ISPRS-Annals ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Annals ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9050 Copernicus Publications Göttingen, Germany 10.5194/isprs-annals-XI-3-2026-813-2026 Development of a transferrable hybrid retrieval model for mapping sweet potato chlorophyll at matured growth stage using ultra high-resolution UAV data Tsele Philemon 1 Ramoelo Abel 2 3 Moleleki Lucy 4 Laurie Sunette 5 Mphela Whelma 6 Tshuma Natasha 1 Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0002, South Africa Earth Observation Programme, South African National Space Agency, Council for Scientific and Industrial Research (CSIR)’s, Building 10, Meiring Naude, Pretoria 0002, South Africa Centre for Environmental Studies, Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0002, South Africa Department of Biochemistry, Genetics and Microbiology; Forestry and Agricultural Biotechnology Institute (FABI), University of Pretoria, Pretoria 0002, South Africa Agricultural Research Council (ARC)-Vegetable, Industrial and Medicinal Plants, Pretoria 0001, South Africa Innovation Africa, University of Pretoria, Pretoria 0002, South Africa 08 07 2026 XI-3-2026 813 819 Copyright: © 2026 Philemon Tsele et al. 2026 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-annals.copernicus.org/articles/XI-3-2026/813/2026/isprs-annals-XI-3-2026-813-2026.html The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/XI-3-2026/813/2026/isprs-annals-XI-3-2026-813-2026.pdf

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 (R<sup>2</sup>) of 0.85, root mean squared error (RMSE) of 5.93 &mu;g/cm<sup>2</sup>, 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.