As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0.92–2.15, NRMSD = 40–67%, bias = −0.64–0.96) and CCC (RMSD = 0.27–1.27 g m⁻², NRMSD = 64–84%, bias = −0.17–0.89 g m⁻²). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0.88–1.64, NRMSD = 27–64%, bias = −0.78–−0.13) and CCC (RMSD = 0.30–0.87 g m⁻², NRMSD = 52–73%, bias = 0.03–0.42 g m⁻²) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation.

Identifying the drivers of ecosystem dynamics, and how responses vary spatially and temporally, is a critical challenge in the face of global change. Grasslands in sub‐Saharan Africa are vital ecosystems supporting biodiversity, carbon storage, and livelihoods through grazing. However, despite their importance, the processes driving change in these systems remain poorly understood, as cross‐scale interactions among drivers produce complex, context‐dependent dynamics that vary across space and time. This is particularly relevant for woody vegetation dynamics, which are often linked to degradation processes (e.g., woody encroachment), with consequences for biodiversity, forage availability, and fire regimes. Here, we used satellite data and structural equation models to investigate the effects of rainfall, temperature, fire, and population density on woody vegetation dynamics in four African grassland regions (the Sahel grasslands, Greater Karoo and Kalahari drylands, Southeast African subtropical grasslands, and Madagascar) during 1997–2016. Across all regions, rainfall was consistently positively correlated with increased woody vegetation, while higher temperatures were associated with decreased woody vegetation, suggesting that water availability promotes woody plant growth, whereas rising aridity limits it. Unexpectedly, fire had a negative effect on woody cover only in the Greater Karoo and Kalahari drylands, while in Madagascar, higher temperatures and greater population density reduced fire; yet these relationships did not translate into significant indirect effects on woody vegetation. These findings illustrate the complex ways by which environmental and anthropogenic drivers shape woody vegetation dynamics in grasslands across sub‐Saharan Africa. Compared to savannas, fire plays a weaker and more region‐specific role in grasslands, where its feedback with woody cover is less consistent. The opposing effects of rainfall and temperature may currently constrain woody expansion, but climate change could disrupt this balance and further weaken fire's limited regulatory role. These differences highlight the need for management strategies tailored to the distinct climate–vegetation dynamics of grassland systems.

Understanding the drivers of ecosystem dynamics, and how responses vary spatially and temporally, is a critical challenge in the face of global change. Here we used structural equation models and remote sensing datasets to understand the direct and indirect effects of climatic, environmental, and anthropogenic variables on woody vegetation dynamics across four grasslands regions (i.e., Sahel grasslands, Greater Karoo and Kalahari drylands, Southeast African subtropical grasslands, and Madagascar) of sub-Saharan Africa. We focus on African grasslands given their importance for biodiversity and ecosystem services, the lack of clarity on how they are likely to respond to changes in disturbances, and how such responses vary geographically. This is particularly the case of grass-dominated ecosystems – the focus of our study – rather than more mixed grass-tree regions (e.g., savannas). Rainfall (β = 0.148 [-0.111, 0.398]) and temperature (β = -0.109 [-0.387, 0.133]) showed consistently opposing effects on woody vegetation (average standardised regression coefficients and 95% confidence interval range during 1997–2016) across the four bioregions. Other variables showed overall negligible effects including, for instance, dry season rainfall, soil moisture and, notably, fire. Other relationships were more context-dependent. Only Greater Karoo and Kalahari drylands showed a negative relationship between woody vegetation and fire (β = -0.031 [-0.069, 0.021]). Similarly, in Madagascar we observed strong negative effects of temperature (β = -0.429 [-1.215, -0.259]) and population density (β = -0.354 [-0.651, -0.015]) on burned area, yet these did not result in any significant indirect effects on woody vegetation. Our results clarify the contribution of environmental and anthropogenic variables in controlling woody dynamics at broad spatiotemporal scales and reveal that the widely documented negative feedback between fire and woody vegetation does not necessarily apply across all African grasslands.

Grasslands cover ca. 7% (2,100,000 km2) of the African continent. They provide a wide range of ecosystem services (e.g., forage, water, recreational spaces, carbon sequestration), and host large wildlife communities. Despite their importance, African grasslands are reported to be suffering from degradation and, perhaps more worryingly, have received little consideration within international policies (e.g., United Nations Sustainable Development Goals). A key issue at present is widespread woody plant encroachment (WPE), which it is shifting African grassland from a grassy- to a (less palatable) woody-dominated biome. However, the way climatic (e.g., precipitation, soil moisture) and non-climatic disturbances (e.g., fire, population density) affect WPE is still poorly understood, particularly at large spatiotemporal scales. Here we identified grasslands in sub-Saharan Africa according to the ESA Climate Change Initiative (CCI) land cover product and use vegetation optical depth (VOD) from passive microwave observations as a proxy for woody vegetation change between 1992 and 2011. We then use independent climatic (precipitation and soil moisture) and non-climatic (burn intensity, population change) data to assess how both spatiotemporal variations and interactions between climatic and non-climatic drivers controlled rates of VOD increase during 1992-2011. We consider not only annual precipitation, soil moisture, fire, and population data, but also integrated and lagged precipitation data (both up to five years ahead of VOD) in these models. Preliminary results reveal a large overall increase in woody vegetation in sub-Saharan Africa grasslands as well as considerable spatiotemporal variation in VOD change that is not due to climatic factors alone.