Development of a transferrable hybrid retrieval model for mapping sweet potato chlorophyll at matured growth stage using ultra high-resolution UAV data

Abstract. Smallholder farmers play a critical role in the growing of underutilized crops, such as sweet potato. Obtaining accurate maps of sweet potato biophysical variables is essential for farmers to assess and monitor crop health at different growth stages. Integrating radiative transfer model (RTM) data with vegetation indices (VIs) based on unmanned aerial vehicle (UAV) data, may have the potential for accurately estimating leaf chlorophyll concentration (LCC) across multiple crop varieties. Firstly, in this paper we developed and tested varying hybrid retrieval models by combining PROSAIL RTMs with broadband, narrowband and leaf-pigment VIs applied to 2-cm resolution UAV imagery, to retrieve LCC over 20 sweet potato varieties at 120 days i.e. matured growth stage. Secondly, the best hybrid retrieval model was transferred to a different site which contain similar sweet potato varieties at matured growth stage for the estimation of sweet potato LCC. Results show that the most accurate retrievals of LCC were achieved by integrating a larger database containing 11000 PROSAIL simulated reflectance samples with broadband indices, particularly the enhanced vegetation index (EVI) with coefficient of determination (R²) of 0.85, root mean squared error (RMSE) of 5.93 μg/cm², and relative RMSE (RRMSE) of 9.87%. Furthermore, when transferred to a different site containing similar sweet potato varieties at matured growth stage, this model achieved 60% agreement with field LCC measurements and responded fairly well by capturing LCC variability. These findings have significant implications in sweet potato breeding programmes for developing new cultivars.