In many environmental applications, it is important to obtain accurate and coherent reconstructions of environmental maps. These can be for example spatial representations of hydrological variables or satellite-based remote sensing products. To do this, analogue-based methods provide a fast and interpretable approach that is based on replicating patterns coming from historical observations and predictor similarity. However, their classical weighted-average aggregation tends to smooth spatial variability and attenuate extremes. This study evaluates whether analogue-based reconstruction of MODIS evapotranspiration (ET) over the Ebro watershed can be improved through local analogue selection and stochastic pattern-based aggregation. Several strategies are compared, including domain-wise and tile-wise analogue selection, inverse-distance weighted averaging, and two Multiple-Point Statistics simulation techniques, <em>chessQS </em>and a new method named Anchor Sampling. Weighted averaging provides the best pixel-wise accuracy, image-structure agreement, and computational efficiency, while tile-wise analogue selection yields only marginal improvements over domain-wise selection. Anchor Sampling offers the most balanced stochastic option, improving variogram agreement, timestep water-balance error, and tail-value statistics while remaining closer to the deterministic baseline than <em>chessQS</em>. In contrast, <em>chessQS </em>increases spatial flexibility but reduces reconstruction accuracy and image-structure agreement in the present application. The results indicate that stochastic aggregation can help preserve aspects of spatial variability and distributions, but that simple analogue averaging remains a strong baseline for ET map reconstruction, unless the realism of the spatial structure or uncertainty representation are specifically required.