# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
## 3 Data and Methods  
### Background  
Surface water mapping is an established application in remote sensing, yet by reviewing the extensive literature on the subject, the approaches vary according to the objectives and scale of the study, sensor used, and environmental settings. In the optical domain, a common approach for mapping surface water is done using image thresholding on spectral bands or water indices such as the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI) or Automated Water Extraction Index (AWEI) [27; 28; 29; 30; 31; 32; 33; 34]. Theoretically, the thresholds for separating water from non-water are universal, but praxis thresholds tend to vary with the regional settings and atmospheric conditions. The thresholding approach therefore tends to be less reproducible, especially over large areas, as it often requires the intervention of experts to determine the optimal threshold. Water indices and thresholding are also impacted by factors such as water turbidity, shadows from terrain, buildings, and clouds, as well as the presence of snow and ice. For instance, the AWEI comes with optimized coefficients for water extraction in situations with shadows and/or other dark surfaces. Yet, in areas with highly reflective surfaces such as ice, snow and reflective roofs, the optimization may lead to misclassifications, and limit the applicability over larger diverse landscapes [27]. Topographic issues, caused by cast shadows from the terrain, can be suppressed by developing masks from Digital Elevation models, e.g., terrain shadow masking [35] or using the Height Above Nearest Drainage (HAND) index [36], to eliminate commission errors located in areas where water is not expected to accumulate [7; 37]. Machine learning (ML) represents another avenue to optical water detection and may overcome some of the inherent issues with threshold-based approaches. A range of methods exist that can be broadly separated into unsupervised and supervised approaches. Unsupervised algorithms require fewer input parameters and decide themselves how to structure the data for example through clustering (e.g., k-means) or automatic thresholding, e.g., the Otsu method [38], with a label being applied afterwards. However, since unsupervised algorithms are self-learning, they depend heavily upon the distribution of the data itself, which can become problematic in certain situations, such as when the data is not naturally bimodal [39]. However, supervised algorithms learn to predict based on labeled input data and are therefore less reliant on the distributions of the target data. A wide range of supervised algorithms has been applied to water classification, including support vector machines [40], decision trees [12], random forests [41] and convolutional neural networks (CNNs) [42]. These algorithms require the labeled data to be applicable across the study area in order to effectively classify unseen data. At larger scales, this can be problematic due to diversity of water conditions such as turbidity, chlorophyll and aquatic vegetation, which poses a challenge to creating universally applicable training data [43]. Regardless of the methodology, cloud cover remains a core limitation of optical imagery and achieving a cloud free coverage will often require some form of temporal image compositing [44]. Nonetheless, in areas with persistent cloud cover and/or lack of sunlight, the required compositing period may comprise the dynamics of the phenomenon being studied. For example, [PERSON] et al. [45] used full annual time series of Landsat 7 and 8 optical imagery, but due to clouds, the authors failed to extract an accurate annual water mask over the Brahmaputra. The generated mask was conservative, only representing low flow conditions because clouds prevented monitoring during the high flow period. Another more direct way to overcome issues with clouds is to use weather and light independent microwave sensors such as Synthetic Aperture Radar (SAR).