Data assimilation comprises steps to improve the level of interpretability of the input data, by enrichment (get rid of spatial/temporal gaps), by accounting for heterogeneity (through harmonization), and by integration (combination with other data that is relevant to the application). Thereby, datasets become more comparable to each other.
Alternative understanding of data assimilation: Data assimilation techniques aim to combine different kinds of information in order to provide an accurate estimate of geophysical / biophysical parameters of dynamic earth models (e.g. geophysical models, crop growth models, climate models etc.)
[two publications that use data assimilation in this way: https://www.tandfonline.com/doi/full/10.3402/tellusa.v67.23629; https://www.sciencedirect.com/science/article/pii/S1474706503000032 ]
Compare different types of data assimilation
Planned