A long-standing challenge in studying the global carbon cycle has been understanding the factors controlling inter–annual variation (IAV) of carbon fluxes related to vegetation photosynthesis and respiration, and improving their representations in existing biogeochemical models. Here, we compared an optimality-based mechanistic model and a semi-empirical light use efficiency model to understand how current models can be improved to simulate IAV of gross primary production (GPP). Both models simulated hourly GPP and were parameterized for (1) each site–year, (2) each site with an additional constraint on IAV (CostIAV), (3) each site, (4) each plant–functional type, and (5) globally. This was followed by forward runs using calibrated parameters, and model evaluations at different temporal scales across 198 eddy covariance sites. Both models performed better on hourly scale than annual scale for most sites. Specifically, the mechanistic model substantially improved when drought stress was explicitly included. Most of the variability in model performances was due to model types and parameterization strategies. The semi-empirical model produced statistically better hourly simulations than the mechanistic model, and site–year parameterization yielded better annual performance for both models. Annual model performance did not improve even when parameterized using CostIAV. Furthermore, both models underestimated the peaks of diurnal GPP in each site–year, suggesting that improving predictions of peaks could produce a comparatively better annual model performance. GPP of forests were better simulated than grassland or savanna sites by both models. Our findings reveal current model deficiencies in representing IAV of carbon fluxes and guide improvements in further model development.

Forest and non-forest trees and shrubs (hereafter collectively referred to as trees), are the basis for the functioning of tree-dominated ecosystems, and are regularly monitored at country scale via forest inventories. However, traditional inventories and large-scale forest mapping projects are expensive, labour-intensive and time-consuming, resulting in a trade-off between the details recorded, spatial coverage, accuracy, regularity of updates, and reproducibility. Also, forest inventories typically do not account for individual trees outside forests, although these trees play a vital role in sustaining communities through food supply, agricultural support, among other benefits. Moreover, the alarming rate of tree cover loss resulting from different natural and human-induced processes has brought both political and economic motives to attract efforts for landscape restoration especially in Africa. Nevertheless, currently, there is no accurate and regularly updated monitoring platform to track the progress and biophysical impact of such ongoing initiatives. Recent approaches counting trees in satellite images in Africa used very costly commercial images, were limited to isolated trees in savannas excluding small trees, and did not cover other complex and heterogeneous ecosystems such as forests. Here, we make use of novel deep learning techniques and publicly available aerial imagery, and introduce an accurate and rapid method to map the crown size, number of trees inside and outside forests, and corresponding carbon stock, regardless of tree size and ecosystem types in Rwanda. The applied deep learning model follows a UNet architecture and was trained using 67,088 manually labeled tree crowns. We mapped over 200 million individual trees in forests, farmlands, wetlands, grasslands, and urban areas, and found about 67.2% of the mapped trees outside forests. An average tree density of 94.6 and 70.8 trees per ha, and average crown size of 38.7 m2 and 15.2 m2 were mapped inside and outside forests, respectively. In savannas we found 64 trees per ha with an average crown size of 15.6 m2. In farmlands we found 79.6 trees per ha with an average crown size of 16.3 m2. We expect methods and results of this kind to become standard in the near future, enabling tree inventory reports to be of unprecedented accuracy.

Afforestation and land use changes have turned Southern China into one of the largest carbon sinks globally, which sequesters carbon from the atmosphere thus mitigating climate change. However, forest growth saturation and available land that can be forested limit the longevity of this carbon sink, and while a plethora of studies have quantified vegetation changes over the last decades, the remaining carbon sink potential of this area is currently unknown. Here, we train a model with multiple predictors characterizing the heterogeneous landscapes of Southern China and predict the carbon carrying capacity of the region for 2002-2017. We compare observed and predicted carbon density and find that during two decades of afforestation, 2.34 Pg C have been sequestered between 2002 and 2017, and a total of 5.32 Pg carbon can potentially still be sequestrated. This means that the region has reached 75% of its carbon carrying capacity in 2017, which is 12% more than in 2002, equal to a decrease of 0.83% per year. We identify potential afforestation areas that can still sequester 2.39 Pg C, while old and new forests have reached 87% of their potential with 1.85 Pg C remaining. Our work locates areas where vegetation has not yet reached its full potential but also shows that afforestation is not a long-term solution for climate change mitigation.

1.Herbaceous aboveground biomass (HAB) is a key indicator of grassland vegetation and indirect estimation tools, such as remote sensing imagery, increase the potential for covering larger areas in a timely and cost-efficient way. Structure from motion (SfM) is an image analysis process that can create a 3D model from a set of images. 2: Computed from UAV and ground camera measurements, the SfM potential to estimate the herbaceous aboveground biomass in Sahelian rangelands was tested in this study. Both UAV and ground camera recordings were used at three different scales: temporal, landscape and national (across Senegal). All images were processed using PIX4D software and were used to extract vegetation indices and heights. 3: A random forest algorithm was used to estimate the HAB and the average estimation errors were around 150 g.m-² for fresh mass (20% relative error) and 60 g.m-² for dry mass (around 25% error). A comparison between different datasets revealed that the estimates based on camera data were slightly more accurate than those from UAV data. 4:It was also found that combining datasets across scales for the same type of tool (UAV or camera) could be a useful option for monitoring HAB in Sahelian rangelands or in other grassy ecosystem.