The wheat stem sawfly (WSS, Cephus cinctus Norton) is a major insect pest of wheat (Triticum aestivum L.) in North America. Few management tactics exist, and quantifying their efficacy is confounded by the difficulty in monitoring infestation at the field scale. Accurate estimates of WSS infestation are cost prohibitive as they rely on comprehensive stem dissection surveys due to the concealed life cycle of the pest. Consolidating the available management tactics into an effective strategy requires inexpensive, spatially explicit estimates of WSS infestation that are compatible with the large field sizes dryland wheat is often sown to. Therefore, we investigated using multitemporal satellite passive remote sensing (RS) to estimate various metrics of WSS infestation collected from field surveys at the subfield scale. To achieve this, we dissected 43,155 individual stems collected from 1158 unique locations across 9 production wheat fields in Montana, USA. The dissected stem samples from each location were then quantified using the following metrics: the proportion of total WSS-infested stems, proportion of stems with more than one node burrowed through (adequate WSS infestations), and proportion of WSS cut stems only. Cloud-free Sentinel 2 images were collected from Google Earth Engine for each field from across the growing season and sparse multiway partial least squares regression was used to produce a model for total WSS infestations, adequate WSS infestations, and WSS cut stems, for each sampled field. Upon comparing the performance of these models, we found that, on average, the metrics for total (R2 = 0.57) and adequate WSS infestations (R2 = 0.57) were more accurately estimated than WSS cut (R2 = 0.34). The results of this study indicate that multitemporal RS can help estimate total and adequate WSS infestations, but more holistic methods of field level sensing should be explored, especially for estimating WSS cutting.