# Remote Sensing and Machine Learning Tools to Support
###### Abstract  
Despite their importance to ecosystem services, wetlands are threatened by pollution and development. Over the last few decades, a growing number of wetland studies employed remote sensing (RS) to scientifically monitor the status of wetlands and support their sustainability. Considering the rapid evolution of wetland studies and significant progress that has been made in the field, this paper constitutes an overview of studies utilizing RS methods in wetland monitoring. It investigates publications from 1990 up to the middle of 2022, providing a systematic survey on RS data type, machine learning (ML) tools, publication details (e.g., authors, affiliations, citations, and publications date), case studies, accuracy metrics, and other parameters of interest for RS-based wetland studies by covering 344 papers. The RS data and ML combination is deemed helpful for wetland monitoring and multi-proxy studies, and it may open up new perspectives for research studies. In a rapidly changing wetlands landscape, integrating multiple RS data types and ML algorithms is an opportunity to advance science support for management decisions. This paper provides insight into the selection of suitable ML and RS data types for the detailed monitoring of wetland-associated systems. The synthesized findings of this paper are essential to determining best practices for environmental management, restoration, and conservation of wetlands. This meta-analysis establishes avenues for future research and outlines a baseline framework to facilitate further scientific research using the latest state-of-art ML tools for processing RS data. Overall, the present work recommends that wetland sustainability requires a special land-use policy and relevant protocols, regulation, and/or legislation. w 2022  
resource use [3]. As such, wetlands have been referred to as the \"kidneys\" of nature [11] because of the many environmental services and ecological benefits they provide. In terms of levels and purposes, more than 50 definitions of wetlands throughout the world have been presented [12; 13]. The varying definitions come from the characteristics of the wetlands and how they are managed in different countries. However, in most definitions, wetlands are regarded as a unique ecosystem formed by interactions between the local area's geomorphology, hydrology, soil, and ecology. As defined explicitly in the Ramsar Convention, wetlands are distinct ecosystems that can be detected and monitored on a variety of scales using satellite remote sensing (RS) imagery and machine learning (ML) algorithms [14]. The combination of RS and ML can greatly relieve the heavy burden of manual work for wetland-related studies and offer huge potential and a wide scope of application space in these areas. Even though there is a lack of reliable data, it is a concern to stakeholders that wetlands continue to be lost and degraded, as no science-based protocols are available in the most wetland regions to guide the monitoring of wetlands [6; 7]. Under the joint influence of natural accumulation, artificial reclamation, and urbanization, wetlands are constantly changing over time. Moreover, several factors influence wetland changes, including the local terrain, hydrology, soil, economy, population, and policy. As such, wetland sustainability requires special land-use policy and relevant regulation and/or legislation to monitor and assess the status of these crucial habitats locally, regionally, and globally. Furthermore, the rapid expansion of agricultural areas and urbanization are important factors in wetland areas having decreased significantly over the past three decades. Wetland resources can be effectively preserved by having up-to-date information on the status and extent of land cover within the ecosystem [15].