Segment-based fusion of multi-sensor multi-scale satellite soil moisture retrievals

TL;DR

This paper introduces a segment-based fusion framework combining Sentinel-1, Sentinel-2, and SMAP data to generate multi-scale soil moisture maps, outperforming pixel-based methods by up to 20%.

Contribution

It presents a novel segment-based image fusion method for multi-sensor soil moisture retrieval, enabling multi-scale mapping beyond pixel-based limitations.

Findings

Improved soil moisture estimation accuracy up to 20%.

Effective multi-scale soil moisture mapping achieved.

Segment-based approach outperforms pixel-based fusion.

Abstract

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.