With a limited amount of arable land, worsening soil degradation, and plateauing of crop yields, the maintenance of limited soil resources to guarantee agriculturally productive soils and to support essential ecosystem services is critical in Canada and globally. The main objective of this thesis is to study and develop practical methods to cost-effectively renew soil and soil landscape information in Canada. Given the limited amount of point-based soil data in Canada, machine learning-based predictive methods were studied for possible operational use. Machine learning methods require point soil data both for training purposes and validating the predictions. When soil data points are lacking, pseudo-point soil data are mined through soil survey polygon maps. In places where detailed soil surveys exist for soil class mapping, fully randomized pseudo-soil point data mining with random forest-based machine learning achieved a prediction accuracy as high as 74%. The prediction was further improved to a high of 78% by using an ensemble of multiple machine learners. Soil properties such as bulk density can be predicted either directly using point soil data or indirectly via predicted soil classes which are associated with reported soil property values. In this study, sampled soil bulk density values were used to predict soil bulk density across the study watershed. The predicted bulk density values come with uncertainty ranges, computed using residual kriging. Soil class prediction (and soil bulk density) may be carried out using environmental covariates from different sources. It is shown that where surficial geological material data are lacking, time series microwave remotely-sensed data, specifically Sentinel-1A synthetic aperture radar (SAR) imagery, can be used to delineate soil spatial patterns which are hypothesized to be linked to the spatial distribution of surficial geological materials. Through this study, a cost-effective work flow and solutions for predictive soil mapping needs in Canada were developed for operational use.