco, B., Huang, W., Mohammad Javad Mirzadeh, S., White, L., Banks, S., Montgomery, J., and Hopkinson, C. (2019). 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