In Saguenay-Lac-St-Jean, there are nearly 27,000 ha of wild blueberries (Vaccinium angustifolium Ait.). This production is carried out in fields with heterogeneous growing conditions due to the local changes in topography, key soil properties, and crop density. The main objective of this study was to develop a regression-based approach to site-specific management (SSM) by integrating proximally and remotely sensed data layers, namely, apparent soil electrical conductivity (ECa), field elevation, and multi-spectral satellite imagery. The study sites were an 11.3-ha flat field (FieldFlat) and a 13.2-ha undulating field (FieldUnd) from Normandin, QC. Soil samples were collected at 5 - 15 cm depth using a 33-m grid sampling strategy and then analyzed for a range of chemical and physical properties. A vegetation index (VI) based on the second principal component in principal components analysis (PCA) was generated from a four-band SPOT satellite image (pan-sharpened to 1.5-m resolution). VI correlation with yield was calculated using Pearson’s correlation test (p < 0.05). Four distinct areas based on combinations of elevation and ECa were defined to signify the most diverse growing conditions in terms of the soil’s potential to store water and nutrients and the landscape’s susceptibility to run-off. Soil characteristics as well as crop performance in these areas were compared using Analysis of Variance (ANOVA) and Tukey’s post-hoc test (α = 0.05). Though neither field showed significant differences in yield among the four growing conditions, several yield-limiting soil properties were significantly different. In both fields, the greatest contrast in soil properties was between high elevation areas with low ECa and low elevation with high ECa. The VI was not strongly correlated with yield (rund=-0.41, rflat=-0.36). However, the VI successfully classified large, contiguous bare spots in the undulating field (rund=0.68). Our findings indicate satellite imagery supplements yield estimation and captures greater crop density variation than the sampled yield. Furthermore, the results indicate an integration of elevation and ECa data targets within-field contrasts effectively for SSM. By combining satellite, elevation and ECa data, our proposed methodology captures diverse field conditions.