# Remote Sensing and Machine Learning Tools to Support
## 3 Results and Discussion
### Methodology Analysis
#### 3.5.2 Google Earth Engine (GEE)  
For the mapping and monitoring of wetlands over broad spatial scales and for long periods of time, long-term earth observation data archives, such as those provided by the optical Landsat repository (1984 onwards), are crucial resources. Mapping wetlands on a long-time scale along with high spatial resolution requires collecting and storing large datasets and often involves complicated processing and manipulation. It is infeasible or time-consuming to use conventional image processing software, such as ENVI and ERDAS; thus, an effective platform is required. For such a system to be established, strong computing power and high capacity of data storage is needed, and these can be limiting factors for many decisionmakers around the world, especially in resource-poor regions. With GEE, users can freely access cloud-based computing resources and parallel processing for analysis on a global scale [7]. To provide benefits to the broader community, GEE offers an online integrated development environment using Earth Engine JavaScript, designed to ease geospatial data analysis and preparation complexities, without the need of heavy data downloading and data-intensive processing. It hosts petabyte-scale RS data, including the NASA's Landsat and ESA's Sentinel archives, crucially corrected to surface reflectance, providing a unique opportunity for developing and disseminating worldwide environmental monitoring systems. In addition to the data provision, GEE is equipped with many ML tools, such as RF Classification. Studies using GEE as an RS data provider with ML techniques have become more numerous since its advent in 2015. Such studies account for 11% of included studies (_n_ = 40 of 344) in the current systematic review. More details of these publications have been presented in Figure 17 and Table 7.  
Figure 16: Number of studies employed different ML models in wetland studies. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**\#** & **Ref.** & **Application Type** & \begin{tabular}{c} **Publication** \\ **Date** \\ \end{tabular} & **\#** & **Ref.** & \
\begin{tabular}{c} **Application** \\ **Type** \\ \end{tabular} & **
\begin{tabular}{c} **Publication** \\ **Date** \\ \end{tabular} \\ \hline
**1** & [144] & Change Detection & May, 2016 & 21 & [145] & Classification & Aug, 2020 \\ \hline
**2** & [146] &
\begin{tabular}{c} Wetland \\ Inundation \\ Analysis \\ \end{tabular} & Dec, 2016 & 22 & [147] & Classification & Sep, 2020 \\ \hline
**3** & [148] & Classification & Dec, 2017 & 23 & [7] &
\begin{tabular}{c} Change \\ Detection \\ \end{tabular} & Nov, 2020 \\ \hline
**4** & [22] & Classification & Dec, 2017 & 24 & [149] &
\begin{tabular}{c} Wetland \\ Extent \\ Extraction \\ \end{tabular} & Dec, 2020 \\ \hline
**5** & [150] & \
\begin{tabular}{c} Carbon Content \\ and Biomass \\ \end{tabular} & May, 2018 & 25 & [151] &
\begin{tabular}{c} Wetland \\ Vegetation \\ Mapping \\ \end{tabular} & Feb, 2021 \\ \hline
**6** & [46] & Classification & Jan, 2019 & 26 & [152] & Classification & Jun, 2021 \\ \hline
**7** & [153] & Classification & Apr, 2019 & 27 & [154] &
\begin{tabular}{c} Change \\ Detection \\ \end{tabular} & Jun, 2021 \\ \hline
**8** & [24] & Classification & Jun, 2019 & 28 & [127] & Classification & Aug, 2021 \\ \hline
**9** & [19] & \
\begin{tabular}{c} Wetland \\ Inundation \\ Analysis \\ \end{tabular} & Jul, 2019 & 29 & [155] &
\begin{tabular}{c} Change \\ Detection \\ \end{tabular} & Sep, 2021 \\ \hline
**10** & [156] & Classification & Oct, 2019 & 30 & [157] &
\begin{tabular}{c} Classification \\ and Change Detection \\ \end{tabular} & Oct, 2021 \\ \hline
**11** & [158] & Change Detection & Oct, 2019 & 31 & [159] &
\begin{tabular}{c} Wetland \\ Vegetation \\ Mapping \\ \end{tabular} & Oct, 2021 \\ \hline
**12** & [160] & Classification & Jan, 2020 & 32 & [161] &
\begin{tabular}{c} Change \\ Detection \\ \end{tabular} & Nov, 2021 \\ \hline \hline \end{tabular}
\end{table}