Climate adaptation for specialty crops under climate-change-induced environmental stress is essential for supporting future crop production demands. Hyperspectral spectroscopy, a type of optical sensing, offers the capability to non-destructively detect abiotic stress before irreversible plant damage occurs. The objectives of this research were to characterize grapevine spectral responses to stress, predict carbon isotope composition (δ13C) based on observed spectral phenotypes to show how spectra capture responses to stress over time, and integrate spectral data with multi-omics data to gain insight into relationships between datasets. Red and white grape varieties were subjected to drought, heat stress, and the combined stress in controlled greenhouse experiments. Leaf reflectance spectra (350-2500 nm) were collected over multiple dates using spectroradiometers. At the middle and end of the experiment, leaf material was collected for δ13C, ionomics, and metabolomics concurrently with leaf spectra. Random forest models showed distinct spectral phenotypes for drought, heat stress, and the combined stress. Partial least squares regression models showed a strong relationship between spectra and δ13C. Linear mixed-effects models of predicted δ13C over time showed that spectral phenotypes do not change over time in the same way between white and red varieties. A multi-block partial least squares discriminant analysis (“DIABLO” method using mixOmics R package) revealed shared biological variation across datasets and suggested that spectral data captured stress responses observed in δ13C, ionomics, and metabolomics data. This study improved our understanding of the biology behind leaf-level spectral phenotypes for drought and heat stress in grapevine, the effects of combined stressors on spectral signatures, and made contributions towards developing diagnostic, spectral-multi-omic signatures for drought and heat stress.