We propose incorporating both spatial and temporal information via a combined CNN-LSTM classification model. We theorize that the inclusion of temporal information may reduce the number of false positives and improve generalizability to new environments. The model is trained and tested on images of landscapes with and without smoke from the HPWREN tower network in southern California, part of the SAGE remote-sensing infrastructure. We use traditional CNN-based classifiers leveraged in past smoke detection literature as baselines to evaluate our model's performance.