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.

翻译：由于不同传感器的优势不同,同步使用土壤湿度回收传感器引起了相当大的兴趣。主动、被动和光学数据整合可以成为利用不同传感器的优势以绘制土壤湿度图的全面解决办法。通常,多感应器混合需要使用像素方法。由于不同应用需要不同比例的土壤湿度图,因此,以像素为基础的方法有限。使用图像对象而不是像素的物体图像分析可以帮助我们满足这一需要。本文提议了一个基于部分的图像集成框架,以评估通过综合性哨兵-1、哨兵-2和土壤湿度活性数据编制多比例土壤湿度图的可能性。结果证实,拟议的方法能够改进不同比例的土壤湿度估计,比基于像素的聚变法改进20%以上。