# Remote Sensing and Machine Learning Tools to Support
###### Abstract
To what extent are the commonly applied ML approaches valid? * How have the ML models varied over the last decades? * What are the key factors in ML model selection for wetland studies? Are there any specific problems that are being solved preferentially by a specific algorithm? * How important is optical imagery compared to SAR (synthetic aperture radar) data? What is the contribution of clear-sky observations from SAR imagery relative to optical imagery? * Is multi-sensor (multi-source dataset) integration more accurate for wetland area delineation than a single sensor? * What are the individual and combined contributions of SAR and optical data to wetland monitoring? * What are the common satellite sensor types utilized for wetland studies? \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\#} & \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**First Author**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**\#Citation**} & \multirow{2}{*}{**Publication Journal**} & \multicolumn{2}{c}{**Review Type**} & \multicolumn{1}{c}{**Reviewing**} \\ \cline{5-8}  & & & & & **DR** * & **QR** * & **Period** \\ \hline
1 & [This Paper] & [PERSON] & 2022 & 0 & Remote Sensing & ✔ & ✔ & 1990–2022 \\ \hline
2 & [27] & [PERSON] & 2021 & 0 & Land & ✔ & ✔ & 2010–2021 \\ \hline
3 & [28] & [PERSON] & 2021 & 0 & Remote Sensing & ✔ & ✔ & 1976–2020 \\ \hline
4 & [29] & [PERSON] & 2021 & 1 & Remote Sensing & ✔ & ✔ & \(\rm N/A\) \\ \hline
5 & [30] & [PERSON] & 2020 & 9 & Remote Sensing & ✔ & ✔ & 2000–2020 \\ \hline
6 & [31] & [PERSON] & 2020 & 31 & Remote Sensing & ✔ & ✔ & 1991–2019 \\ \hline
7 & [13] & [PERSON] & 2020 & 21 & Remote Sensing & ✔ & ✔ & 1980–2019 \\ \hline
8 & [32] & [PERSON] & 2020 & 14 & Remote Sensing & ✔ & ✔ & 1973–2018 \\ \hline
9 & [33] & [PERSON] & 2020 & 6 & Remote Sensing & ✔ & ✔ & 1973–2018 \\ \hline
10 & [34] & [PERSON] & 2019 & 34 & Earth-Science Reviews & ✔ & ✔ & \(\rm N/A\) \\ \hline
11 & [11] & [PERSON] & 2018 & 79 &
\begin{tabular}{c} GIScience \& Remote \\ Sensing \\ \end{tabular} & ✔ & ✔ & \(\rm N/A\) \\ \hline
12 & [12] & [PERSON] & 2017 & 154 & Sensors & ✔ & ✔ & 1964–2015 \\ \hline \hline \end{tabular} Note: * DR and QR stand for descriptive review and quantitative review, respectively.  
\end{table} 
Table 1: List and a brief summary of previous reviews related to the topic. The list was sorted by publication date (the number of citations is reported by 30 June 2022 on the WoS database). What are the common RS image features utilized for wetland studies? * What are recommended steps for future wetland studies and management? Toward responding to these important questions and describing future needs, empirical evidence is needed for protecting natural resources, resolving conflicts, and appropriately managing common pool resources of wetland areas among stakeholders. As a step in reacting to these calls, this paper's main objective is to help find the best combination of data sources and methods to successfully follow the needs of wetland studies, as pictured in Figure 1.  
The paper is structured as follows. A searching procedure will be defined as the first step in finding and evaluating the candidate studies in consideration of this paper's objectives. Our next step is presenting a typology of the main wetland-based practices with their definitions. We identified the following main categories of practices: classification, change detection (CD), vegetation mapping, biomass estimation, wetland area delineation, hydrological characterization, soil, and carbon estimation. We reviewed RS-based studies used to address each practice. In turn, the most critical features of the studies are highlighted and discussed, as are the research requirements for producing accurate and robust information on the wetlands. Finally, we present recommendations for future research in this field. In the conclusion section, the findings of this meta-analysis are summarized regarding the questions mentioned earlier.