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Deep learning in land-use classification and geostatistics in soil pH mapping: a case study at Lakehead University Agricultural Research Station, Thunder Bay, Ontario, Canada

Abstract. Grain agriculture is one of the main economic activities in Thunder Bay. However, farming faces many challenges: shorter growing seasons, severe weather conditions, climate change, and limited services and economic support. Lakehead University Agricultural Research Station (LUARS) is continuously working toward improving farming in Northern Ontario and testing different agricultural initiatives. Precision agriculture (PA) is one of the strategic means to improve Northern Ontario farming and sustain agricultural operations. Hence, PA requires accurate and up-to-date baseline information; the aim of this study is to develop baseline spatial information (land cover and soil pH variability maps) for the LUARS using remote sensing technology. A set of aerial images were acquired from the remotely piloted aircraft system (RPAS) and created an orthophoto (7-cm spatial resolution). A detailed land cover map was produced using a deep learning algorithm. We collected soil samples from three different sections (plots) of the area to analyze soil pH variations. Different geostatistical methods [ordinary kriging, Simple kriging, and empirical Bayesian kriging (EBK)] and deterministic interpolation techniques: local polynomial interpolation (LPI) and inverse distance weighted (IDW) were tested to map the spatial distribution of soil pH. A land cover classification achieved an overall accuracy of 73% and a Kappa coefficient of 0.70. Most of the homogeneous agricultural plots indicated higher producer’s and user’s classification accuracies. There was no linear correlation between soil sample data and vegetation indices derived from RPAS images to test the linear regression for soil pH mapping. EBK, LPI, and IDW produced soil pH maps with higher accuracies for Secs. 1–3, respectively.