## 3 Results
### Focus of the Studies
[PERSON] and [PERSON] (2018) [87] mapped farms that employ water harvesting or conventional agricultural techniques in Burkina Faso with Quickbird and WorldView imagery. [PERSON] et al. (2017) [77] examined floodplain management in Nigeria emphasizing the role of extension workers to teach farmers agricultural best practice. [PERSON] et al. (2015) [141] linked declining cotton yields in southern Mali to fertilizer misallocation, employing RS data combined with in-depth farmer interviews. Using the ALADYN model, [PERSON] et al. (2015) [142] highlighted the unsustainability of traditional farming practices shown through periodic soil fertility declines to levels too low to allow for cultivation. The \"Agroforestry\" category can be subdivided into four areas: (i) mapping of cocoa and oil palm; (ii) the analyses of fruit trees such as bananas, cashews, or mango; (iii) assessment of the above-ground carbon storage with focus on plantations; and (iv) mapping of semi-arid parklands with their ecological implications. Cocoa and palm oil agroforestry systems, promoted as a strategy to mitigate deforestation, require precise delineation to establish a robust monitoring system [79; 90; 145]. Additionally, parameter retrieval such as the age of palm oil or height of the trees is crucial for sustainability assessments, carbon mapping, yield projections, and precision agriculture [46; 78; 193]. However, challenges such as the lack of high-resolution imagery, mainly due to the high cloud cover of tropical regions and the quality of validation data persist [88; 144]. [PERSON] et al. utilized 2.4 m resolution Planet basemaps, 0.5 m resolution aerial imagery, two newly developed deep learning algorithms, and extensive ground truth datasets to successfully create the first national map of cashew cultivation in Benin, characterizing its expansion between 2015 and 2021 [149]. In addition, [PERSON] et al. [40] developed a banana disease classification system and [PERSON] et al. [147] further strengthened an approach to map bananas under mixed-complex African landscapes. Furthermore, studies by [PERSON] et al. [146] and [PERSON] et al. [148] focused on mapping the phenology of mango trees and assessing their yield on orchard level, respectively, contributing to a comprehensive understanding of fruit tree dynamics in the region. Agroforestry systems present viable solutions for climate change due to the above-ground biomass (AGB) sustained by their tree components. Accurate, spatially explicit estimations of AGB within these systems are essential for reporting emission reduction efforts. However, several factors, such as the spatial distribution, structural diversity, composition, and their varying extents pose challenges to RS techniques. Five studies focus on assessing the RS potential of spaceborne optical, SAR, and LiDAR data for AGB estimation in agroforestry systems in West Africa [79; 89; 150; 151; 152]. Smallholder farming in agroforestry parklands is the predominant agricultural system in the Sudano-Sahelian zone of West Africa, serving as the subsistence base for a large proportion of the population. These parklands are agricultural landscapes defined by the coexistence of scattered trees within cultivated fields. Studies by [PERSON] et al., [PERSON] et al., and [PERSON] et al. [68; 85; 151] examine the influence of parkland trees on crop yields at the landscape scale, considering a range of different tree cover characteristics. Other research focuses on mapping the abundance of the multi-purpose agroforest _Faidherbia albida_ trees [153; 155] or comparing machine learning techniques to map different tree species within the agroforestry landscapes of West Africa [156]. A total of 18 publications fall under the category \"LULUC with crop specific focus\", showing no clear temporal pattern or trend. However, studies in this category have to go beyond generating land use maps or tracking land use change over time. Instead, they emphasize specific crop areas or small-scale croplands, which offer more detailed insights.