Remote sensing is commonly employed in agriculture for crop monitoring and environmental studies. However, the accuracy of satellite-based products is impacted by image frequency, spectral sensibility and density, and cloud cover. This study evaluates the impact of assimilating Planet Fusion (PF) data into the AgriCarbon-EO (ACEO) crop modeling chain, alone or combined with Sentinel-2 (S2) data, compared to assimilating (S2) data only. Parallel experiments were conducted using PF data alone, or PF data in addition to S2 data in the simulations. Satellite data are used to estimate the Green Leaf Area Index (GLAI), CO2 flux dynamics, biomass and yields of winter wheat, maize and cover crops in south-west France. We analyzed the data availability and spectral resolution impact when using PF alone, a combination of PF and S2 data, and S2 alone. We demonstrate that PF’s lack of red-edge spectral information can lead to GLAI overestimation during vegetation growth. This is mitigated by applying a statistical correction derived from S2-based GLAI. Assimilating both S2 and PF reduces the Root Mean Square Error (RMSE) and bias for biomass estimates by 19.75 g.m−2 and 117.78 g.m−2, respectively, in maize and by 2.15 g.m2 and 13.19 g.m−2 in cover crops, compared to using S2 data alone. However, it did not improve wheat CO2 flux components estimates. We show that the crop biomass accuracy is more strongly affected by the spectral resolution than by image frequency. The synergistic use of S2 and PF data demonstrates potential for improving biomass accuracy, particularly in cloud-prone regions.