Estimating crop biophysical variables is essential to farmers for assessing and monitoring crop growth at different stages. A comprehensive comparison and integration of retrieval methods is needed for accurately estimating crop biophysical variables such as chlorophyll over a heterogenous sweet potato canopy. In this paper, we explored the capabilities of PROSAIL radiative transfer models (RTMs) applied to 5 cm resolution drone multispectral imagery to retrieve the leaf chlorophyll content (LCC) of over 20 sweet potato cultivars at the peak growth stage. Various vegetation indices spanning broadband, leaf pigment, and narrowband indices were tested on numerous PROSAIL simulation databases in order to optimize their retrieval performance. The results show that the most accurate retrievals of LCC from drone data over 20 sweet potato cultivars wereachieved by integrating larger (11000) PROSAIL simulations with broadband indices, particularly the enhanced vegetation index (EVI) with an R² of 0.85, an RMSE of 5.93 µg/cm², and a RRMSE of 9.98%. This performance was followed by that of narrowband indices, particularly the modified normalized vegetation index (mNDVI), with an R² of 0.84, an RMSE of 5.95 µg/cm², and an RRMSE of 9.91%. Furthermore, a polynomial fitting type model best captured the variability of the LCC compared to the linear model. An attempt to integrate lesser PROSAIL simulations (i.e., of 1500, 5000, and 9000 reflectance samples) with the indices showed deteriorating LCC retrieval performance. These findings suggest that ultra-high-resolution drone imagery may be appropriate for the accurate retrieval and monitoring of LCC over a heterogenous canopy comprising numerous crop cultivars.