Application of Google Earth Engine Cloud Computing Platform, Sentinel Imagery, and Neural Networks for Crop Mapping in Canada

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.

[1]  Ali Mohammadzadeh,et al.  A Novel Radiometric Control Set Sample Selection Strategy for Relative Radiometric Normalization of Multitemporal Satellite Images , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Saeid Parsian,et al.  Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  Mahdi Hasanlou,et al.  Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples , 2020, ISPRS Journal of Photogrammetry and Remote Sensing.

[4]  Yi Ma,et al.  Hyperspectral Classification Using Deep Belief Networks Based on Conjugate Gradient Update and Pixel-Centric Spectral Block Features , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[5]  Mahdi Hasanlou,et al.  A New End-to-End Multi-Dimensional CNN Framework for Land Cover/Land Use Change Detection in Multi-Source Remote Sensing Datasets , 2020, Remote. Sens..

[6]  Sahel Mahdavi,et al.  Supervised wetland classification using high spatial resolution optical, SAR, and LiDAR imagery , 2020 .

[7]  Qiuyan Yu,et al.  Toward Operational Mapping of Woody Canopy Cover in Tropical Savannas Using Google Earth Engine , 2020, Frontiers in Environmental Science.

[8]  Weimin Huang,et al.  A generalized supervised classification scheme to produce provincial wetland inventory maps: an application of Google Earth Engine for big geo data processing , 2019, Big Earth Data.

[9]  Mohsen Azadbakht,et al.  Automatic canola mapping using time series of sentinel 2 images , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[10]  Yanhua Xie,et al.  Mapping irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[11]  Herman Eerens,et al.  Sub-Pixel Crop Type Classification Using PROBA-V 100 m NDVI Time Series and Reference Data from Sentinel-2 Classifications , 2019, Remote. Sens..

[12]  Tao Zhou,et al.  Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region , 2019, Sensors.

[13]  Weimin Huang,et al.  Canadian Wetland Inventory using Google Earth Engine: The First Map and Preliminary Results , 2019, Remote. Sens..

[14]  Jia Liu,et al.  Crop Classification Based on a Novel Feature Filtering and Enhancement Method , 2019, Remote. Sens..

[15]  R. Congalton,et al.  Integrating cloud-based workflows in continental-scale cropland extent classification , 2018, Remote Sensing of Environment.

[16]  Mewa Singh,et al.  Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery , 2018, Remote Sensing of Environment.

[17]  Ali Mohammadzadeh,et al.  An unsupervised feature extraction method based on band correlation clustering for hyperspectral image classification using limited training samples , 2018, Remote Sensing Letters.

[18]  Kristof Van Tricht,et al.  Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium , 2018, Remote. Sens..

[19]  Yi-Chun Du,et al.  Levenberg-Marquardt Neural Network Algorithm for Degree of Arteriovenous Fistula Stenosis Classification Using a Dual Optical Photoplethysmography Sensor , 2018, Sensors.

[20]  Ioannis Papoutsis,et al.  Scalable Parcel-Based Crop Identification Scheme Using Sentinel-2 Data Time-Series for the Monitoring of the Common Agricultural Policy , 2018, Remote. Sens..

[21]  Jiali Shang,et al.  Contribution of Minimum Noise Fraction Transformation of Multi-temporal RADARSAT-2 Polarimetric SAR Data to Cropland Classification , 2018 .

[22]  Timothy A. Warner,et al.  Implementation of machine-learning classification in remote sensing: an applied review , 2018 .

[23]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[24]  Alexandre Bouvet,et al.  Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications , 2017 .

[25]  S. Süsstrunk,et al.  Superpixels and Polygons Using Simple Non-iterative Clustering , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  R. Congalton,et al.  Automated cropland mapping of continental Africa using Google Earth Engine cloud computing , 2017 .

[27]  Alexei Novikov,et al.  Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping , 2017, Front. Earth Sci..

[28]  R. Prasad,et al.  Artificial neural network for crop classification using C-band RISAT-1 satellite datasets , 2016, Russian Agricultural Sciences.

[29]  J. Shang,et al.  Assessing the Impact of Climate Variability on Cropland Productivity in the Canadian Prairies Using Time Series MODIS FAPAR , 2016, Remote. Sens..

[30]  Bing Zhang,et al.  Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data , 2015, Remote. Sens..

[31]  J. Kovacs,et al.  Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data , 2014 .

[32]  Heather McNairn,et al.  Towards operational radar-only crop type classification: comparison of a traditional decision tree with a random forest classifier , 2012 .

[33]  Heather McNairn,et al.  Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories , 2009 .

[34]  Heather McNairn,et al.  Exploiting spectral variation from crop phenology for agricultural land-use classification , 2005, SPIE Optics + Photonics.

[35]  B. Brisco,et al.  The application of C-band polarimetric SAR for agriculture: a review , 2004 .

[36]  Y. Ban Synergy of multitemporal ERS-1 SAR and Landsat TM data for classification of agricultural crops , 2003 .

[37]  P. V. Raju,et al.  Classification of wheat crop with multi-temporal images: performance of maximum likelihood and artificial neural networks , 2003 .

[38]  B. Gao NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[39]  M. F. Møller A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning , 1990 .

[40]  Ainong Li,et al.  A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions , 2020, Remote. Sens..

[41]  Mariana Belgiu,et al.  Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis , 2018 .