# Remote Sensing and Machine Learning Tools to Support
###### Abstract
Monitoring and assessing wetlands by RS is an advanced analytical approach [16; 17] that allows for faster, less destructive, and more geographically expansive observation of the spatiotemporal changes in wetlands, as well as changes in the surrounding land use and land cover (LULC). Currently, Satellite RS technology is acknowledged as the most powerful source of data for wetland identification by the U.S. Army Corps of Engineers (USACE) [18], and it has been extensively and successfully employed to monitor, assess, and preserve wetland areas in the past 50 years [12; 18]. As a complement to conventional approaches in wetland monitoring, RS has the potential to deepen our understanding of both local and global patterns of landscape and biodiversity. Through the development of satellite and sensor technology as well as ML algorithms, human involvement in wetland delineation has gradually decreased, leading to progress in techniques and algorithms for mapping these natural resources with RS tools [19; 20]. Using various satellite datasets (e.g., Landsat, Sentinel, MODIS,... ), researchers have produced land cover maps that include wetlands at national, provincial, regional, and local scales. With the increased development of optical and radar sensor platforms, space-borne sensors are becoming more accurate and useful in characterizing wetland extent and discriminating LULC types [21]. A broad range of users, organizations, and researchers can now benefit from technological advances in open-access satellite data streams, cloud computing, and data science to perform fast, accurate, large-scale, and high-resolution land cover classifications [22; 23; 24]. Recently, the advent of the Google Earth Engine (GEE, [[https://earthegine.google.com](https://earthegine.google.com)]([https://earthegine.google.com](https://earthegine.google.com)) (accessed on 20 August 2022)) has provided a professional platform to assist geoscientists interested in geo-big data analysis [7; 25]. This cloud-based platform has revolutionized the processing and analysis of open-source earth observation data, introduced automatic training sample migration possibilities, and supports some ML methods [7; 26]. The most impressive feature of GEE is its ability for large spatial and temporal scale environmental (e.g., wetland) monitoring through parallel computation service. Meanwhile, the development of data science algorithms and the open-source Python language environment and its packages (such as TensorFlow and Keras) have resulted in detailed modeling and analysis of a wide variety of open-source satellite data sets. Therefore, coupling easy access RS satellite data with data analysis, visualization, geo computation, modeling, and ML tools/packages has dramatically enabled scientists to map and monitor landcover within larger regions more accurately and in an automated and repeatable way. Currently, the combined use of GEE with state-of-the-art deep learning (DL) models in wetland studies is absent from the literature and has not yet been well documented. As listed in Table 1, several review papers have summarized the studies conducted on wetlands from different perspectives. Given its importance, a meta-analysis of studies on the capabilities of RS sensors, features, and ML models for monitoring and characterizing worldwide wetlands is overdue. Thus, we reviewed the literature related to RS data and the methods of producing spatial information on wetlands. The findings of the available review studies indicate that a further review paper should be carried out to compare the different techniques and concepts. Moreover, to the best of our knowledge, a comprehensive review of recent achievements regarding ML and DL for global wetland studies using RS data is still lacking. In order to reach this objective, the following lead questions are considered, and the current meta-analysis sought to address them:  
* Which ML method is best suited to monitor wetlands surface cover and adjacent areas and provide the best result from RS imagery?