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

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.

Land Subsidence Susceptibility Mapping Using Persistent Scatterer SAR Interferometry Technique and Optimized Hybrid Machine Learning Algorithms

“…Road data were obtained from the open street map (OSM) at the scale of 1:100,000, and distance to a road was calculated in the GIS environment (Figure 3h). Google Earth Engine (GEE) cloud computing, gathering massive volumes of various satellite imagery alongside popular machine learning algorithms, is a suitable platform for analyzing geo big data and monitoring the environment [46,47]. The available 10-m Iran-wide LULC map was used.…”

Comparison of Keypoint Detectors and Descriptors for Relative Radiometric Normalization of Bitemporal Remote Sensing Images

“…R ELATIVE Radiometric Normalization (RRN) is the process of rectifying radiometric distortions of a multi-band subject image with respect to a multi-band reference image, acquired by inter/intra sensors on the same scene at different times [1]. RRN is usually applied as a preprocessing operation on multi-temporal data prior to their use for remote sensing applications [1,2,3] such as time series image analysis [4], video processing [5], automatic change detection [6], [7], pansharpening [7], and image mosaicking [8].…”

Crop Area Mapping in Southern and Central Malawi With Google Earth Engine

“…They are also quite coarse, being reported at the administrative unit level for ground-based statistics, and generally, 300-1,000 m pixels for satellite-based maps (Carletto et al, 2015;Samasse et al, 2018). The recent availability of 10 m Sentinel-2 data in Google Earth Engine (GEE) allows for efficient processing of high spatial resolution data, making high spatial resolution crop area maps over large areas feasible (Chivasa et al, 2017;Samasse et al, 2018;Jin et al, 2019;Amani et al, 2020;Karlson et al, 2020;Kerner et al, 2020;Masiza et al, 2020;Tseng et al, 2020;Verde et al, 2020).…”

“…Most of the studies using crop phenology to map crop area use either raw bands, vegetation indices (VIs) such as the Normalized Difference Vegetation Index (NDVI, Rouse et al, 1973), or a combination of the two (Samasse et al, 2018;Jin et al, 2019;Amani et al, 2020;Karlson et al, 2020;Kerner et al, 2020;Masiza et al, 2020;Tseng et al, 2020;Verde et al, 2020). In this study we took a different approach.…”