# Monitoring the Dynamics of Ephemeral Rivers from Space: An Example of the Kuiseb River in Namibia
## 3 Data Used for This Study
### Sentinel Missions
#### 3.1.1 Multispectral Imagery from Sentinel-2  
Multispectral images came from the Sentinel-2 mission, which is comprised two identical satellites, 2A and 2B, that were launched on June 2015 and March 2017, respectively, to continue SPOT (Satellite pour l'Observation de la Terre) for Earth observation [25; 26]. The on-board MSI (Multi Spectral Imagery) sensors acquire data in 13 spectral bands, from visible to shortwave infrared (SWIR), with a spatial resolution from 10 to 60 m. Detailed characteristics of the Sentinel-2 mission are provided on [[https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-2](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-2)]([https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-2](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-2)) (accessed on 10 September 2020). The two satellites were on the same level, but phased at 180\({}^{\circ}\), to acquire images every 5 days from the same location. However, the quality of the images is strongly dependant on the weather.  
Sentinel-2 Level 2A images are available for free download from [[https://peps.cnes.fr/rocket/#/home](https://peps.cnes.fr/rocket/#/home)]([https://peps.cnes.fr/rocket/#/home](https://peps.cnes.fr/rocket/#/home)) (accessed on 14 September 2020). Images were atmospherically corrected using the MAJA algorithm (MACCS ATCOR Joint Algorithm), which is a processing chain developed by the CNES (Centre National d'Etudes Spatiales) and CESBIO (Centre d'Etudes Spatiales de la Biosphere) to detect and apply atmospheric corrections [27]. This MAJA algorithm has shown good performance and accuracy in previous studies [28].  
A total of 123 cloud-free images were acquired between June 2015 and December 2021, covering the Kuiseb River--and were analyzed. Spectral indexes were computed to monitor the vegetation and moisture variations through time. A spectral index corresponds to a ratio between two or more bands, which allows the standardization of data and the reduction of environmental and illumination effects. One of the most used spectral indexes is the NDVI (Normalised Difference Vegetation Index), which is defined by the following equation (Equation (1), [29]):  
\[\mathrm{NDVI}=\frac{\mathrm{NIR}-\mathrm{Red}}{\mathrm{NIR}+\mathrm{Red}} \tag{1}\]  
with:  
- NIR band: central wavelength 842 nm (spectral band n\({}^{\circ}\)8)  
- Red band: central wavelength 665 nm (spectral band n\({}^{\circ}\)4)  
In order to study water or moisture variations, we also considered the NDMI index (Normalised Difference Moisture Index), which is defined by the following equation (Equation (2), [30]):  
\[\mathrm{NDMI}=\frac{\mathrm{NIR}-\mathrm{SWIR}}{\mathrm{NIR}+\mathrm{SWIR}} \tag{2}\]  
with:  
- NIR band: central wavelength 842 nm (spectral band n\({}^{\circ}\)8)  
- SWIR band: central wavelength 1610 nm (spectral band n\({}^{\circ}\)11)  
However, for the purpose of our case, these two indexes were used to make a qualitative assessment to detect general patterns and variations. They could not be used to exactly quantify the vegetation and/or moisture contents.