## 3 Results
### Focus of the Studies
[99] highlighted the variability within and between sorghum cultivars and harvest index (ratio of grain to total crop biomass), emphasizing the promise of growing high-resolution satellite data availability for monitoring plots. Sentinel-1 and 2 data were employed by [PERSON] et al. [106] to map irrigated crops around Lake Bam, estimating productivity through regression analysis of fragmented small-scale farms with seasonal changes. [PERSON] et al. [105] explored methods for anticipating sugarcane production prior to harvest in Cote d'Ivoire using cropping practice, meteorological, and satellite data. [PERSON] et al. [97] assessed the potential of deep learning approaches with SAR and optical data compared to traditional regression methods, aiming to enhance crop yield estimation with limited reference data. In addition, yield forecasts throughout the cropping period assisted in determining how well RS-based modeling can help accelerate the collection of crop production information in the study site. [PERSON] et al. [98] highlight the use of UAV-derived vegetation indices to understand yield variability at the field scale and the importance of very high-resolution imagery for rapid agronomic monitoring and robust decision support. [PERSON] et al. (2022) [81] investigated out-of-sample forecasting skills and evaluated the benefits of enhanced temporal resolution and non-standard EO data to improve grain-yield forecasting and food security warnings across Africa. [PERSON] and [PERSON] (2023) focused on improving maize yield predictions under different management practices using a Random Forest model [95], while [PERSON] et al. (2023) [96] aimed to develop a valid crop yield model based on a three-year in situ dataset in northwestern Burkina Faso. The aim was to reduce the need for extensive ground data collection while ensuring accurate household-level yield estimates through Sentinel-2 satellite-based crop yield models [96]. A total of 26 publications focus on the mapping of cropland areas and crop types in West Africa. In their 2011 study, [PERSON] et al. [112] generated unbiased estimates of crop area during Niger's principal crop-producing season in 2005, using medium-resolution satellite imagery along with high-resolution imagery. [PERSON] et al. (2010) [25] examined historical and contemporary aerial photographs and satellite images to investigate changes in land use and cover, as well as soil degradation in Nigeria and Benin, revealing an expansion of agricultural land at the expense of forests, shrubs, fallow areas, and uncultivated land. [PERSON] et al. (2021) [107] evaluated the accuracy of mapping maize, potato, and mixed cropping systems in Nigeria by employing the complete dataset from Sentinel-2, while also analyzing the distribution of various crop types across the study region and among different field sizes. In 2018, [PERSON] et al. [86] developed a replicable sampling methodology using very high-resolution optical imagery to explore how landscape composition influences crop pest incidence and biological control, specifically focusing on the millet head miner. [PERSON] et al. (2018) [110] concentrated on the delineation of maize cultivation areas within complex cropping systems in Ghana, employing UAV imagery for their analysis. Expanding the scope, [PERSON] et al. (2012) [45] and [PERSON] et al. (2018) [100] conducted a detailed performance assessment of various global landcover datasets, such as GLC2000, GLOBCOVER, MODIS V05, and ECOCLIMAP-II for the AMMA zone to accurately map the cultivated area in Sahelian West African countries. They employed very high-resolution imagery as reference data. Furthermore, [PERSON] et al. applied various methodological approaches to map fragmented landscapes, for instance, data mining based on MODIS or utilizing coarse resolution satellite-derived metrics (spectral, textural, temporal and spatial) to assign pixels to defined crop production system in Mali [70,71]. In another study in Mali, [PERSON] et al.