## 3 Data and Methods
### Development of Training Materials
The results include the binarized images. Depending on the exercise, their results were later complemented with scripts in R to perform exploratory analyses of Sentinel data (Figure 4b), or for advanced analysis such as predicting atmospheric pollutants with Autoregressive Integrated Moving Average (ARIMA) [38], or deriving deforestation maps using land cover data, as well as their accuracy metrics (Table 3). These R scripts were developed in posit.Cloud, which allowed the participants to create, share, and monitor R projects in the exercises without the need to install software locally. Some of the practical exercises included the use of QGIS for cartographic analysis, such as when participants were asked to derive validation samples from Sentinel-2 RGB composites or explore and derive statistics from the rapid mapping of CEMS data from the Ecuador 2016 earthquake [39]. Similarly, despite the field trip being canceled to risk zones related to the Cotopaxi volcano, a dedicated exercise using the QFIELD mobile app was conducted to demonstrate its use with the GHS built-up surface map [40] as a field data tool for emergency management purposes. For methodologies specialized in decision-making, such as the Analytical Hierarchy Process (AHP) [41], exercises were integrated with some theoretical background and a survey to qualify the importance of parameters such as soil moisture, biomass carbon density, NDVI, and land surface temperature for biophysical fire risk assessment. These parameters were used to derive weights, construct a model, and compare it to burned areas obtained from CLMS [42]. Similarly, for methodologies on distribution models, where species data were required, the open-data platform Global Biodiversity Facility (GBIF) [43] was also explained, along with some concepts of taxonomy, distribution models, and procedures for downloading and cleaning species data. As it was necessary to rescale climate models obtained from C3S (e.g., Agrometeorological indicators, ERA5-Land monthly averages [44; 45]), a routine based on the random forest algorithm described in [PERSON] et al. [46] was adapted for this procedure. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Session Name, Objectives and Study Areas** & **Tasks** & **Outcomes** & **References** \\ \hline CAMS: processing Sentinel-5P images for assessing and forecasting atmospheric pollution in continental Ecuador with ARIMA & 1. Processing Sentinel-5 time series products & Differentiate atmospheric pollutants, identify regions with the highest pollution levels, experience ARIMA for forecasting & [38; 39] \\ \hline CLMS: processing Sentinel-1 and 2 images to generate maps of deforestation of the Ecuador during the resulting cartography & 1. Processing Sentinel 1 and 2 time series for land cover mapping 2. Derive deforestation maps for different periods & Observe synergy effects of Sentinel 1 and 2 for land cover classification, experience challenges in validating time series deforestation maps & [42] \\ \hline CLMS: processing Sentinel-1 and 2 images to generate the data of deforestation of the Ecuador during the resulting cartography & 2. Deviving deforestation maps for different periods & 1 and 2 for land cover classification, experience challenges in validating time series deforestation maps & [42] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sessions, objectives, study areas, tasks, and outcomes of the practical exercises in the Ecuador training workshops. As data from CSS were not available, principles of synthetic aperture radar (SAR) and Sentinel-1 mission data acquisition were explained to give context and interpret results of the Sentinel 1-VIIRS GEE routine for identifying suspicious infrastructures. These scripts, along with those for API access and data analysis, are available upon request to the authors.