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Enhancing food security in the Sahel through nature-based solutions is urgent given population growth, resource scarcity and climate change. Traditional agroforestry parklands are a farmer- and nature-based widespread form of ecological intensification which randomly integrates trees into crop fields. While most studies estimating crop yields in agroforestry have been conducted in controlled experimental settings, few have addressed the inherent variability in such highly heterogeneous systems. Thus, the purpose of this study is to benefit from a UAV-based proxy-sensing and machine learning approach to address the variability of pearl millet grain yield, according to the distance to randomly distributed trees in a traditional agroforestry system dominated by Faidherbia albida (i.e. groundnut basin of Senegal). 21 vegetation indices (VIs), 32 normalized difference texture indices (NDTIs) derived from multispectral drone images, and normalization variables for radiative conditions were used with yield data collected in 15 plots (around 1 ha each) and subplots (15 m 2 each) displayed at 3 distances from the tree over five cropping seasons (2018 - 2022). In this context, the optimal phenological stage was determined for predicting pearl millet grain yield, which proved to be the pre-heading period. This period was used as the basis for our machine learning model training dataset in the subplots. Two models, Random Forest (RF) and Gradient Boosting Machine (GBM) were compared by combining VIs, NDTIs and normalization variables. GBM was the best-performing model, explaining 78% of observed pearl millet yield variability over five years in the subplots, with a RMSE of 16 g. m −2 . This study revealed that NDTIs calculated from red and green bands were more influential for yield estimation than those based on near-infrared. These results were subsequently used to predict yield in all plots, resulting in a mean relative error of 17.5% between yields estimated by the farmers

In recent decades, remote sensing has been shown to be useful for crop cover monitoring over smallholder agricultural landscapes, such as agroforestry parklands. However, the fraction of green vegetation cover (FCover) has received little attention. Indeed, the collection of FCover ground data representative of the within-field heterogeneity is time-consuming. Thus, this article aims to bridge this gap by proposing an original methodological framework combining FCover data derived from unmanned aerial vehicle (UAV) and Sentinel-2 (S2) images for estimating millet FCover at the landscape scale in an agroforestry parkland of the groundnut basin of Senegal during the 2021 and 2022 cropping seasons. UAV-based FCover was computed over a 3 m × 3 m grid using a thresholding approach for six dates over the cropping seasons and then used as ground observation for the upscaling of millet FCover at the landscape scale with S2 data. Various spectral vegetation indices and textural features were derived from S2, and several modeling approaches based on machine learning algorithms were benchmarked. Our results showed that the modeling approach using the full-time series in combination with a random forest algorithm was able to explain 73% (root mean square error = 12.13%) of the UAV-FCover variability after validation in 2021 and 2022. In addition, UAV images are suitable for consistent monitoring of millet FCover over heterogeneous agricultural landscapes by training S2 satellite images. To further check its robustness, this approach should be tested for different crops and practices across a variety of agricultural landscapes in sub-Saharan Africa.