## 3 Results
### Focus of the Studies
(2021) [120] employed UAV-based vegetation indices to estimate the temporal dynamics of leaf area, shoot biomass, and plants height within a sorghum panel representative of the genetic and phenotypic diversity of African sorghum under contrasted water situations. Furthermore, [PERSON] et al. (2022) [147] evaluated the accuracy of the 2019 Digital Earth Africa cropland masks, developed an LAI retrieval method from Planet surface reflectance data, created a maize classification dataset for Ghana, and examined the relationship between maximum LAI and crop yield. The findings indicate that linking the yield with EO-derived metrics like maximum LAI is challenging due to significant variability in yield within individual fields. Better co-located yield and LAI measurements could improve the understanding of uncertainties in mapping canopy variables to LAI. [PERSON] et al. (2024) [127] calibrated and validated successfully sorghum varieties LAI values estimated from UAV at different growing seasons in Senegal and Mali. [PERSON] et al. (2024) [128] assessed the LAI at the seedling stage after conducting a field experiment with rainfed groundnut based on Sentinel-2 data. In future work, they plan to include high-resolution UAV hyperspectral, RGB, and multispectral image data in the modeling process. Lastly, [PERSON] et al. (2024) [12] addressed the limited focus on the fraction of green vegetation (FCover) due to challenges in collecting reference data. The study introduces a novel framework that effectively combines FCover data from UAVs and Sentinel-2 images to estimate FCover for millet at landscape scale in Senegal's groundnut basin. Seventeen studies can be related to detecting agricultural management practices. [PERSON] et al. (2023) [132] calculated the Normalized Difference Water Index over nine years to identify flood-prone areas and assess their suitability for flood residual water cultivation based on crop reference evapotranspiration. [PERSON] et al. [133] (2020) investigated irrigation management and performance of the Kou Valley using several Landsat-derived parameters to reveal spatial differences in crop areas related to water stress, identifying low-performing water management during early and late phenological stages. [PERSON] et al. (2012) [136] similarly analyzed irrigation, drainage, and productivity patterns in Mauretania. [PERSON] et al. (2019) [1] used the PhenoRice algorithm with MODIS imagery to track rice cultivation in the Senegal River Valley, mapping variations in cultivated areas and phenological metrics. [PERSON] et al. (2018) [134] demonstrated the use of high-resolution satellite and UAV imagery to assess yield variability and fertilizer response. [PERSON] et al. (2017) [10] evaluated RS's ability to detect crop status in Mali's rainfed systems, focusing on NDVI sensitivity to fertilizer treatments on cotton, millet, sorghum, maize, and peanut. [PERSON] and [PERSON] (2015) [135] used RS to distinguish organic from non-organic cotton practices to aid an organic cotton certification process. [PERSON] and [PERSON] (2010) [137] presented an application based solely on high-resolution RS data to analyze strategic and diagnostic performance indicators, offering insights for improving the overall system performance. [PERSON] and [PERSON] (2007) [138] monitored the impact of agricultural intensification, LULUC trends, and soil-water conservation practices. [PERSON] et al. (2019) [37] measured the impact of farming practices and landscape context on the control of the millet head miner by natural enemies, hypothesizing that landscape diversity positively influences pest regulation. [PERSON] et al. (2023) [139] assessed the global market effects on land use sustainability and the magnitude of the regime shift from subsistence-oriented agro-pastoral ecosystems into increasingly market-oriented ones. [PERSON] et al. (2021) [38] analyzed agricultural digitization in Mali, while [PERSON] et al. [140] (2019) monitored forest to agricultural and agro-forestry transitions in Togo within the REDD+ framework.