# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
## 3 Data and Methods
### Background
SAR sensors transmit a microwave signal towards the target and detect the backscattered portion of the signal. The strength of the signal returned can be used to identify different targets, however, this can vary depending on the polarization used to transmit and receive. Typically, cross polarizations (HV, VH) have been shown to be more informative for volume scatterers, such as forests, whilst co-polarizations (HH, VV) are more effective for surface scatterers, such as water bodies. Sentinel-1 in IW mode operates using the polarizations VH and VV across most parts of the world. Studies support a slight increase in performance of co-polarization (VV) compared to cross polarizations (VH) in land-water classification [37; 46]. More generally, the composition of the area in question will impact upon the choice of polarization for land-water classification. Numerous studies show SARs effectiveness for water inundation and flood mapping [37; 46; 47; 48; 49; 50], however, the separation of land and water based on backscatter values alone is rarely perfect. Inundated vegetation can cause complex scattering mechanism and is a frequently reported source of omission errors [51; 52]. Similarly, wind and rain may cause omission errors by increasing waters' surface roughness, resulting in higher backscatter coefficients than expected. [53]. Reversely, dry bare sand, and alluvial sediment often exhibit low backscatter properties, which can be confused for water and lead to commission errors [54; 55]. Speckle noise or interference is another undesirable effect, which can result in spurious water detection. Filtering, image segmentation and texture analysis have been widely applied in attempts to overcome this, and to incorporate the assumption of smoothness, i.e., nearby pixels in space or time are more closely related than distant pixels [56; 57]. Moreover, the side-looking geometry of SAR imagery combined with topographic relief can create shadow effects that reduce backscatter and distorts the image geometry [58]. There are limitations to mapping surface water when relying solely on either optical or SAR-based instruments. Therefore, the synergistic use of optical and SAR data emerges as an interesting alternative, with the potential to take advantage of the individual sensors' strengths, while minimizing their weaknesses. The fusion of optical and radar data is not new. A review of more than one hundred data fusion studies for land use/land cover assessments concluded that the combination of optical and SAR data provided results with higher accuracy than using either of the datasets individually [59]. However, for large scale operational applications data, fusion approaches have been limited by the lack of systematic SAR data. This is a situation that has now changed following the emergence of the European Copernicus program. In Northern Ireland, [PERSON] et al. [60] investigated a decision level Sentinel-1 and -2 data fusion approach for surface water mapping, whereas [PERSON] et al. [61] harmonized Sentinel-1 and Landsat time-series into a common water index over a cloud prone region in the lower Mekong. Whilst both these studies showed promise, they were performed on a limited scale, but as demonstrated by [PERSON] et al. [62], there is also a potential for Optical-SAR data fusion for larger national scale surface water mapping. Furthermore, continuous improvements in computational capacity, availability of cloud-based platforms, access to global archives of satellite data and the required tools for big data analytics have further incentivized mapping at scale. As an established remote sensing application, an accurate and efficient approach for fusing the latest generation of free and open optical and SAR data is needed to support surface water mapping at a large scale and to provide operational services. Whilst accuracy should be the foremost concern when choosing a mapping approach, simplicity and ease of implementation are also important to consider the increase to understanding, maintainability, and potential scalability.