# Remote Sensing and Machine Learning Tools to Support
## 3 Results and Discussion
### RS Data Used in Wetland Studies  
#### 3.4.1 Data Type  
The use of satellite RS in environmental research studies has been widely adopted as a rapid scientific tool for monitoring and investigative purposes [7; 21; 64; 65], as shown in Figure 11. It offers a powerful alternative over-ground survey, providing a synoptic view, multispectral data, multitemporal coverage, and cost-effectiveness [36; 66]. With these improvements and advances in quality, volume, and diversity, spectral imagery is being used to study wetlands more effectively and with a wider variety of applications. These advances support increases in the quality, volume, and diversity of applications of spectral imagery in the study of wetlands, in particular, accurate assessment of their status and measuring the pattern of the changes, as well as the success of restoration efforts. Meanwhile, wetland studies conducted by optical and SAR imagery have by far the largest shares. A variety of applications have been proposed and studied for RS optical sensors, from small target evaluation to global issue resolution. In the wetland field of studies, in addition to providing information on vegetation cover and community type, optical sensors are capable of identifying and differentiating wetlands and vegetation zones, as well as mapping wetlands on a large scale, among other applications [67]. Over the last two decades, improvement of satellite spatial resolution and reduced costs of multi- and hyperspectral sensors have increased the scope for applying spectral imaging to advance wetland study. Multispectral satellite imagery can be acquired at resolutions \(<\) 5 m, enabling examination of wetland systems at much finer scales and supporting longitudinal analyses of change over extended periods. However, due to their inability to penetrate clouds and atmospheric haze or a dense vegetated canopy, optical sensors are limited to daytime clear-sky image acquisition [24]. Even though optical satellite imagery successfully detects and monitors wetlands, the presence of cloud cover limits their usefulness, especially in coastal areas [14; 68; 69]. Figure 11: Distribution of employed data types and their combinations with the largest shares for multispectral and hyperspectral (46%), SAR (20%), and LiDAR/DEM (10%) data types. Unlike optical sensors, as an active RS system, SAR is not affected by atmospheric conditions, can detect sub-canopy soil and vegetation structural features, and does not rely on external sources of radiation (i.e., sunlight) for its operation. It has the ability to overcome the above limitations (unaffected by weather conditions) and can operate in all-weather and daylight-independent conditions [36; 70; 71], so it is well suited for wetland monitoring. SAR has proven to be a preferred alternative or supplemental source to optical images for wetland mapping, especially in coastal wetlands and intertidal zones [69; 72; 73]. However, several factors affect SAR data and its components, such as the effects of surface moisture content and roughness and instrument viewing direction, and incidence angle [74]. As a result, the collected backscattering SAR signals influenced by vegetation and soil properties carry information related to wetlands and soil's geometric structure and dielectric properties [73]. This makes SAR data essential to distinguish certain land cover classes such as \"built-up\" and \"bare soil\", or classes with different levels of moisture and inundation such as \"heterogeneous wetland sites\" and \"intermittent water bodies\". Considering the advantages of optical and SAR data, the combination of optical and SAR imagery can offer the greatest potential for supporting wetland mapping and monitoring tasks, as described in [21]. It is critical, therefore, to explore the extent to which optical and SAR RS data may supplement the LULC studies, particularly in wetland regions. Our findings showed that multispectral and hyperspectral optical imagery and SAR data present the greatest portion of wetland studies.