Nuisance cyanobacterial blooms degrade water resources through accelerated eutrophication, odor generation, and production of toxins that cause adverse effects on human health. Quick and effective methods for detecting cyanobacterial abundance in drinking water supplies are urgently needed to compliment conventional laboratory methods, which are costly and time consuming. Hyperspectral remote sensing can be an effective approach for rapid assessment of cyanobacterial blooms. Samples (n=250) were collected from five drinking water sources in central Indiana (CIN), USA, and South Australia (SA), which experience nuisance cyanobacterial blooms. In situ hyperspectral data were used to develop models by relating spectral signal with handheld fluorescence probe (YSI 6600 XLM-SV) measured phycocyanin (PC in cell/ml), a proxy pigment unique for indicating the presence of cyanobacteria. Three-band model (TBM), which is effective for chlorophyll-a estimates, was tuned to quantify cyanobacteria coupled with the PC probe measured cyanobacteria. As a comparison, two band model proposed by Simis et al. (Limnol Oceanogr, 50(11): 237-245, 2005; denoted as SM05) was paralleled to evaluate TBM model performance. Our observation revealed a high correlation between measured and estimated PC for SA dataset (R (2) =0.96; range: 534-20,200 cell/ml) and CIN dataset (R (2) =0.88; range: 1,300-44,500 cell/ml). The potential of this modeling approach for imagery data were assessed by simulated ESA/Centinel3/OLCI spectra, which also resulted in satisfactory performance with the TBM for both SA dataset (RMSE % =26.12) and CIN dataset (RMSE % =34.49). Close relationship between probe-measured PC and laboratory measured cyanobacteria biovolume was observed (R (2) =0.93, p<0.0001) for the CIN dataset, indicating a stable performance for PC probe. Based on our observation, field spectroscopic measurement coupled with PC probe measurements can provide quantitative cyanobacterial bloom information from both relatively static and flowing inland waters. Hence, it has promising implications for water resource managers to obtain information for early warning detection of cyanobacterial blooms through the close association between probe measured PC values and cyanobacterial biovolume via remote sensing modeling.