River water extent is critical for understanding river discharge or its hydrological conditions. Although numerous methods have been proposed to map river water from either optical or synthetic aperture radar (SAR) remotely sensed images, uncertainties still exist broadly. In this study, we developed an image fusion method that integrates Landsat-8, Sentinel-1 and Sentinel-2 images simultaneously for river water mapping with two major steps. Firstly, a posterior probability support vector machine model was adopted to generate water probability maps from each individual image; and second, a Multi-dimensional Weighted Fusion Method (MDWFM) was developed to fuse these probability maps. Four reaches with different characteristics were selected as case study sites. High resolution aerial images were acquired and used as the reference to evaluate our results. We found the fusion process not only improves the quality of river water mapping, but also excludes the cloud interference. The fused river water maps become more reliable after the conflicts from difference images being solved by the proposed MDWFM method that contains a proportional conflict redistribution rule. The weighted root mean square difference was reduced to 0.066, and the Area Under the ROC curve reached up to 0.984. The Critical Success Index, Kappa Coefficient, and F-measure reached up to 0.810, 0.836 and 0.895, respectively. These stable and accurate river extent mapping results obtained through fusing multiple images with high spatial resolution (10 m) and short revisit interval (0.4~4.4 days) are of great significance for enriching the data and methodology of hydrological studies.

As the Internet of things (IoT) is increasingly popular, the number of IoT devices such as sensors and smart equipments are growing at an astonishing rate and data generated by these devices is exploding. However, these massive IoT data, stored in the form of isolated data centers, can not be shared by others who also need it. Moreover, data exchange is now needing a secure and fair mechanism to guarantee the data provider's rights and data security. Data providers also lack the motivation to share their data, as no effective mechanism exists to reward this behavior. To solve these problems, we propose a digital asset exchange mechanism based on blockchain technology, in which we record the behavior of data publishing and exchanging into the blockchain, which can ensure the reliability and transparency of data exchange without the restriction of trusted third-party payment institutions. Especially, to inspire the data providers to share their high-quality data, we design an incentive mechanism based on QoS, which gives higher rewards to those who provide high-quality data. Experimental results of this prototype demonstrate that this mechanism is appropriate to be applied in practice.