The use of unmanned aerial systems (UAS) in precision agriculture has increased rapidly due to the availability of reliable, low-cost, and high-resolution sensors as well as advanced image processing software. Lint yield in cotton is the product of three physiological parameters: photosynthetically active radiation intercepted by canopy (IPAR), the efficiency of converting intercepted active radiation to biomass (RUE), and the ratio of economic yield to total dry matter (HI). The relationships between lint yield and vegetation indices (VI’s) in cotton have been extensively studied; however, reports addressing the yield determining physiological parameters are far less common. A study was conducted during the 2021 growing season with the objective of relating different VI’s derived from UAS multispectral imagery with yield-determining physiological parameters (IPAR, RUE, and HI) of cotton. Five different nitrogen treatments were applied to generate substantial variability in canopy development and yield. Multispectral imagery was collected fortnightly along with light interception and biomass measurements throughout the season. Several different VI’s were computed using the red (668 nm), blue (475 nm), green (560 nm), near-infrared (842 nm), and red-edge (717 nm) spectral bands. A regression analysis was performed to identify VI’s that can be used to predict IPAR, biomass, and RUE in cotton. Data analysis indicated that power functions best described the relationship of IPAR and cotton biomass with VI’s. RVI and NDRE explained more than 90% of variation in IPAR with R2 value of 0.932 and 0.916, respectively. Similarly, cotton biomass was found to be strongly related with GNDVI (R2 = 0.929) and SCCCI (R2 = 0.906). In context of RUE, most of the variation was best explained by GRVI (linear relationship with R2 0.549) and GNDVI (linear relationship with R2 0.419). The results from this study show that VI’s such as GNDVI, RVI, and GRVI derived from UAS multispectral imagery could potentially be used to predict certain physiological parameters (IPAR, biomass, and RUE) of cotton within a growing season. Utilizing UAS technology to predict these parameters can help in advancing high throughput phenotyping and prediction of yield driving parameters in response to nitrogen.