## 3 Results
### Temporal and Spatial Resolution  
The following section analyzes the temporal and spatial resolution of RS data in the reviewed articles. Figure 8 illustrates the time periods covered by the RS data in relation to the publication dates of the studies, providing a visual comparison of data collection timelines and the timing of the study's release. Each study was classified according to the best spatial resolution of the sensors used, with the following categories: very high spatial resolution (below 10 m), high-resolution (10 to below 30 m), medium resolution (30 to below 50 m), medium-low resolution (50 to below 1000 m), and coarse resolution (above 1000 m). UAV dominate the very high-resolution category. The high-resolution category is defined by a threshold below 30 m, distinguishing between Sentinel-2 (10-20 m) and Landsat (30 m). MODIS falls within the medium-low group, while AVHRR is categorized under coarse resolution. The temporal aspect of RS data is grouped into four categories: mono-temporal (single observation), multi-temporal (with multiple observations within one year), multi-temporal (multiple years with single observations), and time-series (with at least eleven timesteps and multiple recordings over several years). The timeline graph shows that a large number of mono-temporal studies are categorized as high to very high-resolution, while most UAV studies are mono- or multi-temporal (intra annual). In terms of investigated time periods, 39.4% are time-series, 31.9% are multi-temporal intra-annual, 15.6% are multi-temporal inter annual studies, and 13.1% are mono-temporal studies. For example, time series studies include [PERSON]'s work [80], which utilizes AVHRR data from 1982 to 1990 to create ten-day composite NDVI values, and [PERSON]'s 2022 [81] study, which employs a mix of MODIS and AVHRR data from 1981 to 2021. Multi-temporal (inter-annual) studies involving multiple years of single observation can be seen in [PERSON]'s research [83], which used Landsat images from 1984, 1994, 2007, and 2017 to monitor land use and soil salinity changes in coastal landscapes of Senegal. Similarly, [PERSON]'s study [84] assessed changes in the agricultural irrigated area in Burkina Faso using Landsat images from 1987, 2000, and 2005. In contrast, multi-temporal studies (intra-annual) with multiple observations in a single year are exemplified by [PERSON]'s article [64], which integrates six RapidEye scenes and six TerraSAR-X images to enhance crop discrimination in the Vea watershed in the Sudanian Savanna. Lastly, mono-temporal studies focus on a single point in time, such as [PERSON]'s [85] UAV mission in October 2018, which evaluated the influence of individual standing trees in the Senegalese parklands on millet yields. Figure 9 shows the study area in relation to the pixel size. For studies using multiple datasets, the dataset with the highest spatial resolution has been considered in this analysis. The study area size is categorized as local (<100 km\({}^{2}\)), regional (>100 km\({}^{2}\) but smaller than district/federal level), federal/district level, national, and multinational. A total of 25.3% of the studies focus on a local-scale study area and 21% combining this local focus with a pixel size smaller than 30 m. Regional areas, defined as those greater than 100 km\({}^{2}\) but smaller than district/federal level are covered by 40.7% of the publications reviewed across all spatial resolution levels. Among these, 33.3% employ very high, high, or medium spatial resolution. As the study area size increases, the spatial resolution of the sensors used tends to increase analog. Nearly 10% (9.3%) of studies cover federal/district level,  
Figure 8: Overview of the time periods covered by the RS data in relation to the publication dates of the studies, providing a visual comparison of data collection timelines and the timing of the studies release. while 11.7% examine national-level areas and 13% investigate multinational area or cover whole West Africa.