  Synergetic use of sensors for soil moisture retrieval is attracting
considerable interest due to the different advantages of different sensors.
Active, passive, and optic data integration could be a comprehensive solution
for exploiting the advantages of different sensors aimed at preparing soil
moisture maps. Typically, pixel-based methods are used for multi-sensor fusion.
Since, different applications need different scales of soil moisture maps,
pixel-based approaches are limited for this purpose. Object-based image
analysis employing an image object instead of a pixel could help us to meet
this need. This paper proposes a segment-based image fusion framework to
evaluate the possibility of preparing a multi-scale soil moisture map through
integrated Sentinel-1, Sentinel-2, and Soil Moisture Active Passive (SMAP)
data. The results confirmed that the proposed methodology was able to improve
soil moisture estimation in different scales up to 20% better compared to
pixel-based fusion approach.

Image and Video Processing;Artificial Intelligence