Segment-based fusion of multi-sensor multi-scale satellite soil moisture retrievals
This addresses the need for scalable soil moisture maps in applications like agriculture, though it is incremental as it builds on object-based image analysis.
The paper tackled the problem of multi-scale soil moisture mapping by proposing a segment-based fusion framework using Sentinel-1, Sentinel-2, and SMAP data, resulting in up to 20% improvement over pixel-based methods.
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.