Monitoring crops with high spatio-temporal resolution satellites provides valuable observations to ensure food security in the global change context. This study focuses on estimating the Green Area Index (GAI) to monitor wheat crops with a spatial resolution of 3 m and daily satellite observations from the SuperDove constellation. With an easier access to large training datasets of ground GAI measurements, and the improvement of the realism of radiative transfer model simulations, the choice of the optimal approach (data -driven or model -driven) constitutes a key question when retrieving GAI from satellite observations. This study compares a data -driven and a model -driven approach to estimate GAI from the SuperDove satellites. Both approaches are based on Gaussian Process Regression (GPR) machine learning techniques. The datadriven approach uses over 300 ground GAI measurements collected from 12 sites in China and France, each with 20 to 51 contrasting plots. The model -driven approach uses 10,000 simulations of top of canopy reflectance and the corresponding GAI values generated by the LESS radiative transfer model applied to 3D scenes built with the ADEL-Wheat (Architectural model of Development based on L -systems) model. Results confirm that the SuperDove reflectance are reliable and consistent with Sentinel -2 values. When estimating GAI using GPR with SuperDove top of canopy reflectance, the model -driven approach (R 2 = 0.83, RMSE = 0.80, Accuracy = 0.01 and Precision = 0.80) generally outperforms the data -driven approach (R 2 = 0.80, RMSE = 0.88, Accuracy = -0.13 and Precision = 0.87), except for small GAI values. In-silico experiments show that the uncertainties in the ground -measured GAI and the size and diversity of the training datasets limit the data -driven approach. In contrast, the model -driven approach is mostly constrained by the realism of the reflectance simulations, particularly for low GAI values. Two ensemble solutions based on the weighted average of the two previous approaches are then proposed: the global ensemble solution (R 2 = 0.86, RMSE = 0.75, A = -0.06 and P = 0.74) where the weight is assumed independent from the GAI values, and the adaptive ensemble solution (R 2 = 0.85, RMSE = 0.76, A = -0.08 and P = 0.76) where the weight depends on the GAI values. Both solutions perform similarly, improving both datadriven and model -driven approaches. Finally, applying both solutions to monitor wheat plots along the growth cycle allows clear differentiation of nitrogen modalities and cultivar effects. However, a minimum plot size of 12 m x 12 m (4 x 4 pixels) is recommended to minimize the co -registration errors and increase estimate precision.