Lobelia’s Soil Moisture product is based on a disaggregation algorithm which takes coarser-resolution input soil moisture from the SMAP and SMOS satellite missions, and converts it into a high resolution product by using Land Surface Temperature and Vegetation information from high resolution satellite missions such as MODIS, Sentinel-3, Landsat and Sentinel-2.The results of this data processing may be affected in regions with persistent cloud cover, where the effectiveness of multi-spectral data is significantly reduced as clouds obstruct the earth’s surface view. This limitation has a higher impact for high-resolution data retrieval, as the opportunity to capture clear images is less frequent, leading to gaps in data or delays in obtaining timely soil moisture information. On the other hand, strong radio frequency interference, such as emissions from radar installations, broadcast stations, or other telecommunication devices, could impact the performance of these satellites used in soil moisture monitoring. These interferences are typically more pronounced in or near urban areas or regions with dense telecommunications networks. However, in the context of agricultural applications, particularly in rural areas where such emissions are less prevalent, the impact of these interferences might be considerably reduced. In the case of Sardinia, these limitations are less likely to affect the effectiveness of the product, as it is a rural area and the mediterranean weather in this area is favorable for remote sensing, and our methodology has been broadly validated for this region.