Description: The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground live biomass (AGB; dry mass) stored in forests with a spatial resolution of 1 ha. Using an extensive database of 110 897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high-carbon-stock forests with AGB 〉250 Mg ha−1, where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in the literature (426–571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the Global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country's national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps and identified major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the subtropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon, and socio-economic modelling schemes and provides a crucial baseline in future carbon stock change estimates. The dataset is available at https://doi.org/10.1594/PANGAEA.894711 (Santoro, 2018).

Description: So far, various studies have aimed at decomposing the integrated terrestrial water storage variations observed by satellite gravimetry (GRACE, GRACE-FO) with the help of large-scale hydrological models. While the results of the storage decomposition depend on model structure, little attention has been given to the impact of the way that vegetation is represented in these models. Although vegetation structure and activity represent the crucial link between water, carbon, and energy cycles, their representation in large-scale hydrological models remains a major source of uncertainty. At the same time, the increasing availability and quality of Earth-observation-based vegetation data provide valuable information with good prospects for improving model simulations and gaining better insights into the role of vegetation within the global water cycle. In this study, we use observation-based vegetation information such as vegetation indices and rooting depths for spatializing the parameters of a simple global hydrological model to define infiltration, root water uptake, and transpiration processes. The parameters are further constrained by considering observations of terrestrial water storage anomalies (TWS), soil moisture, evapotranspiration (ET) and gridded runoff (Q) estimates in a multi-criteria calibration approach. We assess the implications of including varying vegetation characteristics on the simulation results, with a particular focus on the partitioning between water storage components. To isolate the effect of vegetation, we compare a model experiment in which vegetation parameters vary in space and time to a baseline experiment in which all parameters are calibrated as static, globally uniform values. Both experiments show good overall performance, but explicitly including varying vegetation data leads to even better performance and more physically plausible parameter values. The largest improvements regarding TWS and ET are seen in supply-limited (semi-arid) regions and in the tropics, whereas Q simulations improve mainly in northern latitudes. While the total fluxes and storages are similar, accounting for vegetation substantially changes the contributions of different soil water storage components to the TWS variations. This suggests an important role of the representation of vegetation in hydrological models for interpreting TWS variations. Our simulations further indicate a major effect of deeper moisture storages and groundwater–soil moisture–vegetation interactions as a key to understanding TWS variations. We highlight the need for further observations to identify the adequate model structure rather than only model parameters for a reasonable representation and interpretation of vegetation–water interactions.

Description: The backscattered power recorded by a spaceborne scatterometer operating at C-band is sensitive to land surface parameters and is operationally used by some global remote sensing services, e.g., to estimate soil moisture. The estimation of forest variables, in particular above-ground biomass (AGB), from scatterometer data instead was seldom explored. Given the availability of multi-decadal sets of scatterometer observations from space, it is of interest to address the contribution of C-band scatterometer data to the quantification of carbon stocks stored in forests even if the spatial resolution of spaceborne scatterometers is very coarse. In this paper, we investigated the prospects of AGB estimation using backscatter observations by the MetOp Advanced SCATterometer (ASCAT) with a spatial resolution of 0.25°. For this study, ASCAT observations acquired in 2010 were used to be contemporary with AGB datasets selected to benchmark the performance of the estimation. A Water Cloud Model that integrates two allometric equations derived from spaceborne LiDAR data reproduced the relationship between observations of radar backscatter as a function of AGB. Estimates of AGB from individual observations were then combined with a weighted average to reduce uncertainties. Finally, a correction was introduced to compensate for the offset introduced by sloped terrain and surfaces not covered by woody vegetation on the AGB estimate of a pixel. Uncertainties associated with the scatterometer observations, and the modelling framework were propagated to obtain per-pixel values of the standard deviation of an AGB estimate. The proposed method explains much of the variance in AGB estimates when compared to measurements from inventory data (R2 = 0.72) and generated unbiased estimates globally (bias: −3.3 Mg⋅ha−1). Nonetheless, the discrepancy between estimated and plot-based AGB values tended to increase for decreasing biomass level from 20% to 60% of the reference AGB level. A further assessment related to global stocks indicated that the value estimated from the scatterometer dataset (596 Pg, 95% of which 563 Pg stored in forest land) was in line with two published estimates based on forest inventory data only (571 Pg and 600 Pg, respectively). Despite the coarse spatial resolution, our results indicate that C-band scatterometer observations from space can contribute to the characterization of terrestrial biomass pools. The record of observations starting in the early 1990s may provide an unprecedented way to look at long-term forest dynamics as well as to constrain the strength of carbon-climate cycle feedback simulated by Earth System models.

Description: Climate change exacerbates the occurrence of extreme droughts and heatwaves, increasing the frequency and intensity of large wildfires across the globe. Forecasting wildfire danger and uncovering the drivers behind fire events become central for understanding relevant climate-land surface feedback and aiding wildfire management. In this work, we leverage Deep Learning (DL) to predict the next day's wildfire danger in a fire-prone part of the Eastern Mediterranean and explainable Artificial Intelligence (xAI) to diagnose model attributions. We implement DL models that capture the temporal and spatio-temporal context, generalize well for extreme wildfires, and demonstrate improved performance over the traditional Fire Weather Index. Leveraging xAI, we identify the substantial contribution of wetness-related variables and unveil the temporal focus of the models. The variability of the contribution of the input variables across wildfire events hints into different wildfire mechanisms. The presented methodology paves the way to more robust, accurate, and trustworthy data-driven anticipation of wildfires.

Description: Vegetation carbon turnover time (τ) is a central ecosystem property to quantify the global vegetation carbon dynamics. However, our understanding of vegetation dynamics is hampered by the lack of long-term observations of the changes in vegetation biomass. Here we challenge the steady state assumption of τ by using annual changes in vegetation biomass that derived from remote-sensing observations. We evaluate the changes in magnitude, spatial patterns, and uncertainties in vegetation carbon turnover times from 1992 to 2016. We found the robustness in the steady state assumption for forest ecosystems at large spatial scales, contrasting with local larger differences at the grid cell level between τ under steady state and τ under non-steady state conditions. The observation that terrestrial ecosystems are not in a steady state locally is deemed crucial when studying vegetation dynamics and the potential response of biomass to disturbance and climatic changes.