# Inland Water Level Monitoring from Satellite Observations:
## 2 Methodological Framework
### Search and Selection Strategy  
For the analysis of satellite missions used for inland water level monitoring, five electronic literature databases were employed. We used Google Scholar, Scopus, ScienceDirect, IEEE Xplore, and Taylor & Francis databases to search English-language peer-reviewed scientific journals and proceedings. The searching period of the current scoping review paper is between 1 January 2018 and 31 December 2022, since until 2018 Sentinel-3 was operated in the OL tracking mode in specific areas of interest, having a low number of hydrological targets [24]. Then the research team established the exclusion and inclusion criteria for the evaluation of the literature. We excluded studies characterized as \"_grey literature_\" including presentations, books or book chapters, commentary, preprints, thesis, abstracts, as well as report deliverables. Based on pre-determined research questions, we searched for the best combination of keywords relevant to the research topic. The multiple keywords combined with the appropriate Boolean operators were queried in the aforementioned five literature databases. The multiple keywords with the Boolean operators are presented in Table 1. Then, all results were uploaded to Mendeley software (Version 1.19.4) [43] to remove the duplicates between the literature databases. Afterward, the review team accomplished a two-level screening (abstract screening and full-text screening). The systematic review software, Swift Review [44], was used to provide tools for assistance in the literature prioritization during the screening. Swift-Review software uses the latent Dirichlet allocation (LDA) model to probabilistically assign the publications to the topic. The papers assigned to Swift Review were manually labeled as \"Relevant\" or \"Non-Relevant\" and were used to predict the probability of each publication as \"Relevant\" [45]. This stage of screening includes the title and the abstract of each document. Swift Review uses a machine learning method to compute a score which attempts to prioritize the documents such that the documents similar to the \"Included\" papers are ranked at the top of the list. Thus, the most \"representative\" publications get the highest scores and are presented at the top of the list. Having completed the triage of documents in \"Relevant\" and \"Non-Relevant\" and performing the priority ranking, the second stage of screening has to be performed. The second stage includes the evaluation of predicted ranking scores, denoting the threshold of relevance. For the objectivity of data sample, the training and test documents were split to a 50-50 ratio. For the evaluation process, we denoted as \"Relevant\" the corpus of a prioritization ranking \begin{table}
\begin{tabular}{c c} \hline \hline
**Question Components** & **Search Keywords** \\ \hline \multirow{4}{*}{Inland Water Level} & (”Inland Water Level” OR “Inland Water Monitoring” OR “Inland Water Monitoring” OR “Matllite”) \\  & (”Monitoring Inland Water”) AND \\ \cline{1-1}  & (Satellite)–”Monitoring Inland Water Quality”–”Water Quality Monitoring” \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Group of the selected keywords used in the five selected databases. score of 0.6 or higher. Also, the confusion matrix based on visual inspection analysis, as well as the sensitivity score are analyzed to evaluate the results. The sensitivity score, depicted in Equation (1) is a measure of how many 'Relevant' documents are selected, and it is calculated as the proportion of True Positive with True Positive and False Positive documents. TP stands for 'Relevant' publications correctly identified as Relevant, and FP stands for 'Non-Relevant' publications incorrectly identified as Relevant. \[Sensitivity=\frac{TP}{TP+FP} \tag{1}\]  
Afterward, the dataset was updated including the validated \"Relevant\" articles and the remaining articles, which were denoted as \"Included\" during the first stage of screening.