## 3 Data and Methods
### Development of Training Materials  
Before the Ecuadorian workshop began, instructions were given to the participants to install the required software in their computers to follow the demonstration of downloading products and perform the practical exercises, which included the following:  
* QGIS 3.28-Firenze (long-term release) and QFIELD 4.2.0 plugin;
* Sentinel Application Platform (SNAP) 9.0.0;
* Anaconda 2.4.2 and Spyder 5.4.1. The participants were also requested to register on the following cloud-based platforms:
* Google Earth Engine (GEE);
* posit.Cloud for Rstudio. Additionally, they were asked to install the following mobile apps for special exercises:
* QFIELD 3.1.1-Borneo app;
* Google Jamboard app. In the case of the demonstration of downloading products, the participants were asked to follow the facilitator's steps during the use of Copernicus's services' GUIs. This involved explaining the process of registering to the participants and the catalog of products before downloading. For the APIs, the participants were first explained how to use Anaconda, and they were asked to create a dedicated environment for Copernicus APIs. This environment was based on Python version 3.10.13, together with the installation of packages cdsapi 0.6.1 and terracataloguesclient 0.1.18. A Python routine in the Copernicus APIs' user manual [37] was adapted, which requires configuring access to C3S, CAMS, and CLMS by creating files with API credentials to make requests and list and download datasets in a user-defined directory. A complete list of the GUIs and APIs analyzed in the workshop can be seen in Appendix C.  
With regard to the practical exercises, different platforms were considered. In the case of GEE, scripts for processing Sentinel 1, 2, and 5P time series data were implemented. Figure 4a shows an example of Sentinel-5P processing in GEE. Figure 4: Outputs of GEE and posit.Cloud exercises for the CAMS session: (**a**) Sentinel-5P SO2 average composite over Cotopaxi volcano and its SO2 plume, with column density values from 0.000018 to 0.00043 mole/m2 for the period 30 July 2019 to 31 December 2023. Data at 1 km resolution uses a green-to-red gradient to highlight SO2 concentrations. (**b**) Time series of Sentinel-5P SO2 (mole/m2) for various cantons around Cotopaxi volcano. Dashed vertical line indicates the Cotopaxi recent eruption date (21 October 2022), while the horizontal dashed line marks the 0.0005 mole/m2 threshold for high SO2 levels. These scripts can be summarized as follows:  
* Sentinel-5P processing: This script processes Sentinel-5P time series data to extract atmospheric products (e.g., SO\({}_{2}\), NO\({}_{2}\)) for a specific area and time period. It composites the data based on a user-defined percentile, generates time series images, and exports them with metadata for analysis. * Sentinel 1-2 land cover classification: This script processes Sentinel-1 and Sentinel-2 data to classify land cover in a specific study area. Users can define parameters such as the time period, cloud cover thresholds, and classification options. The script calculates indices (e.g., NDVI, NDWI) from Sentinel-2 and uses radar backscatter (VH and VV) from Sentinel-1. These are reduced into 30 th, 50 th, and 70 th percentile composites, combined, and used to train a random forest classifier based on an ancillary land cover map for prediction. The output includes RGB composites for validation purposes and classification results. * Sentinel 1-VIIRS Metrics Extraction: This script processes Sentinel-1 and VIIRS data to derive key metrics for identifying suspicious infrastructures within a specified area and time period. Users can set parameters such as time period, polarization, orbit type, binarization threshold, and focal max filter size. The script applies thresholds to binarize Sentinel-1 backscatter and generates focal max images to highlight corner reflectors or flat areas. It also processes VIIRS data to highlight nighttime light spots and integrates these with the Sentinel-1 outputs.