Very high-resolution satellite images and deep learning are achieving the mapping of individual trees and shrubs over large areas in Africa. Each woody plant is precisely georeferenced and defined by its crown area and, sometimes, its height. The challenge is to build allometric equations for foliage, wood and root dry masses based either on crown area alone, or the product crown area × tree height as independent variables regardless of species. This was met by reanalyzing existing Sahel woody plant data from destructive sampling. Overall, the foliage (seasonal maximum), wood and root dry masses were measured on 900, 698 and 26 trees or shrubs from 27, 26 and 5 species respectively. The regression models tested for foliage, wood or root masses were linear or power functions using a range of different approaches: Ordinary least square (OLS) regression after log–log transform with or without Baskerville correction (1972), and non-linear regressions (NLR). All the models outputs were intercompared for fit indicators and prediction uncertainty, as well as the resulting trends in root to wood ratio, and foliage to wood ratio over the range of crown areas. This process selected a set of OLS log–log equations with crown area as independent variable. When the Baskerville correction is applied the approximation errors are 0.4 kg Dry Matter (DM) for foliage, 12.2 for wood and 6.3 for roots, aggregating at 13.8 kg DM per tree. These allometry equations were compared to published equations for tropical trees, most from the more humid tropics that were generally based on stem diameter, tree height and wood density. This paper's allometry predictions were within the range of these other allometry equations, reinforcing the confidence in their use even beyond the Sahel domain towards sub-humid savannas.