The consistent monitoring of trees both inside and outside of forests is key to sustainable land management. Current monitoring systems either ignore trees outside forests or are too expensive to be applied consistently across countries on a repeated basis. Here we use the PlanetScope nanosatellite constellation, which delivers global very high-resolution daily imagery, to map both forest and non-forest tree cover for continental Africa using images from a single year. Our prototype map of 2019 (RMSE = 9.57%, bias = −6.9%). demonstrates that a precise assessment of all tree-based ecosystems is possible at continental scale, and reveals that 29% of tree cover is found outside areas previously classified as tree cover in state-of-the-art maps, such as in croplands and grassland. Such accurate mapping of tree cover down to the level of individual trees and consistent among countries has the potential to redefine land use impacts in non-forest landscapes, move beyond the need for forest definitions, and build the basis for natural climate solutions and tree-related studies.

The distribution of dryland trees and their density, cover, size, mass and carbon content are not well known at sub-continental to continental scales^1–14. This information is important for ecological protection, carbon accounting, climate mitigation and restoration efforts of dryland ecosystems^15–18. We assessed more than 9.9 billion trees derived from more than 300,000 satellite images, covering semi-arid sub-Saharan Africa north of the Equator. We attributed wood, foliage and root carbon to every tree in the 0–1,000 mm year⁻¹ rainfall zone by coupling field data¹⁹, machine learning^20–22, satellite data and high-performance computing. Average carbon stocks of individual trees ranged from 0.54 Mg C ha⁻¹ and 63 kg C tree⁻¹ in the arid zone to 3.7 Mg C ha⁻¹ and 98 kg tree⁻¹ in the sub-humid zone. Overall, we estimated the total carbon for our study area to be 0.84 (±19.8%) Pg C. Comparisons with 14 previous TRENDY numerical simulation studies²³ for our area found that the density and carbon stocks of scattered trees have been underestimated by three models and overestimated by 11 models, respectively. This benchmarking can help understand the carbon cycle and address concerns about land degradation^24–29. We make available a linked database of wood mass, foliage mass, root mass and carbon stock of each tree for scientists, policymakers, dryland-restoration practitioners and farmers, who can use it to estimate farmland tree carbon stocks from tablets or laptops.

In the West African Sahel, two paradoxical hydrological behaviors have occurred during the last five decades. The first paradox was observed during the 1968–1990s ‘Great Drought’ period, during which runoff significantly increased. The second paradox appeared during the subsequent period of rainfall recovery (i.e., since the 1990s), during which the runoff coefficient continued to increase despite the general re-greening of the Sahel. This paper reviews and synthesizes the literature on the drivers of these paradoxical behaviors, focusing on recent works in the West African Sahelo/Sudanian strip, and upscaling the hydrological processes through an analysis of recent data from two representative areas of this region. This paper helps better determine the respective roles played by Land Use/Land Cover Changes (LULCC), the evolution of rainfall intensity and the occurrence of extreme rainfall events in these hydrological paradoxes. Both the literature review and recent data converge in indicating that the first Sahelian hydrological paradox was mostly driven by LULCC, while the second paradox has been caused by both LULCC and climate evolution, mainly the recent increase in rainfall intensity.

Quantitative estimates of forage availability at the end of the growing season in rangelands are helpful for pastoral livestock managers and for local, national and regional stakeholders in natural resource management. For this reason, remote sensing data such as the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) have been widely used to assess Sahelian plant productivity for about 40 years. This study combines traditional FAPAR-based assessments with agrometeorological variables computed by the geospatial water balance program, GeoWRSI, using rainfall and potential evapotranspiration satellite gridded data to estimate the annual herbaceous yield in the semi-arid areas of Senegal. It showed that a machine-learning model combining FAPAR seasonal metrics with various agrometeorological data provided better estimations of the in situ annual herbaceous yield (R² = 0.69; RMSE = 483 kg·DM/ha) than models based exclusively on FAPAR metrics (R² = 0.63; RMSE = 550 kg·DM/ha) or agrometeorological variables (R² = 0.55; RMSE = 585 kg·DM/ha). All the models provided reasonable outputs and showed a decrease in the mean annual yield with increasing latitude, together with an increase in relative inter-annual variation. In particular, the additional use of agrometeorological information mitigated the saturation effects that characterize the plant indices of areas with high plant productivity. In addition, the date of the onset of the growing season derived from smoothed FAPAR seasonal dynamics showed no significant relationship (0.05 p-level) with the annual herbaceous yield across the whole studied area. The date of the onset of rainfall however, was significantly related to the herbaceous yield and its inclusion in fodder biomass models could constitute a significant improvement in forecasting risks of a mass herbaceous deficit at an early stage of the year.