# Remote Sensing and Machine Learning Tools to Support
## 5 Conclusions  
In this study, the trend of worldwide RS-based wetland monitoring studies was established, exploring 344 papers published in the last three decades. Over fifteen sub-fields were summarized and highlighted, including ML approaches and their accuracies in wetland studies, RS data types and their corresponding accuracies, journals/conferences/publishers, authors affiliations, publications per year, the geographical distribution of case studies and the corresponding study extent, and paper citations. Through this, a comprehensive meta-analysis was used to discuss the utilization of RS and ML tools in these subfields. Consequently, this paper addresses the role of RS and ML tools in supporting global wetland monitoring. Research opportunities and directions for further supporting wetlands studies were also presented in the paper. In summary, the general findings of this review paper concerning both technological and substantive viewpoints are as follows:* A total of 88 journals have published the papers summarized in the present meta-analysis (with a share of 89%). The papers from the IGARSS conference, as well as a few from other international conference proceedings, were also included. About 67% of these publications were published between 2018 and 2022. * More than half (51%) of the reviewed publications investigated wetlands from the perspective of classifying wetland zones, while 14% analyzed the changes within the wetlands. Wetland vegetation mapping and wetland extent recognition are in third and fourth places, with a share of 12% and 7% among all reviewed papers. * Over 70% of the research studies have been conducted in China, the USA, and Canada, illustrating the need for wider international efforts to be undertaken in other countries in order to have consistent monitoring of wetlands across the globe. Over the past few years, the number of wetland studies has increased. In light of the increase in quality RS data availability, the launch of new RS platforms, as well as increased computing capabilities, and the growing interest in wetlands as part of climate change research, it is likely that this trend will continue. * Slightly more than three-quarters of the studies have been conducted at areas with very small and local scales, whereas a few national-scale research papers have been published. More large-scale (e.g., continental-scale) studies are likely to be conducted as the number of satellites continues to increase and data become more widely available. The very recent ability of GEE to apply DL models has also opened up new possibilities for large-scale wetland classification research. * The largest number of studies have been conducted on optical sensors using the Landsat archive (with 201 studies) and Sentinel-2 (with 54 studies), while SAR-based studies mostly employed data from Sentinel-1 (with 45 studies) and RADARSAT-2 (with 31 studies) missions. This is likely partially due to the relatively long history of these datasets and low/no cost availability. * Reviewed studies indicated that optical images had most often been used in the wetland monitoring tasks with 247 studies, followed by SAR datasets with 116 studies. A fusion of data types, including optical and SAR data, increases overall accuracy compared to each data type separately. * A review of the published literature from the methodological viewpoint found that 152 studies adopted ensemble learning methods, 70 employed DT-based methods, and 68 utilized kernel-based methods. Further, among different classification approaches, CNN as a DL model, as well as RF as a ML model are the most successful classifiers for wetland mapping. * As expected, spatial resolution was highly correlated with the overall accuracy of the wetland classification. This shows that wetland mapping may be improved by high/medium resolution RS imagery, at least until some minimum resolution threshold is reached.