Sclerotinia sclerotiorum (white mold) is a fungus that infects the flowers of snap beans and causes a reduction in the number of pods, and subsequent yields, due to premature pod abscission. Snap bean fields typically are treated with prophylactic fungicide applications to control white mold, once 10% of the plants have at least one flower. The holistic goal of this research is to develop spatially-explicit white mold risk models, based on inputs from remote sensing systems aboard unmanned aerial systems (UAS). The objectives of this study are to i) identify spectral signatures for the onset of flowering towards optimal timing of the fungicide application and ii) investigate spectral characteristics of white mold onset in snap bean, for eventual inclusion in the risk models. This paper concentrates on the first objective. The study area was located at Cornell University, Geneva, NY, USA. A DJI Matrice-600 UAS, boasting a high spatial resolution color (RGB) camera, a Headwall Photonics Nano-imaging spectrometer (272 bands; 400-1000 nm), and a Velodyne VLP-16 light detection and ranging (LiDAR) system, was utilized to collect the data. High frequency flights were flown around days when various portions of the snap bean fields were beginning to flower. The imaging spectroscopy data were first ortho-rectified and then mosaicked using GPS/IMU (inertial measurement unit) information from the UAS. The imagery was calibrated into reflectance data using the empirical line calibration method, based on in-field black/white calibration panels. Samples of flowering and non-flowering snap bean spectra were selected from the hyperspectral imagery, followed by single feature linear discriminant analysis to determine which ratio indices, normalized difference indices, and wavelengths critical for discriminating between flowering and non-flowering plants. Next, the features with the highest c-index trained linear discriminant, logistic regression, and support vector machine models. Results showed that the linear discriminant model had the highest test accuracy of 93%, 95%, and 92% for 20, 10, and 3 features, respectively. These results are promising for eventual implementation in disease risk models.