A wide range of ecological, agricultural, hydrological and meteorological applications at local to regional scales requires decametric biophysical data. However, before the launch of SENTINEL-2A, only few decametric products are produced and most of them remain limited by the small number of available observations, mostly due to a moderate revisit frequency combined with cloud occurrence. Conversely, kilometric and hectometric biophysical products are now widely available with almost complete and continuous coverage, but the associated spatial resolution limits the application over heterogeneous landscapes. The objective of this study is to combine unfrequent decametric spatial resolution products with frequent hectometric spatial resolution products to improve the temporal frequency and completeness of decametric observations. The study focuses on the fraction of photosynthetically active radiation absorbed by the green vegetation (FAPAR) because of its important role in canopy models and small dependency to scaling issues. An algorithm is developed to provide near real time estimates of FAPAR called DHF (for Decametric Hectometric Fusion) at a decametric resolution and dekadal time step. It is assumed that the FAPAR time course is described by a second-degree polynomial function over a limited 60-days temporal window for each decametric pixel. To reduce the dimensionality of the problem, landcover classes are considered instead of each individual pixel. For each class, the coefficients of the polynomial function are adjusted using the temporal course of the available decametric FAPAR products, under the constraint of providing a good match with the time course of the hectometric dekadal FAPAR products. The point spread function associated to the hectometric FAPAR products and the possible biases between the decametric and hectometric FAPAR products are explicitly accounted for. The algorithm was evaluated over a time series of decametric Landsat-8 FAPAR images (30 m) and hectometric (330 m) dekadal GEOV3 FAPAR derived from PROBA-V images acquired in 2014 over a site in the South-West of France. Results show that the estimated DHF FAPAR products capture well the expected seasonal variation and spatial distribution while improving the temporal frequency and spatial and temporal completeness of the original Landsat-8 products. A leave one out exercise shows that the DHF values are in very good agreement with the Landsat-8 FAPAR (RMSE = 0.05–0.14) that were not used when computing the DHF. This demonstrates the robustness of the algorithm and interest under cloudy regions. Additional comparison with ground measurements collected over 14 sunflower fields along the growth season confirms the good performances of the DHF FAPAR products (RMSE = 0.11).